A Recommendation Engine based on Social Metrics
نویسندگان
چکیده
Recommender systems have changed the way people find products, points of interest, services or even new friends. The technology behind recommender systems has evolved to provide user preferences and social influences. In this paper, we present a first approach to develop a recommendation engine based on social metrics applied to graphs that represent object’s characteristics, user profiles and influences obtained from social connections. It exploits graph centrality measures to elaborate personalized recommendations from the semantic knowledge represented in the graph. The graph model and selected graph algorithms for calculating graph centralities that are the core of the recommender system are presented. Semantic concepts such as semantic predominance and similarity measures are adapted to the graph model. Implementation challenges faced to solve performance issues are also discussed.
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