Group Feature Aggregation for Web Service Recommendations

Increasingly low barriers to Internet applications allow a large number of ordinary users to become developers or users of Web services. However, confronted with massive services and complex application scenarios, users often struggle to filter out satisfactory services, in fact, even professional users find it difficult to describe their requirements specifically and accurately in many cases. In order to aggregate more feature information and mitigate the negative impact of low-quality user requirement description, we propose a novel group feature aggregation service recommendation framework (GFASR). Concretely, we first calculate the semantic similarity between users, and create a group for each user according to the similarity ranking. Furthermore, on the basis of learning neural embeddings of users, candidate services, and groups, we employ a dual-attention mechanism to capture effective feature (such as requirement description, service history invoked information, etc.) and preference information of group members for each user, thereby supplementing or enhancing the user's feature representation. Finally, we aggregate and propagate the information of all embeddings, and a neural and attentional factorization machine model is used to recommend services for users. Comparative experiments on a real dataset demonstrate that our method significantly outperforms the state-of-the-art service recommendation models.