  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.
