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The existing collaborative recommendation models that use multi-modalinformation emphasize the representation of users' preferences but easilyignore the representation of users' dislikes. Nevertheless, modelling users'dislikes facilitates comprehensively characterizing user profiles. Thus, therepresentation of users' dislikes should be integrated into the user modellingwhen we construct a collaborative recommendation model. In this paper, wepropose a novel Collaborative Recommendation Model based on Multi-modalmulti-view Attention Network (CRMMAN), in which the users are represented fromboth preference and dislike views. Specifically, the users' historicalinteractions are divided into positive and negative interactions, used to modelthe user's preference and dislike views, respectively. Furthermore, thesemantic and structural information extracted from the scene is employed toenrich the item representation. We validate CRMMAN by designing contrastexperiments based on two benchmark MovieLens-1M and Book-Crossing datasets.Movielens-1m has about a million ratings, and Book-Crossing has about 300,000ratings. Compared with the state-of-the-art knowledge-graph-based andmulti-modal recommendation methods, the AUC, NDCG@5 and NDCG@10 are improved by2.08%, 2.20% and 2.26% on average of two datasets. We also conduct controlledexperiments to explore the effects of multi-modal information and multi-viewmechanism. The experimental results show that both of them enhance the model'sperformance.

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