Mining the Social Semantic Web for making cross-domain recommendations
نویسنده
چکیده
The vast majority of current recommender systems focus on a single domain. Netflix makes personalized recommendations of movies and TV series, and Last.fm suggests music compositions and artists. E-commerce sites like Amazon, however, may take benefit from exploiting the user’s preferences on diverse types of items to provide recommendations in different but somehow related domains. Recommendation across domains could mitigate the cold-start problem when little information about the user’s preferences is available in a target domain, and are potentially more diverse and serendipitous than single-domain recommendations.
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