A novel recommendation system based on semantics and context awareness

The existing content-based recommendation methods have two major limitations. First, due to the defects of the items and the user model matching algorithms, the recommendation results are very narrow. Second, scant attention is paid to the scenario, making the recommendation system not context-aware. It is essential to improve user satisfaction through high-quality recommendation. In this paper, two state-of-the-art methods are analyzed and extended to enhance recommendation performance. The first method is the context-aware recommender, which integrates context information into the recommendation process. The second method is the semantic analysis-based recommender, which incorporates domain semantics. Despite their compatibility, the challenge is to combine them in a way that will fully exploit their potential. An improved content-based model is proposed in this paper incorporating both semantics and context. Context-aware recommendation is performed to improve sensitivity to the context. Semantic relevance-based instance similarity is computed to address the problem of narrowness. The proposed recommendation system is evaluated using metrics (for instance, recall metric) and paralleled with the current methods grounded on the content. Results demonstrate the superiority of the proposed system in terms of accuracy.