Results 21 to 30 of about 7,593,003 (291)
Hybrid Deep Framework for Group Event Recommendation
IEEE Access, 2020 Group recommender systems suggesting items for a group of users have received many attentions recently. Some aggregation-based and model-based group recommendation methods have been proposed.
Ruichang Li +3 more
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Memory-augmented meta-learning on meta-path for fast adaptation cold-start recommendation
Connection Science, 2022 Personalised recommendation is a difficult problem that has received a lot of attention to academia and industry. Because of the sparse user–item interaction, cold-start recommendation has been a particularly difficult problem.
Tianyuan Li +7 more
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HyperRS: Hypernetwork-Based Recommender System for the User Cold-Start Problem
IEEE Access, 2023 Meta-learning has been proven to be effective for the cold-start problem of recommender systems. Many meta-learning recommender systems that are designed for the user cold-start problem are gradient-based.
Yuxun Lu +2 more
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Survey on Solving Cold Start Problem in Recommendation Systems [PDF]
Jisuanji kexue yu tansuoRecommender systems provide important functions in areas such as dealing with data overload, providing personalized consulting services, and assisting clients in investment decisions. However, the cold start problem in recommender systems has always been
MAO Qian, XIE Weicheng, QIAO Yitian, HUANG Xiaolong, DONG Gang
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Eliciting Auxiliary Information for Cold Start User Recommendation: A Survey
Applied Sciences, 2021 Recommender systems suggest items of interest to users based on their preferences. These preferences are typically generated from user ratings of the items. If there are no ratings for a certain user or item, it is said that there is a cold start problem,
Nor Aniza Abdullah +3 more
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Using Association Rules to Solve the Cold-Start Problem in Recommender Systems [PDF]
, 2010 Recommender systems are widely used online to help users\ud find other products, items etc that they may be interested in based on what is known about that user in their profile.
Geva, Shlomo +5 more
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Future Internet, 2021 At present, most mobile App start-up prediction algorithms are only trained and predicted based on single-user data. They cannot integrate the data of all users to mine the correlation between users, and cannot alleviate the cold start problem of new ...
Shaoyong Li +3 more
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Improved Epsilon-greedy Algorithm for Cold-start Problem of New Users [PDF]
Jisuanji gongcheng, 2018 When solving the cold-start problem of new users,fixed and invariant Epsilon parameters will slow the convergence of traditional Epsilon-greedy algorithm.Therefore,an improved Epsilon-greedy algorithm is proposed.Immune feedback model is used to ...
WANG Suqin,ZHANG Yang,JIANG Hao,ZHU Dengming
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Two-stage system using item features for next-item recommendation
Intelligent Systems with Applications, 2022 The next-item recommendation task aims to predict the item that a user is likely to interact with next, given the previous transactions of the user. In this paper, we explore the use of discrete item features to improve the performance of a next-item ...
Madiraju Srilakshmi +2 more
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A Conscious Cross-Breed Recommendation Approach Confining Cold-Start in Electronic Commerce Systems
IEEE Access, 2023 When a new customer enters the spectrum of the E-Commerce system, the informative records and dataset, such as about the new user, purchasing history and other browsing data become insufficient, resulting in the emergence of one serious issue such as a ...
S. Gopal Krishna Patro +6 more
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