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TCNSVD: A Temporal and Community-Aware Recommender Approach

Year 2017
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Recommender systems support users in finding relevant items in overloaded information spaces. Researchers and practitioners have proposed many different collaborative filtering algorithms for different information scenarios, domains and contexts. One of the latter, 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 little attention to model temporal community structure dynamics (community drift). 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 offine experiments on the Netflix dataset and the latest MovieLens dataset with tag information. Our method outperformed the current state-of-the-art in rating and item-ranking prediction. this work contributes to the connection of two separate recommender research directions, in which exploits community structure and temporal effects together in recommender systems.

Details

Mohsen Shahriari, Martin Barth, Ralf Klamma, Christoph Trattner: TCNSVD: A Temporal and Community-Aware Recommender Approach. RecTemp@RecSys 2017: 21-27

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Presented at

RecTemp 2017, 2017 , Como , IT.

Published in

Proceedings of the 1st Workshop on Temporal Reasoning in Recommender Systems co-located with 11th International Conference on Recommender Systems (RecSys 2017) , by Maria Bielikova, Veronika Bogina, Tsvi Kuflik, Toy Sasson , p. 21-27 ; CEUR .

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