Nonlinear Matrix Factorization With Cognitive Opinion Formation for Social Recommendation

Recommender systems continuously strive to recommend items that the users potentially like accurately. Most recommender systems assume that latent user preferences and item features are linearly combined. However, the existing linear interaction patterns do not realistically reflect users' decision-making processes. The formation of users' opinions on items and the evolutionary preference interaction process among users needs to be explored. In our work, we bridge social psychology and recommender systems to develop a social recommendation model, nonlinearly utilizing latent user preferences and item features to simulate the intrinsic formation of users' decision-making. We extend the cognitive opinion formation mechanism by improving the two-stage process and seamlessly combine it and matrix factorization, simulating the nonlinear interactions between users and items. We incorporate the implicit user influence and explicit social dynamics with bounded confidence effect into the nonlinear cognitive recommendation framework to characterize the evolutionary preference interactions among users. We conduct comprehensive experiments on real-world datasets to compare the proposed method with the state-of-the-art models. The results indicate that our method makes notable improvements in rating prediction for all users and cold-start users. In addition, the nonlinear cognitive opinion formation has a significant effect on improving performance, conferring higher interpretability to the recommendation.