Memory bank augmented long-tail sequential recommendation

• Main Authors: Hu, Yidan, Liu, Yong, Miao, Chunyan, Miao, Yuan
• Other Authors: School of Computer Science and Engineering
• Format: Conference or Workshop Item
• Language: English
• Published: 2023
• Subjects: Engineering::Computer science and engineering; Sequential Recommendation; Memory Bank
• Online Access: https://hdl.handle.net/10356/164144
• Institution: Nanyang Technological University

The goal of sequential recommendation is to predict the next item that a user would like to interact with, by capturing her dynamic historical behaviors. However, most existing sequential recommendation methods do not focus on solving the long-tail item recommendation problem that is caused by the imbalanced distribution of item data. To solve this problem, we propose a novel sequential recommendation framework, named MASR (ie <u>M</u>emory Bank <u>A</u>ugmented Long-tail <u>S</u>equential <u>R</u>ecommendation). MASR is an "Open-book"model that combines novel types of memory banks and a retriever-copy network to alleviate the long-tail problem. During inference, the designed retriever-copy network retrieves related sequences from the training samples and copies the useful information as a cue to improve the recommendation performance on tail items. Two designed memory banks provide reference samples to the retriever-copy network by memorizing the historical samples appearing in the training phase. Extensive experiments have been performed on five real-world datasets to demonstrate the effectiveness of the proposed MASR model. The experimental results indicate that MASR consistently outperforms baseline methods in terms of recommendation performance on tail items.