Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/231240
Title: R<sc>a</sc>P<sc>are</sc>: A Generic Strategy for Cold-Start Rating Prediction Problem
Authors: Jingwei Xu;Yuan Yao;Hanghang Tong;Xianping Tao;Jian Lu
Year: 2017
Publisher: IEEE
Abstract: In recent years, recommender system is one of indispensable components in many e-commerce websites. One of the major challenges that largely remains open is the cold-start problem, which can be viewed as a barrier that keeps the cold-start users/items away from the existing ones. In this paper, we aim to break through this barrier for cold-start users/items by the assistance of existing ones. In particular, inspired by the classic Elo Rating System, which has been widely adopted in chess tournaments, we propose a novel rating comparison strategy (RAPARE) to learn the latent profiles of cold-start users/items. The centerpiece of our RAPARE is to provide a fine-grained calibration on the latent profiles of cold-start users/items by exploring the differences between cold-start and existing users/items. As a generic strategy, our proposed strategy can be instantiated into existing methods in recommender systems. To reveal the capability of RAPARE strategy, we instantiate our strategy on two prevalent methods in recommender systems, i.e., the matrix factorization based and neighborhood based collaborative filtering. Experimental evaluations on five real data sets validate the superiority of our approach over the existing methods in cold-start scenario.
URI: http://localhost/handle/Hannan/231240
volume: 29
issue: 6
More Information: 1296,
1309
Appears in Collections:2017

Files in This Item:
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7582500.pdf1 MBAdobe PDF
Title: R<sc>a</sc>P<sc>are</sc>: A Generic Strategy for Cold-Start Rating Prediction Problem
Authors: Jingwei Xu;Yuan Yao;Hanghang Tong;Xianping Tao;Jian Lu
Year: 2017
Publisher: IEEE
Abstract: In recent years, recommender system is one of indispensable components in many e-commerce websites. One of the major challenges that largely remains open is the cold-start problem, which can be viewed as a barrier that keeps the cold-start users/items away from the existing ones. In this paper, we aim to break through this barrier for cold-start users/items by the assistance of existing ones. In particular, inspired by the classic Elo Rating System, which has been widely adopted in chess tournaments, we propose a novel rating comparison strategy (RAPARE) to learn the latent profiles of cold-start users/items. The centerpiece of our RAPARE is to provide a fine-grained calibration on the latent profiles of cold-start users/items by exploring the differences between cold-start and existing users/items. As a generic strategy, our proposed strategy can be instantiated into existing methods in recommender systems. To reveal the capability of RAPARE strategy, we instantiate our strategy on two prevalent methods in recommender systems, i.e., the matrix factorization based and neighborhood based collaborative filtering. Experimental evaluations on five real data sets validate the superiority of our approach over the existing methods in cold-start scenario.
URI: http://localhost/handle/Hannan/231240
volume: 29
issue: 6
More Information: 1296,
1309
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7582500.pdf1 MBAdobe PDF
Title: R<sc>a</sc>P<sc>are</sc>: A Generic Strategy for Cold-Start Rating Prediction Problem
Authors: Jingwei Xu;Yuan Yao;Hanghang Tong;Xianping Tao;Jian Lu
Year: 2017
Publisher: IEEE
Abstract: In recent years, recommender system is one of indispensable components in many e-commerce websites. One of the major challenges that largely remains open is the cold-start problem, which can be viewed as a barrier that keeps the cold-start users/items away from the existing ones. In this paper, we aim to break through this barrier for cold-start users/items by the assistance of existing ones. In particular, inspired by the classic Elo Rating System, which has been widely adopted in chess tournaments, we propose a novel rating comparison strategy (RAPARE) to learn the latent profiles of cold-start users/items. The centerpiece of our RAPARE is to provide a fine-grained calibration on the latent profiles of cold-start users/items by exploring the differences between cold-start and existing users/items. As a generic strategy, our proposed strategy can be instantiated into existing methods in recommender systems. To reveal the capability of RAPARE strategy, we instantiate our strategy on two prevalent methods in recommender systems, i.e., the matrix factorization based and neighborhood based collaborative filtering. Experimental evaluations on five real data sets validate the superiority of our approach over the existing methods in cold-start scenario.
URI: http://localhost/handle/Hannan/231240
volume: 29
issue: 6
More Information: 1296,
1309
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7582500.pdf1 MBAdobe PDF