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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WEBIST 2017 - 13th International Conference on Web Information Systems and Technologies, Apr 2017, Porto, Portugal. WEBIST 2017
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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, Apr 2017, Porto, Portugal. WEBIST 2017. 〈hal-01500348〉

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