Probabilistic Masked Attention Networks for Explainable Sequential Recommendation

Author:

Chen Huiyuan1,Zhou Kaixiong2,Jiang Zhimeng3,Yeh Chin-Chia Michael1,Li Xiaoting1,Pan Menghai1,Zheng Yan1,Hu Xia2,Yang Hao1

Affiliation:

1. Visa Research

2. Rice University

3. Texas A&M University

Abstract

Transformer-based models are powerful for modeling temporal dynamics of user preference in sequential recommendation. Most of the variants adopt the Softmax transformation in the self-attention layers to generate dense attention probabilities. However, real-world item sequences are often noisy, containing a mixture of true-positive and false-positive interactions. Such dense attentions inevitably assign probability mass to noisy or irrelevant items, leading to sub-optimal performance and poor explainability. Here we propose a Probabilistic Masked Attention Network (PMAN) to identify the sparse pattern of attentions, which is more desirable for pruning noisy items in sequential recommendation. Specifically, we employ a probabilistic mask to achieve sparse attentions under a constrained optimization framework. As such, PMAN allows to select which information is critical to be retained or dropped in a data-driven fashion. Experimental studies on real-world benchmark datasets show that PMAN is able to improve the performance of Transformers significantly.

Publisher

International Joint Conferences on Artificial Intelligence Organization

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Sequential Recommendation with Collaborative Explanation via Mutual Information Maximization;Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval;2024-07-10

2. The Explainability of Transformers: Current Status and Directions;Computers;2024-04-04

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