Recommendation based on attributes and social relationships

Attributes are important auxiliary information for representing user and item features, especially in data-sparse scenario, and can serve as their main source. However, different attributes have different importance for representing users or items, and users have different preferences for different attributes of an item, which is often ignored in current research. With the rapid development of social network platforms, the use of users' social relationships to improve and enhance the performance of recommender systems has also received considerable attention. However, most studies have treated the influence of different social relationships in the same way. Different social friends have different influences on users' preferences. To solve these problems, this study proposes a recommendation model based on attributes and social relationships. In the model, we use attributes to learn the feature representations of users and items, while we use social relationships to augment the feature representations of users. We developed two attention mechanisms: attribute-level and social-level. The former was used to distinguish the relative importance of different attributes, and the latter was used to model the influence of different social relationships. The novel recommendation model in this study innovatively combines attribute information and social relationships to improve feature representation learning of users and items, and to alleviate the problem of data sparsity. Extensive experiments on public datasets have confirmed the effectiveness of the proposed model, and comparisons with different baselines showed significant improvement in recommendation accuracy.