Everyone’s Preference Changes Differently: A Weighted Multi-Interest Model For Retrieval

Hui Shi, Yupeng Gu, Yitong Zhou, Bo Zhao, Sicun Gao, Jishen Zhao
Proceedings of the 40th International Conference on Machine Learning, PMLR 202:31228-31242, 2023.

Abstract

User embeddings (vectorized representations of a user) are essential in recommendation systems. Numerous approaches have been proposed to construct a representation for the user in order to find similar items for retrieval tasks, and they have been proven effective in industrial recommendation systems. Recently people have discovered the power of using multiple embeddings to represent a user, with the hope that each embedding represents the user’s interest in a certain topic. With multi-interest representation, it’s important to model the user’s preference over the different topics and how the preference changes with time. However, existing approaches either fail to estimate the user’s affinity to each interest or unreasonably assume every interest of every user fades at an equal rate with time, thus hurting the performance of candidate retrieval. In this paper, we propose the Multi-Interest Preference (MIP) model, an approach that not only produces multi-interest for users by using the user’s sequential engagement more effectively but also automatically learns a set of weights to represent the preference over each embedding so that the candidates can be retrieved from each interest proportionally. Extensive experiments have been done on various industrial-scale datasets to demonstrate the effectiveness of our approach.

Cite this Paper

BibTeX
@InProceedings{pmlr-v202-shi23b, title = {Everyone’s Preference Changes Differently: A Weighted Multi-Interest Model For Retrieval}, author = {Shi, Hui and Gu, Yupeng and Zhou, Yitong and Zhao, Bo and Gao, Sicun and Zhao, Jishen}, booktitle = {Proceedings of the 40th International Conference on Machine Learning}, pages = {31228--31242}, year = {2023}, editor = {Krause, Andreas and Brunskill, Emma and Cho, Kyunghyun and Engelhardt, Barbara and Sabato, Sivan and Scarlett, Jonathan}, volume = {202}, series = {Proceedings of Machine Learning Research}, month = {23--29 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v202/shi23b/shi23b.pdf}, url = {https://proceedings.mlr.press/v202/shi23b.html}, abstract = {User embeddings (vectorized representations of a user) are essential in recommendation systems. Numerous approaches have been proposed to construct a representation for the user in order to find similar items for retrieval tasks, and they have been proven effective in industrial recommendation systems. Recently people have discovered the power of using multiple embeddings to represent a user, with the hope that each embedding represents the user’s interest in a certain topic. With multi-interest representation, it’s important to model the user’s preference over the different topics and how the preference changes with time. However, existing approaches either fail to estimate the user’s affinity to each interest or unreasonably assume every interest of every user fades at an equal rate with time, thus hurting the performance of candidate retrieval. In this paper, we propose the Multi-Interest Preference (MIP) model, an approach that not only produces multi-interest for users by using the user’s sequential engagement more effectively but also automatically learns a set of weights to represent the preference over each embedding so that the candidates can be retrieved from each interest proportionally. Extensive experiments have been done on various industrial-scale datasets to demonstrate the effectiveness of our approach.} }
Endnote
%0 Conference Paper %T Everyone’s Preference Changes Differently: A Weighted Multi-Interest Model For Retrieval %A Hui Shi %A Yupeng Gu %A Yitong Zhou %A Bo Zhao %A Sicun Gao %A Jishen Zhao %B Proceedings of the 40th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2023 %E Andreas Krause %E Emma Brunskill %E Kyunghyun Cho %E Barbara Engelhardt %E Sivan Sabato %E Jonathan Scarlett %F pmlr-v202-shi23b %I PMLR %P 31228--31242 %U https://proceedings.mlr.press/v202/shi23b.html %V 202 %X User embeddings (vectorized representations of a user) are essential in recommendation systems. Numerous approaches have been proposed to construct a representation for the user in order to find similar items for retrieval tasks, and they have been proven effective in industrial recommendation systems. Recently people have discovered the power of using multiple embeddings to represent a user, with the hope that each embedding represents the user’s interest in a certain topic. With multi-interest representation, it’s important to model the user’s preference over the different topics and how the preference changes with time. However, existing approaches either fail to estimate the user’s affinity to each interest or unreasonably assume every interest of every user fades at an equal rate with time, thus hurting the performance of candidate retrieval. In this paper, we propose the Multi-Interest Preference (MIP) model, an approach that not only produces multi-interest for users by using the user’s sequential engagement more effectively but also automatically learns a set of weights to represent the preference over each embedding so that the candidates can be retrieved from each interest proportionally. Extensive experiments have been done on various industrial-scale datasets to demonstrate the effectiveness of our approach.
APA
Shi, H., Gu, Y., Zhou, Y., Zhao, B., Gao, S. & Zhao, J.. (2023). Everyone’s Preference Changes Differently: A Weighted Multi-Interest Model For Retrieval. Proceedings of the 40th International Conference on Machine Learning, in Proceedings of Machine Learning Research 202:31228-31242 Available from https://proceedings.mlr.press/v202/shi23b.html.

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