Real Estate Recommendation Approach for Solving the Item Cold-Start Problem
IEEE Access, 2021 The item cold-start problem occurs when a recommendation system cannot recommend new items owing to record deficiencies and new listing omissions. When searching for real estate, users can register a concurrent interest in recent and prior projects. Thus,
Jirut Polohakul +3 more
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User Cold Start Problem in Recommendation Systems: A Systematic Review
IEEE Access, 2023 The recommendation system makes recommendations based on the preferences of the users. These user preferences usually come from the user’s basic information, item rating, historical data, and so on.
Hongli Yuan, Alexander A. Hernandez
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ML2E: Meta-Learning Embedding Ensemble for Cold-Start Recommendation
IEEE Access, 2020 Cold-start problem has been recognized as the most crucial challenge in recommender systems. Many recommendation algorithms work well when lots of preference information is available but start to degrade in cold-start settings.
Huiwei Wang, Yong Zhao
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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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Content-Aware Few-Shot Meta-Learning for Cold-Start Recommendation on Portable Sensing Devices [PDF]
SensorsThe cold-start problem in sequence recommendations presents a critical and challenging issue for portable sensing devices. Existing content-aware approaches often struggle to effectively distinguish the relative importance of content features and ...
Xiaomin Lv, Kai Fang, Tongcun Liu
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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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CSRLoan: Cold Start Loan Recommendation with Semantic-Enhanced Neural Matrix Factorization
Applied Sciences, 2022 Recommending loan products to applicants would benefit many financial businesses and individuals. Nevertheless, many loan products suffer from the cold start problem; i.e., there are no available historical data for training the recommendation model ...
Kai Zhuang, Sen Wu, Shuaiqi Liu
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Recommendation Method for User Cold-start Based on Multi-graph Rank [PDF]
Jisuanji gongcheng, 2019 In order to integrate the heterogeneous information such as user-item scoring,social network and social labeling into the nearest neighbor search process of collaborative filtering recommendation method,and to make up for the lack of information on the ...
MAO Mingsong,ZHANG Fuguo
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RACRec: Review Aware Cross-Domain Recommendation for Fully-Cold-Start User
IEEE Access, 2020 Traditional recommendation algorithms such as matrix factorization, collaborative filtering perform poorly when lack of interactive information of user and product, known as the user cold-start problem, which may cut down the revenue of E-Commerce ...
Yaru Jin +3 more
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Improving Cold Start Stereotype-Based Recommendation Using Deep Learning
IEEE Access, 2023 Recommendation engines constitute a key component of many online platforms. One of the most challenging problems facing a recommender system is that of cold start, namely the recommendation of items from the catalogue to a new unknown user, or the ...
Nourah A. Al-Rossais
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