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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 32

ATRank: An Attention-Based User Behavior Modeling Framework for Recommendation

March 15, 2023

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Published Date: 2018-02-08

Registration: ISSN 2374-3468 (Online) ISSN 2159-5399 (Print)

Copyright: Published by AAAI Press, Palo Alto, California USA Copyright © 2018, Association for the Advancement of Artificial Intelligence All Rights Reserved.

Authors

Chang Zhou

Alibaba Group


Jinze Bai

Peking University


Junshuai Song

Peking University


Xiaofei Liu

Alibaba Group


Zhengchao Zhao

Alibaba Group


Xiusi Chen

Peking University


Jun Gao

Peking University


DOI:

10.1609/aaai.v32i1.11618


Abstract:

A user can be represented as what he/she does along the history. A common way to deal with the user modeling problem is to manually extract all kinds of aggregated features over the heterogeneous behaviors, which may fail to fully represent the data itself due to limited human instinct. Recent works usually use RNN-based methods to give an overall embedding of a behavior sequence, which then could be exploited by the downstream applications. However, this can only preserve very limited information, or aggregated memories of a person. When a downstream application requires to facilitate the modeled user features, it may lose the integrity of the specific highly correlated behavior of the user, and introduce noises derived from unrelated behaviors. This paper proposes an attention based user behavior modeling framework called ATRank, which we mainly use for recommendation tasks. Heterogeneous user behaviors are considered in our model that we project all types of behaviors into multiple latent semantic spaces, where influence can be made among the behaviors via self-attention. Downstream applications then can use the user behavior vectors via vanilla attention. Experiments show that ATRank can achieve better performance and faster training process. We further explore ATRank to use one unified model to predict different types of user behaviors at the same time, showing a comparable performance with the highly optimized individual models.

Topics: AAAI

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HOW TO CITE:

Chang Zhou||Jinze Bai||Junshuai Song||Xiaofei Liu||Zhengchao Zhao||Xiusi Chen||Jun Gao ATRank: An Attention-Based User Behavior Modeling Framework for Recommendation Proceedings of the AAAI Conference on Artificial Intelligence, 32 (2018) .

Chang Zhou||Jinze Bai||Junshuai Song||Xiaofei Liu||Zhengchao Zhao||Xiusi Chen||Jun Gao ATRank: An Attention-Based User Behavior Modeling Framework for Recommendation AAAI 2018, .

Chang Zhou||Jinze Bai||Junshuai Song||Xiaofei Liu||Zhengchao Zhao||Xiusi Chen||Jun Gao (2018). ATRank: An Attention-Based User Behavior Modeling Framework for Recommendation. Proceedings of the AAAI Conference on Artificial Intelligence, 32, .

Chang Zhou||Jinze Bai||Junshuai Song||Xiaofei Liu||Zhengchao Zhao||Xiusi Chen||Jun Gao. ATRank: An Attention-Based User Behavior Modeling Framework for Recommendation. Proceedings of the AAAI Conference on Artificial Intelligence, 32 2018 p..

Chang Zhou||Jinze Bai||Junshuai Song||Xiaofei Liu||Zhengchao Zhao||Xiusi Chen||Jun Gao. 2018. ATRank: An Attention-Based User Behavior Modeling Framework for Recommendation. "Proceedings of the AAAI Conference on Artificial Intelligence, 32". .

Chang Zhou||Jinze Bai||Junshuai Song||Xiaofei Liu||Zhengchao Zhao||Xiusi Chen||Jun Gao. (2018) "ATRank: An Attention-Based User Behavior Modeling Framework for Recommendation", Proceedings of the AAAI Conference on Artificial Intelligence, 32, p.

Chang Zhou||Jinze Bai||Junshuai Song||Xiaofei Liu||Zhengchao Zhao||Xiusi Chen||Jun Gao, "ATRank: An Attention-Based User Behavior Modeling Framework for Recommendation", AAAI, p., 2018.

Chang Zhou||Jinze Bai||Junshuai Song||Xiaofei Liu||Zhengchao Zhao||Xiusi Chen||Jun Gao. "ATRank: An Attention-Based User Behavior Modeling Framework for Recommendation". Proceedings of the AAAI Conference on Artificial Intelligence, 32, 2018, p..

Chang Zhou||Jinze Bai||Junshuai Song||Xiaofei Liu||Zhengchao Zhao||Xiusi Chen||Jun Gao. "ATRank: An Attention-Based User Behavior Modeling Framework for Recommendation". Proceedings of the AAAI Conference on Artificial Intelligence, 32, (2018): .

Chang Zhou||Jinze Bai||Junshuai Song||Xiaofei Liu||Zhengchao Zhao||Xiusi Chen||Jun Gao. ATRank: An Attention-Based User Behavior Modeling Framework for Recommendation. AAAI[Internet]. 2018[cited 2023]; .


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
Artificial Intelligence 1900 Embarcadero Road, Suite
101, Palo Alto, California 94303 All Rights Reserved

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