Results 31 to 40 of about 2,553,529 (294)
Learning to Learn a Cold-start Sequential Recommender [PDF]
ACM Transactions on Information Systems, 2022 The cold-start recommendation is an urgent problem in contemporary online applications. It aims to provide users whose behaviors are literally sparse with as accurate recommendations as possible. Many data-driven algorithms, such as the widely used matrix factorization, underperform because of data sparseness. This work adopts the idea of meta-learning
Xiaowen Huang 0001 +3 more
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An Efficient Cross-Domain Recommendation Technique in Cold-Start Situations [PDF]
, 2017 Most of the recent studies on recommender systems are focused on single domain recommendation systems. In the single domain recommendation systems, the items that are used for training and test data set are belongs to within the same domain.
, V. Kiran Kumar Reddy, K. Ramesh, T. Subramanyam
core +1 more source
A Flexible Two-Tower Model for Item Cold-Start Recommendation
IEEE Access, 2023 One of the main challenges in recommendation system is the item cold-start problem, where absence of historical interactions or ratings in new items makes recommendation difficult.
Won-Min Lee, Yoon-Sik Cho
doaj +1 more source
Cold-start playlist recommendation with multitask learning [PDF]
, 2018 Playlist recommendation involves producing a set of songs that a user might enjoy. We investigate this problem in three cold-start scenarios: (i) cold playlists, where we recommend songs to form new personalised playlists for an existing user; (ii) cold users, where we recommend songs to form new playlists for a new user; and (iii) cold songs, where we
Dawei Chen +2 more
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A Monte Carlo algorithm for cold start recommendation [PDF]
Proceedings of the 23rd international conference on World wide web, 2014 Recommendation systems have been widely used in E-commerce sites, social networks, etc. One of the core tasks in recommendation systems is to predict the users' ratings on items. Although many models and algorithms have been proposed, how to make accurate prediction for new users with extremely few rating records still remains a big challenge, which is
Yu Rong 0001, Xiao Wen, Hong Cheng 0001
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Contextual bandits to increase user prediction accuracy in movie recommendation system [PDF]
ITM Web of ConferencesCold-start problems are inevitable phenomena where recommendation systems fail to accurately predict users’ favour and cause the loss of new users. The typical Multi-Armed Bandit (MAB) models are widely adopted as recommendation systems to solve cold ...
Chen Yizhe
doaj +1 more source
Comprehensive Trust Recommendation Algorithm Based on User Fuzzy Clustering [PDF]
Jisuanji gongcheng, 2021 The traditional collaborative filtering recommendation algorithm is limited by data scarcity and the cold start problem.Leveraging the trust relationships between users, this paper proposes a comprehensive trust recommendation algorithm based on Fuzzy C ...
JIA Junjie, ZHANG Yuchao
doaj +1 more source
Representation Learning for cold-start recommendation
, 2014 A standard approach to Collaborative Filtering (CF), i.e. prediction of user ratings on items, relies on Matrix Factorization techniques. Representations for both users and items are computed from the observed ratings and used for prediction. Unfortunatly, these transductive approaches cannot handle the case of new users arriving in the system, with no
Contardo, Gabriella +2 more
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Privileged Graph Distillation for Cold Start Recommendation [PDF]
Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2021 10 pages,5 ...
Shuai Wang 0020 +5 more
openaire +4 more sources
Spatial-Temporal Topic Model for Cold-Start Event Recommendation
IEEE Access, 2020 Event recommendation has attracted an increasing attention with the popularity of event-based social networks (EBSNs). The previous studies mainly focus on exploiting various contextual information to alleviate the cold-start problem in event ...
Ruichang Li +3 more
doaj +1 more source