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• W2807146906 abstract "Image based social networks are among the most popular social networkingservices in recent years. With tremendous images uploaded everyday,understanding users' preferences to the user-generated images and recommendingthem to users have become an urgent need. However, this is a challenging task.On one hand, we have to overcome the extremely data sparsity issue in imagerecommendation. On the other hand, we have to model the complex aspects thatinfluence users' preferences to these highly subjective content from theheterogeneous data. In this paper, we develop an explainable social contextualimage recommendation model to simultaneously explain and predict users'preferences to images. Specifically, in addition to user interest modeling inthe standard recommendation, we identify three key aspects that affect eachuser's preference on the social platform, where each aspect summarizes acontextual representation from the complex relationships between users andimages. We design a hierarchical attention model in recommendation processgiven the three contextual aspects. Particularly, the bottom layered attentionnetworks learn to select informative elements of each aspect from heterogeneousdata, and the top layered attention network learns to score the aspectimportance of the three identified aspects for each user. In this way, we couldovercome the data sparsity issue by leveraging the social contextual aspectsfrom heterogeneous data, and explain the underlying reasons for each user'sbehavior with the learned hierarchial attention scores. Extensive experimentalresults on real-world datasets clearly show the superiority of our proposedmodel." @default.
• W2807146906 created "2018-06-13" @default.
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• W2807146906 date "2018-06-03" @default.
• W2807146906 modified "2023-09-26" @default.
• W2807146906 title "Explainable Social Contextual Image Recommendation with Hierarchical Attention." @default.
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• W2807146906 hasPublicationYear "2018" @default.
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