TCNSVD: A Temporal and Community-Aware Recommender Approach

Recommender systems support users in €nding relevant items in overloaded information spaces. Researchers and practitioners have proposed many di‚erent collaborative €ltering algorithms for different information scenarios, domains and contexts. One of the laŠer, are time-aware recommender methods that consider temporal dynamics in the users’ interests in certain items, topics, etc. While there is extensive research on time-aware recommender systems, surprisingly, researchers have paid liŠle aŠention to model temporal community structure dynamics (community dri‰). In consequence, recommender systems seldom exploit explicit and implicit community structures that are present in online systems, where one can see what others have been watching, sharing and or tagging. In this paper, we propose a recommender method that not only considers temporal interest dynamics in online communities, but also exploits the social structure by the means of community detection algorithms. We conducted o„ine experiments on the Netƒix dataset and the latest MovieLens dataset with tag information. Our method outperformed the current state-of-the-art in rating and item-ranking prediction. Œis work contributes to the connection of two separate recommender research directions, in which exploits community structure and temporal e‚ects together in recommender systems.