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Bootstrapping Conditional Retrieval for User-to-Item Recommendations

Published: 08 October 2024 Publication History

Abstract

User-to-item retrieval has been an active research area in recommendation system, and two tower models are widely adopted due to model simplicity and serving efficiency. In this work, we focus on a variant called conditional retrieval, where we expect retrieved items to be relevant to a condition (e.g. topic). We propose a method that uses the same training data as standard two tower models but incorporates item-side information as conditions in query. This allows us to bootstrap new conditional retrieval use cases and encourages feature interactions between user and condition. Experiments show that our method can retrieve highly relevant items and outperforms standard two tower models with filters on engagement metrics. The proposed model is deployed to power a topic-based notification feed at Pinterest and led to +0.26% weekly active users.

References

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Yu A Malkov and Dmitry A Yashunin. 2018. Efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs. IEEE transactions on pattern analysis and machine intelligence 42, 4 (2018), 824–836.
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Hemanth Vemuri, Sheshansh Agrawal, Shivam Mittal, Deepak Saini, Akshay Soni, Abhinav V Sambasivan, Wenhao Lu, Yajun Wang, Mehul Parsana, Purushottam Kar, 2023. Personalized Retrieval over Millions of Items. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1014–1022.
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  1. Bootstrapping Conditional Retrieval for User-to-Item Recommendations

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    cover image ACM Conferences
    RecSys '24: Proceedings of the 18th ACM Conference on Recommender Systems
    October 2024
    1438 pages
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    Published: 08 October 2024

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    Author Tags

    1. Conditional Retrieval
    2. Learned Retrieval
    3. Topic Feed Generation
    4. Two Tower Model

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