kNN-Embed: Locally Smoothed Embedding Mixtures For Multi-interest Candidate Retrieval

• URL: http://arxiv.org/abs/2205.06205v3
• Date: Sat, 5 Aug 2023 19:10:12 GMT
• Title: kNN-Embed: Locally Smoothed Embedding Mixtures For Multi-interest Candidate Retrieval
• Authors: Ahmed El-Kishky, Thomas Markovich, Kenny Leung, Frank Portman, Aria Haghighi, Ying Xiao
• Abstract summary: kNN-Embed represents each user as a smoothed mixture over learned item clusters that represent distinct "interests" of the user. We experimentally compare kNN-Embed to standard ANN candidate retrieval, and show significant improvements in overall recall and improved diversity across three datasets.
• Score: 7.681386867564213
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Candidate retrieval is the first stage in recommendation systems, where a light-weight system is used to retrieve potentially relevant items for an input user. These candidate items are then ranked and pruned in later stages of recommender systems using a more complex ranking model. As the top of the recommendation funnel, it is important to retrieve a high-recall candidate set to feed into downstream ranking models. A common approach is to leverage approximate nearest neighbor (ANN) search from a single dense query embedding; however, this approach this can yield a low-diversity result set with many near duplicates. As users often have multiple interests, candidate retrieval should ideally return a diverse set of candidates reflective of the user's multiple interests. To this end, we introduce kNN-Embed, a general approach to improving diversity in dense ANN-based retrieval. kNN-Embed represents each user as a smoothed mixture over learned item clusters that represent distinct "interests" of the user. By querying each of a user's mixture component in proportion to their mixture weights, we retrieve a high-diversity set of candidates reflecting elements from each of a user's interests. We experimentally compare kNN-Embed to standard ANN candidate retrieval, and show significant improvements in overall recall and improved diversity across three datasets. Accompanying this work, we open source a large Twitter follow-graph dataset (https://huggingface.co/datasets/Twitter/TwitterFollowGraph), to spur further research in graph-mining and representation learning for recommender systems.

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