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In event-based social networks, an event recommender helps users to discover events that align with their preferences from a large number of upcoming events. In this paper, we propose a personalized event recommender called SoCaST based on the geographical, categorical, social and temporal influences of events on users to provide event recommendations. SoCaST uses an adaptive Kernel Density Estimation...
The existing social recommendation models mostly utilize various explicit user-generated information. Although there exist a few studies adopting the implicit relationship between users for social recommendation, however, these studies do not consider the deeper social relationship, nor simultaneously take into account two or more deeper relationships between users from different angles. To this end,...
In 2007, Netflex provided a large training dataset describing user ratings of movies for KDD Cup contest. Many competitors proposed various kinds of data mining model trying to achieve the best prediction performance. The first place winner among the competitors got the best root mean square error (RMSE) of 0.256. Most of the models applied statistical machines learning techniques with collaborative...
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