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Time Weight Content-Based Extensions of Temporal Graphs for Personalized Recommendation

Abstract : Recommender systems are an answer to information overload on the web. They filter and present to customer, a small subset of items that he is most likely to be interested in. Since user's interests may change over time, accurately capturing these dynamics is important, though challenging. The Session-based Temporal Graph (STG) has been proposed by Xiang et al. to provide temporal recommendations by combining long-and short-term preferences. Later, Yu et al. have introduced an extension called Topic-STG, which takes into account topics extracted from tweets' textual information. Recently, we pushed the idea further and proposed Content-based STG. However, in all these frameworks, the importance of links does not depend on their arrival time, which is a strong limitation: at any given time, purchases made last week should have a greater influence than purchases made a year ago. In this paper, we address this problem by proposing Time Weight Content-based STG, in which we assign a time-decreasing weight to edges. Using Time-Averaged Hit Ratio, we show that this approach outperforms all previous ones in real-world situations.
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Submitted on : Monday, April 3, 2017 - 10:37:22 AM
Last modification on : Tuesday, December 7, 2021 - 5:50:03 PM
Long-term archiving on: : Tuesday, July 4, 2017 - 12:42:09 PM


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Armel Jacques Nzekon Nzeko'O, Maurice Tchuente, Matthieu Latapy. Time Weight Content-Based Extensions of Temporal Graphs for Personalized Recommendation. WEBIST 2017 - 13th International Conference on Web Information Systems and Technologies, INSTICC, Apr 2017, Porto, Portugal. ⟨10.5220/0006288202680275⟩. ⟨hal-01500348⟩



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