An End-to-End Review-Based Aspect-Level Neural Model for Sequential Recommendation

Yupeng Liu,Xiaochen Zhang,Yanan Zhang,Stefania Tomasiello

doi:10.1155/2021/6693730

Abstract

Users’ reviews of items contain a lot of semantic information about their preferences for items. This paper models users’ long-term and short-term preferences through aspect-level reviews using a sequential neural recommendation model. Specifically, the model is devised to encode users and items with the aspect-aware representations extracted globally and locally from the user-related and item-related reviews. Given a sequence of neighbor users of a user, we design a hierarchical attention model to capture union-level preferences on sequential patterns, a pointer model to capture individual-level preferences, and a traditional attention model to balance the effects of both union-level and individual-level preferences. Finally, the long-term and short-term preferences are combined into a representation of the user and item profiles. Extensive experiments demonstrate that the model substantially outperforms many other state-of-the-art baselines substantially.

Highlights

In the era of information explosion, information overload is an important problem faced by users
To better express users’ short-term preferences, we further explore the influence of neighbor users on purchase action at an individual level, that is, identify several users associated with the item
On most datasets, aspect-based systems (ANR and CARP) are better than review-based systems (DeepCoNN, D-Attn, TransNet, TARMF, and MPCN), where the best performance is achieved by using an attention mechanism (D-Attn, TARMF, and MPCN). e review-based system is better than the shallow semantic model (RBLT and CMLE)

Summary

Introduction

In the era of information explosion, information overload is an important problem faced by users. Recommendation system came into being to solve this problem. Recommendation models include review-based model [1], attention-based model [2,3,4], aspect-based model [5], etc [6,7,8]. In the academic research area, a lot of work is focused on modelling long-term preferences. There is no appropriate model for short-term preferences from nearneighbor users. Near-neighbor users often influence the item purchase decisions made by a user. A user would be highly interested in buying Apple mobile phones if his/her near-neighbor users buy them. Sequential recommendations have drawn an increasing attention from both academic and industrial circles. E task is to identify an item purchased by a user by considering his or her temporal preference as a sequence Sequential recommendations have drawn an increasing attention from both academic and industrial circles. e task is to identify an item purchased by a user by considering his or her temporal preference as a sequence

Methods

Results

Conclusion