A Tag-aware Recommendation Algorithm Based on Deep Learning and Multi-objective Optimization

Social tagging information to describe characteristics. Recent systems introduce tagging user preferences and item work shows that the recommendation accuracy can be remarkably promoted when tag information is handled properly. However, other performance indicators of recommendations, such as diversity and novelty, are also of great importance in practice. Thus, we propose a two-stage tag-aware multi-objective framework for providing accurate and diversity recommendations. Specifically, we formulate a tag-based recommendation algorithm via deep learning to generate accurate items and abstract effective tag-based potential features for users and items. According to these features, two conflicting objectives are designed to estimate the recommendation accuracy and diversity, respectively. By optimizing these two objectives simultaneously, the designed multi-objective recommendation model can pro-vide a set of recommendation lists for each user. Comparative experiments verify that the proposed model is promising to generate improved recommendations in terms of accuracy and diversity.