Using Individual Interactions to Infer Group Interests and to Recommend Content for Groups in Public Spaces
This work explores user’s individual interactions combined with contextual information rules to support socially-aware content selection to best fit users’ interests in public spaces. The system uses information from individual users to infer about the general interests of the place visitors and thus allowing the system to present the content that is representative of the interests of people who attend the same space. Two experiments were performed to evaluate the socially-aware proposed system and to analyze the users’ opinions in different scenarios. Results from both experiments indicate that the proposed approach can be used to deliver content for individuals and for groups in public spaces and the users recognize its advantages over traditional information systems usually used in public spaces.
Keywordsadaptive systems context-awareness public displays public recommender systems socially-aware computing ubiquitous computing
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