An Agent-based Movie Recommender System Combining the Results Computed based on Heterogeneous Semantic Datasets

In a growing number of domains, recommender systems support users’ decision making processes in finding entities (such as book, movies, or newspaper articles) matching their individual preferences. Individual preferences vary extensively thus hampering the creation of good recommender systems. Additionally, the relevance of items to users depends on a plethora of criteria. Such relevance criteria may consider users’ current contexts, their ratings (“likes”/“dislikes”) and content describing the entities (e. g., a movie’s leading actors). In this paper, we introduce an agent-based recommender approach that integrates heterogeneous recommender agents and combines their results based on a personalized weighting scheme. The framework is open for new agents, allowing us to integrate new algorithms and data sources. The use of a personalized weighting scheme for combining the results from heterogeneous agents as well as the integration of personalized recommender agents allows the system to adapt the results to individual users’ preferences. For improving the trust in unknown items, the system provides detailed explanations based on the semantic relations. We present a recommender system for the movie domain (based on the framework) and discuss the evaluation results.