Accurately recommending items of interests is essential for users to improve their experience. To acquire better performance, a recommender system can utilize multiple sources of data and the social knowledge graph. This can lead to efficient use of information to improve the recommender system. By exploring the data and extract crucial features to feed to designed models, the recommending engine can increase its performance dramatically. Furthermore, a social knowledge graph contains the description and relation of users, which can act as a knowledge base for inference. By fusion of those techniques, the resulting recommender system can be optimized.
AppDirect Canada Inc
Professional, scientific and technical services
University of Toronto
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