Context-aware seq2seq translation model for sequential recommendation

Context information, such as product category, plays a vital role in sequential recommendations. Recently, there has been a growing interest in context-aware sequential recommender systems. However, in previous studies, contexts have often been treated as auxiliary information without the considerat...

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Bibliographic Details
Published in:Information sciences Vol. 581; pp. 60 - 72
Main Authors: Sun, Ke, Qian, Tieyun, Chen, Xu, Zhong, Ming
Format: Journal Article
Language:English
Published: Elsevier Inc 01.12.2021
Subjects:
Context information
Seq2seq translation model
Sequential recommendation
Sequential recommendation
Seq2seq translation model
Context information
ISSN:0020-0255, 1872-6291
Online Access:Get full text
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Summary:Context information, such as product category, plays a vital role in sequential recommendations. Recently, there has been a growing interest in context-aware sequential recommender systems. However, in previous studies, contexts have often been treated as auxiliary information without the consideration of the inter-sequence dependency between the item sequence and the context sequence. Such a dependency provides valuable details for predicting a user’s future behavior. For example, a user may buy electronic accessories after buying an electronic product. In this paper, we propose a context-aware seq2seq translation model to capture the inter-sequence dependency for sequential recommendations. The key component in our model is a tripled seq2seq translation architecture with an injected variational autoencoder (VAE). The tripled architecture, consisting of forward and backward translation, naturally encodes bi-directional inter-sequence dependency. Moreover, the injected VAE enables the translation process to redress the semantic imbalance between context and item. We conduct extensive experiments on four real-world datasets. The results show the superior performance of our model over the state-of-the-art baselines. The code and datasets are available athttps://github.com/NLPWM-WHU/CAST.
ISSN:0020-0255
1872-6291
DOI:10.1016/j.ins.2021.09.001