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Given their strong performance on a variety of graph learning tasks, Graph Neural Networks (GNNs) are increasingly used to model financial networks. Traditional GNNs, however, are not able to capture higher-order topological information, and their ...

Tips, as a compacted and concise form of reviews, were paid less attention by researchers. In this paper, we investigate the task of tips generation by considering the “persona” information which captures the intrinsic language style of the users or the ...

Many predictive tasks in recommender systems model from categorical variables. Different from continuous features extracted from images and videos, categorical data is discrete and of multi-field while their dependencies are little known, which brings ...

Exploiting contextual information is considered a good solution to improve the quality of recommendations, aiming at suggesting more relevant items for a specific context. On the other hand, recommender systems research still strive for solving the cold-...

There are usually limited user evaluation of resources on a recommender system, which caused an extremely sparse user rating matrix, and this greatly reduce the accuracy of personalized recommendation, especially for new users or new items. This paper ...

Recommender systems in online marketing websites like Amazon.com and CDNow.com suggest relevant services and favorite products to customers. In this paper, we proposed a novel hypergraph-based kernel computation combined with k nearest neighbor (kNN) to ...

We propose a novel method to improve the prediction accuracy on the rating prediction task by correcting the bias of user ratings. We demonstrate that the manner of user rating and review is biased and that it is necessary to correct this difference for ...

This paper proposes and evaluates several alternate design choices for common prediction metrics employed by neighborhood-based collaborative filtering approach. It first explores the role of different baseline user averages as the foundation of ...