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"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"HNSW ANN example and strategy"
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p
|
You don’t have to check all the possible connections when you travel.
|
[
"articles",
"articles/new-recommendation-api"
] | 1,101 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"HNSW ANN example and strategy"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(11)
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p
|
You select an intercontinental flight, then a local one,
|
[
"articles",
"articles/new-recommendation-api"
] | 1,102 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"HNSW ANN example and strategy"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(11)
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p
|
and finally a bus or a taxi.
|
[
"articles",
"articles/new-recommendation-api"
] | 1,103 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"HNSW ANN example and strategy"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(11)
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p
|
All the decisions are made based on the distance between the points.
|
[
"articles",
"articles/new-recommendation-api"
] | 1,104 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"HNSW ANN example and strategy"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(12)
|
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p
|
The search process in HNSW is also based on similarly traversing the graph.
|
[
"articles",
"articles/new-recommendation-api"
] | 1,105 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"HNSW ANN example and strategy"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(12)
|
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p
|
Start from the entry point in the top layer, find
|
[
"articles",
"articles/new-recommendation-api"
] | 1,106 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"HNSW ANN example and strategy"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(12)
|
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0.07010899484157562,
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] |
p
|
its closest point and then use that point as the entry point into the next densest layer.
|
[
"articles",
"articles/new-recommendation-api"
] | 1,107 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"HNSW ANN example and strategy"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(12)
|
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] |
p
|
This process repeats until we reach
|
[
"articles",
"articles/new-recommendation-api"
] | 1,108 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"HNSW ANN example and strategy"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(12)
|
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p
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the bottom layer.
|
[
"articles",
"articles/new-recommendation-api"
] | 1,109 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"HNSW ANN example and strategy"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(12)
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p
|
Visited points and distances to the original query vector are kept in memory.
|
[
"articles",
"articles/new-recommendation-api"
] | 1,110 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"HNSW ANN example and strategy"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(12)
|
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p
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If none of the neighbours of
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[
"articles",
"articles/new-recommendation-api"
] | 1,111 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"HNSW ANN example and strategy"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(12)
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p
|
the current point is better than the best match, we can stop the traversal, as this is a local minimum.
|
[
"articles",
"articles/new-recommendation-api"
] | 1,112 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"HNSW ANN example and strategy"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(12)
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p
|
We start at the biggest
|
[
"articles",
"articles/new-recommendation-api"
] | 1,113 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"HNSW ANN example and strategy"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(12)
|
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p
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scale, and then gradually zoom in.
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[
"articles",
"articles/new-recommendation-api"
] | 1,114 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"HNSW ANN example and strategy"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(13)
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p
|
In this oversimplified example, we assumed that the distance between the points is the only factor that matters.
|
[
"articles",
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[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
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] |
/articles/new-recommendation-api/
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html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(13)
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p
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In reality, we
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[
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[
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/articles/new-recommendation-api/
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html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(13)
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p
|
might want to consider other criteria, such as the ticket price, or avoid some specific locations due to certain restrictions.
|
[
"articles",
"articles/new-recommendation-api"
] | 1,117 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"HNSW ANN example and strategy"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(13)
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p
|
That means, there are various strategies for choosing the best match, which is also true in the case of vector recommendations.
|
[
"articles",
"articles/new-recommendation-api"
] | 1,118 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"HNSW ANN example and strategy"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(13)
|
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p
|
We can use different approaches to determine the path of traversing the HNSW graph by changing how we calculate the score of a
|
[
"articles",
"articles/new-recommendation-api"
] | 1,119 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"HNSW ANN example and strategy"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(13)
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p
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candidate point during traversal.
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[
"articles",
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[
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/articles/new-recommendation-api/
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html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(13)
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p
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The default behaviour is based on pure distance, but Qdrant 1.6 exposes two strategies for the
|
[
"articles",
"articles/new-recommendation-api"
] | 1,121 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"HNSW ANN example and strategy"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(13)
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p
|
recommendation API.
|
[
"articles",
"articles/new-recommendation-api"
] | 1,122 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > h3:nth-of-type(2)
|
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h3
|
Average vector
|
[
"articles",
"articles/new-recommendation-api"
] | 1,123 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"Average vector"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(14)
|
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p
|
The default strategy, called average_vector is the previous one, based on the average of positive and negative examples.
|
[
"articles",
"articles/new-recommendation-api"
] | 1,124 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"Average vector"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(14)
|
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p
|
It
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[
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[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"Average vector"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(14)
|
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p
|
simplifies the recommendations process and converts it into a single vector search.
|
[
"articles",
"articles/new-recommendation-api"
] | 1,126 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"Average vector"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(14)
|
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p
|
It supports both point IDs and vectors as
|
[
"articles",
"articles/new-recommendation-api"
] | 1,127 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"Average vector"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(14)
|
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p
|
parameters.
|
[
"articles",
"articles/new-recommendation-api"
] | 1,128 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"Average vector"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(14)
|
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p
|
For example, you can get recommendations based on past interactions with existing points combined with query vector
|
[
"articles",
"articles/new-recommendation-api"
] | 1,129 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"Average vector"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(14)
|
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p
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embedding.
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[
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[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"Average vector"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(14)
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p
|
Internally, that mechanism is based on the averages of positive and negative examples and was calculated with the
|
[
"articles",
"articles/new-recommendation-api"
] | 1,131 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"Average vector"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(14)
|
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p
|
following formula:
|
[
"articles",
"articles/new-recommendation-api"
] | 1,132 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"Average vector"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(15)
|
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p
|
$$
|
[
"articles",
"articles/new-recommendation-api"
] | 1,133 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"Average vector"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(15)
|
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p
|
\text{average vector} = \text{avg}(\text{positive vectors}) + \left( \text{avg}(\text{positive vectors}) - \text{avg}(\text{negative vectors}) \right)
|
[
"articles",
"articles/new-recommendation-api"
] | 1,134 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"Average vector"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(15)
|
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p
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$$
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[
"articles",
"articles/new-recommendation-api"
] | 1,135 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"Average vector"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(16)
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p
|
The average_vector converts the problem of recommendations into a single vector search.
|
[
"articles",
"articles/new-recommendation-api"
] | 1,136 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > h3:nth-of-type(3)
|
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h3
|
The new hotness - Best score
|
[
"articles",
"articles/new-recommendation-api"
] | 1,137 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"The new hotness - Best score"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(17)
|
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p
|
The new strategy is called best_score.
|
[
"articles",
"articles/new-recommendation-api"
] | 1,138 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"The new hotness - Best score"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(17)
|
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p
|
It does not rely on averages and is more flexible.
|
[
"articles",
"articles/new-recommendation-api"
] | 1,139 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"The new hotness - Best score"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(17)
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p
|
It allows you to pass just negative
|
[
"articles",
"articles/new-recommendation-api"
] | 1,140 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"The new hotness - Best score"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(17)
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p
|
samples and uses a slightly more sophisticated algorithm under the hood.
|
[
"articles",
"articles/new-recommendation-api"
] | 1,141 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"The new hotness - Best score"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(18)
|
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p
|
The best score is chosen at every step of HNSW graph traversal.
|
[
"articles",
"articles/new-recommendation-api"
] | 1,142 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"The new hotness - Best score"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(18)
|
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p
|
We separately calculate the distance between a traversed point
|
[
"articles",
"articles/new-recommendation-api"
] | 1,143 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"The new hotness - Best score"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(18)
|
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p
|
and every positive and negative example.
|
[
"articles",
"articles/new-recommendation-api"
] | 1,144 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"The new hotness - Best score"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(18)
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p
|
In the case of the best score strategy, there is no single query vector anymore, but a
|
[
"articles",
"articles/new-recommendation-api"
] | 1,145 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"The new hotness - Best score"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(18)
|
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p
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bunch of positive and negative queries.
|
[
"articles",
"articles/new-recommendation-api"
] | 1,146 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"The new hotness - Best score"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(18)
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p
|
As a result, for each sample in the query, we have a set of distances, one for each
|
[
"articles",
"articles/new-recommendation-api"
] | 1,147 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"The new hotness - Best score"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(18)
|
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p
|
sample.
|
[
"articles",
"articles/new-recommendation-api"
] | 1,148 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"The new hotness - Best score"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(18)
|
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p
|
In the next step, we simply take the best scores for positives and negatives, creating two separate values.
|
[
"articles",
"articles/new-recommendation-api"
] | 1,149 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"The new hotness - Best score"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(18)
|
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p
|
Best scores
|
[
"articles",
"articles/new-recommendation-api"
] | 1,150 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"The new hotness - Best score"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(18)
|
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p
|
are just the closest distances of a query to positives and negatives.
|
[
"articles",
"articles/new-recommendation-api"
] | 1,151 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"The new hotness - Best score"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(18)
|
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p
|
The idea is: if a point is closer to any negative than to
|
[
"articles",
"articles/new-recommendation-api"
] | 1,152 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"The new hotness - Best score"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(18)
|
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p
|
any positive example, we do not want it.
|
[
"articles",
"articles/new-recommendation-api"
] | 1,153 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"The new hotness - Best score"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(18)
|
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p
|
We penalize being close to the negatives, so instead of using the similarity value
|
[
"articles",
"articles/new-recommendation-api"
] | 1,154 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"The new hotness - Best score"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(18)
|
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] |
p
|
directly, we check if it’s closer to positives or negatives.
|
[
"articles",
"articles/new-recommendation-api"
] | 1,155 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"The new hotness - Best score"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(18)
|
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] |
p
|
The following formula is used to calculate the score of a traversed
|
[
"articles",
"articles/new-recommendation-api"
] | 1,156 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"The new hotness - Best score"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(18)
|
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p
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potential point:
|
[
"articles",
"articles/new-recommendation-api"
] | 1,157 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"The new hotness - Best score"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(19)
|
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p
|
If the point is closer to the negatives, we penalize it by taking the negative squared value of the best negative score.
|
[
"articles",
"articles/new-recommendation-api"
] | 1,158 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"The new hotness - Best score"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(19)
|
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p
|
For a
|
[
"articles",
"articles/new-recommendation-api"
] | 1,159 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"The new hotness - Best score"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(19)
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p
|
closer negative, the score of the candidate point will always be lower or equal to zero, making the chances of choosing that point
|
[
"articles",
"articles/new-recommendation-api"
] | 1,160 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"The new hotness - Best score"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(19)
|
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p
|
significantly lower.
|
[
"articles",
"articles/new-recommendation-api"
] | 1,161 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"The new hotness - Best score"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(19)
|
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p
|
However, if the best negative score is higher than the best positive score, we still prefer those that are
|
[
"articles",
"articles/new-recommendation-api"
] | 1,162 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"The new hotness - Best score"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(19)
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] |
p
|
further away from the negatives.
|
[
"articles",
"articles/new-recommendation-api"
] | 1,163 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"The new hotness - Best score"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(19)
|
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] |
p
|
That procedure effectively pulls the traversal procedure away from the negative examples.
|
[
"articles",
"articles/new-recommendation-api"
] | 1,164 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"The new hotness - Best score"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(20)
|
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p
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If you want to know more about the internals of HNSW, you can check out the article about the
|
[
"articles",
"articles/new-recommendation-api"
] | 1,165 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"The new hotness - Best score"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(20)
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p
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Filtrable HNSW that covers the topic thoroughly.
|
[
"articles",
"articles/new-recommendation-api"
] | 1,166 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > h2:nth-of-type(2)
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h2
|
Food Discovery demo
|
[
"articles",
"articles/new-recommendation-api"
] | 1,167 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"Food Discovery demo"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(21)
|
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p
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Our Food Discovery demo is an application built on top of the new Recommendation API.
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[
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html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(21)
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p
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It allows you to find a meal based on liked and disliked photos.
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[
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[
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/articles/new-recommendation-api/
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html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(21)
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p
|
There are some updates, enabled by the new Qdrant release:
|
[
"articles",
"articles/new-recommendation-api"
] | 1,170 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"Food Discovery demo"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul:nth-of-type(2) > li:nth-of-type(1)
|
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li
|
Ability to include multiple textual queries in the recommendation request.
|
[
"articles",
"articles/new-recommendation-api"
] | 1,171 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"Food Discovery demo"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul:nth-of-type(2) > li:nth-of-type(1)
|
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li
|
Previously, we only allowed passing a single
|
[
"articles",
"articles/new-recommendation-api"
] | 1,172 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"Food Discovery demo"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul:nth-of-type(2) > li:nth-of-type(1)
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li
|
query to solve the cold start problem.
|
[
"articles",
"articles/new-recommendation-api"
] | 1,173 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"Food Discovery demo"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul:nth-of-type(2) > li:nth-of-type(1)
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li
|
Right now, you can pass multiple queries and mix them with the liked/disliked photos.
|
[
"articles",
"articles/new-recommendation-api"
] | 1,174 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"Food Discovery demo"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul:nth-of-type(2) > li:nth-of-type(1)
|
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li
|
This became possible because of the new flexibility in parameters.
|
[
"articles",
"articles/new-recommendation-api"
] | 1,175 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"Food Discovery demo"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul:nth-of-type(2) > li:nth-of-type(1)
|
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li
|
We can pass both point IDs and embedding vectors in the same
|
[
"articles",
"articles/new-recommendation-api"
] | 1,176 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"Food Discovery demo"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul:nth-of-type(2) > li:nth-of-type(1)
|
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li
|
request, and user queries are obviously not a part of the collection.
|
[
"articles",
"articles/new-recommendation-api"
] | 1,177 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"Food Discovery demo"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul:nth-of-type(2) > li:nth-of-type(2)
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|
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li
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You can now choose between the average_vector and the best_score scoring
|
[
"articles",
"articles/new-recommendation-api"
] | 1,179 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"Food Discovery demo"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ul:nth-of-type(2) > li:nth-of-type(2)
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li
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algorithm.
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[
"articles",
"articles/new-recommendation-api"
] | 1,180 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > h3:nth-of-type(4)
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h3
|
Differences between the strategies
|
[
"articles",
"articles/new-recommendation-api"
] | 1,181 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"Differences between the strategies"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(22)
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p
|
The UI of the Food Discovery demo allows you to switch between the strategies.
|
[
"articles",
"articles/new-recommendation-api"
] | 1,182 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"Differences between the strategies"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(22)
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p
|
The best_vector is the default one, but with just
|
[
"articles",
"articles/new-recommendation-api"
] | 1,183 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"Differences between the strategies"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(22)
|
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p
|
a single switch, you can see how the results differ when using the previous average_vector strategy.
|
[
"articles",
"articles/new-recommendation-api"
] | 1,184 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"Differences between the strategies"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(23)
|
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p
|
If you select just a single positive example, both algorithms work identically.
|
[
"articles",
"articles/new-recommendation-api"
] | 1,185 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > h5:nth-of-type(1)
|
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h5
|
One positive example
|
[
"articles",
"articles/new-recommendation-api"
] | 1,186 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"One positive example"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(25)
|
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p
|
The difference only becomes apparent when you start adding more examples, especially if you choose some negatives.
|
[
"articles",
"articles/new-recommendation-api"
] | 1,187 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > h5:nth-of-type(2)
|
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h5
|
One positive and one negative example
|
[
"articles",
"articles/new-recommendation-api"
] | 1,188 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"One positive and one negative example"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(27)
|
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p
|
The more likes and dislikes we add, the more diverse the results of the best_score strategy will be.
|
[
"articles",
"articles/new-recommendation-api"
] | 1,189 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"One positive and one negative example"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(27)
|
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p
|
In the old strategy, there
|
[
"articles",
"articles/new-recommendation-api"
] | 1,190 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"One positive and one negative example"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(27)
|
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p
|
is just a single vector, so all the examples are similar to it.
|
[
"articles",
"articles/new-recommendation-api"
] | 1,191 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"One positive and one negative example"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(27)
|
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p
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The new one takes into account all the examples separately, making
|
[
"articles",
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[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"One positive and one negative example"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(27)
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p
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the variety richer.
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[
"articles",
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[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant"
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/articles/new-recommendation-api/
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html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > h5:nth-of-type(3)
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h5
|
Multiple positive and negative examples
|
[
"articles",
"articles/new-recommendation-api"
] | 1,194 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"Multiple positive and negative examples"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(29)
|
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p
|
Choosing the right strategy is dataset-dependent, and the embeddings play a significant role here.
|
[
"articles",
"articles/new-recommendation-api"
] | 1,195 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"Multiple positive and negative examples"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(29)
|
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p
|
Thus, it’s always worth trying
|
[
"articles",
"articles/new-recommendation-api"
] | 1,196 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"Multiple positive and negative examples"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(29)
|
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p
|
both of them and comparing the results in a particular case.
|
[
"articles",
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[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > h4:nth-of-type(1)
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h4
|
Handling the negatives only
|
[
"articles",
"articles/new-recommendation-api"
] | 1,198 |
[
"Deliver Better Recommendations with Qdrant’s new API - Qdrant",
"Handling the negatives only"
] |
/articles/new-recommendation-api/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(30)
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] |
p
|
In the case of our Food Discovery demo, passing just the negative images can work as an outlier detection mechanism.
|
[
"articles",
"articles/new-recommendation-api"
] | 1,199 |
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