Zobrazeno 1 - 10
of 49
pro vyhledávání: '"Yongfeng Zhang"'
Publikováno v:
ACM Transactions on Information Systems. 39:1-22
Understanding user preference is of key importance for an effective recommender system. For comprehensive user profiling, many efforts have been devoted to extract user feature-level preference from the review information. Despite effectiveness, exis
Publikováno v:
ACM Transactions on Information Systems. 38:1-28
Recommendation systems play a vital role to keep users engaged with personalized contents in modern online platforms. Recently, deep learning has revolutionized many research fields and there is a surge of interest in applying it for recommendation.
Publikováno v:
WWW
Knowledge Graph (KG) is a flexible structure that is able to describe the complex relationship between data entities. Currently, most KG embedding models are trained based on negative sampling, i.e., the model aims to maximize some similarity of the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5c1568a948efef90239b4b4ec2eccd1d
http://arxiv.org/abs/2104.10796
http://arxiv.org/abs/2104.10796
Publikováno v:
WWW
Existing Collaborative Filtering (CF) methods are mostly designed based on the idea of matching, i.e., by learning user and item embeddings from data using shallow or deep models, they try to capture the associative relevance patterns in data, so tha
Publikováno v:
AAAI
Providing explanations in a recommender system is getting more and more attention in both industry and research communities. Most existing explainable recommender models regard user preferences as invariant to generate static explanations. However, i
Publikováno v:
ACM Transactions on Information Systems. 37:1-27
In recent years, many studies extract aspects from user reviews and integrate them with ratings for improving the recommendation performance. The common aspects mentioned in a user's reviews and a product's reviews indicate indirect connections betwe
Publikováno v:
ACM Transactions on Information Systems. 37:1-28
Integrating external knowledge into the recommendation system has attracted increasing attention in both industry and academic communities. Recent methods mostly take the power of neural network for effective knowledge representation to improve the r
Publikováno v:
NAACL-HLT
Knowledge graphs (KG) have become increasingly important to endow modern recommender systems with the ability to generate traceable reasoning paths to explain the recommendation process. However, prior research rarely considers the faithfulness of th
Publikováno v:
RecSys
Recent years have witnessed the emerging of conversational systems, including both physical devices and mobile-based applications. Both the research community and industry believe that conversational systems will have a major impact on human-computer
Autor:
Yongfeng Zhang, Xinxing Yu, Min Zhang, Yiqun Liu, Weizhi Ma, Shaoyun Shi, Houzhi Shan, Shaoping Ma
Publikováno v:
SIGIR
Modeling large scale and rare-interaction users are the two major challenges in recommender systems, which derives big gaps between researches and applications. Facing to millions or even billions of users, it is hard to store and leverage personaliz