Collaborative Recommendation Model Based on Multi-modal Multi-view Attention Network: Movie and literature cases

TL;DR

This paper introduces CRMMAN, a collaborative recommendation model that integrates user preferences and dislikes from multi-modal and multi-view data, improving recommendation accuracy on movie and literature datasets.

Contribution

The paper proposes a novel multi-modal multi-view attention network that models both user preferences and dislikes, enriching item representations with semantic and structural scene information.

Findings

CRMMAN outperforms state-of-the-art methods in AUC, NDCG@5, and NDCG@10.

Multi-modal information enhances recommendation performance.

Modeling user dislikes improves the comprehensiveness of user profiles.

Abstract

The existing collaborative recommendation models that use multi-modal information emphasize the representation of users' preferences but easily ignore the representation of users' dislikes. Nevertheless, modelling users' dislikes facilitates comprehensively characterizing user profiles. Thus, the representation of users' dislikes should be integrated into the user modelling when we construct a collaborative recommendation model. In this paper, we propose a novel Collaborative Recommendation Model based on Multi-modal multi-view Attention Network (CRMMAN), in which the users are represented from both preference and dislike views. Specifically, the users' historical interactions are divided into positive and negative interactions, used to model the user's preference and dislike views, respectively. Furthermore, the semantic and structural information extracted from the scene is employed to enrich the item representation. We validate CRMMAN by designing contrast experiments based on two benchmark MovieLens-1M and Book-Crossing datasets. Movielens-1m has about a million ratings, and Book-Crossing has about 300,000 ratings. Compared with the state-of-the-art knowledge-graph-based and multi-modal recommendation methods, the AUC, NDCG@5 and NDCG@10 are improved by 2.08%, 2.20% and 2.26% on average of two datasets. We also conduct controlled experiments to explore the effects of multi-modal information and multi-view mechanism. The experimental results show that both of them enhance the model's performance.