Work

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

Year: 2021

Type: article

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.

Source: Discrete Dynamics in Nature and Society

DOI: 10.1155/2021/6693730

Authors: Yupeng Liu, Yanan Zhang, Xiaochen Zhang

Institutions: Harbin University of Science and Technology, Heilongjiang Institute of Technology

Language: English

Topic: Recommender Systems and Techniques

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,570

OpenAlex ID: W3132593375