A recommendation system is often used to recommend items that may be of interest to users. One of the main challenges is that the scarcity of actual interaction data between users and items restricts the performance of recommendation systems. To solve this problem, multi-modal technologies have been used for expanding available information. However, the existing multi-modal recommendation algorithms all extract the feature of single modality and simply splice the features of different modalities to predict the recommendation results. This fusion method can not completely mine the relevance of multi-modal features and lose the relationship between different modalities, which affects the prediction results. In this paper, we propose a Cross-Modal-Based Fusion Recommendation Algorithm (CMBF) that can capture both the single-modal features and the cross-modal features. Our algorithm uses a novel cross-modal fusion method to fuse the multi-modal features completely and learn the cross information between different modalities. We evaluate our algorithm on two datasets, MovieLens and Amazon. Experiments show that our method has achieved the best performance compared to other recommendation algorithms. We also design ablation study to prove that our cross-modal fusion method improves the prediction results.

In recent years, great efforts have been made to develop the conversational recommender system (CRS). However, existing works always ignore the incorporation of the recommended items and the generated replies. This causes the performance of the recommendation degrading in the conversations. To solve this problem, we propose a novel framework called UCRI to fuse the recommender module and the dialogue module seamlessly. UCRI captures the semantic similarity between the recommended items and the candidate words to realize the item-guided conditional generation. Besides, we further design the weight control mechanism and the recommender gating mechanism to make accurate recommendations in the conversations. Our approach can explicitly generate the recommended items in the replies and encourage the model to generate the related context for the items. Extensive experiments on the benchmark dataset REDIAL show that our model achieves the best performance on both item recommendation and reply generation tasks.