Incorporating Spatial, Temporal, and Social Context in Recommendations for Location-Based Social Networks
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...For example, DAO can accomplish automatic matching of roles/tasks, through digitizing information and behavior data of individuals/organizations (such as users’ click, search, and browse data), and match individuals’ positions and roles in a DAO according to their contributions and abilities, then automatically complete task identification, recommendation [36], and matching....
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...incorporated in the recommendation process [8,16,3]. The analysis of users’ behavior indicates that geographical information has a higher impact on users’ preference than other contextual information [18,22,6]. As a consequence, several POI recommendation methods have been proposed considering the geographical context [8,11,12,21]. However, the past work has considered geographical context only from the us...
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...tions at each time slot. – PFMPD: A method using the Power-law Distribution [19] that model people tend to visit nearby POIs. We integrate this model with the Probabilistic Factor Model (PFM). – LMFT [18]: A method that considers a user’s recent activities as more important than their past activities and multiple visits to a location, as indicates of a stronger preference for that location. – iGLSR7 [...
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...Given a set of users U and a set of items I , a recommender system attempts to find the subset of items that are the most relevant to each user (U j ) [20]....
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...To give credit to a larger set of coratings, we scale (1) by the Jaccard similarity index [23]...
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