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In this research, we propose using time context to improve predictive accuracy and quality of collaborative filtering for music recommendation. We use time contextual information called micro-profiling. Thus, each user has multiple micro profiles, in particular, six-time slots instead of a single profile. The recommendation is performed depended on these micro-profiling. Our method takes into account...
Traditional recommender systems use collaborative filtering or content-based methods to recommend new items for users. New users and items are continuously updated to the system bringing changes in user's preferences, as well as the additional context in form of temporal information. The continuous system updates change not just individual user's preferences, but also group user's preferences affecting...
Context prediction approaches forecast future contexts based on known context patterns to adapt e.g., services in advance. In the case of the user's context history not providing suitable context information for the observed context pattern, to the best of our knowledge context prediction algorithms will fail to forecast the appropriate future context. To overcome the gap of missing context information...
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