Addressing the Item Cold-Start Problem by Attribute-Driven Active Learning
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TLDR
Wang et al. as mentioned in this paper designed useful user selection criteria based on items' attributes and users' rating history, and combine the criteria in an optimization framework for selecting users, then generate accurate rating predictions for the other unselected users.Abstract:
In recommender systems, cold-start issues are situations where no previous events, e.g., ratings, are known for certain users or items. In this paper, we focus on the item cold-start problem. Both content information (e.g., item attributes) and initial user ratings are valuable for seizing users’ preferences on a new item. However, previous methods for the item cold-start problem either (1) incorporate content information into collaborative filtering to perform hybrid recommendation, or (2) actively select users to rate the new item without considering content information and then do collaborative filtering. In this paper, we propose a novel recommendation scheme for the item cold-start problem by leveraging both active learning and items’ attribute information. Specifically, we design useful user selection criteria based on items’ attributes and users’ rating history, and combine the criteria in an optimization framework for selecting users. By exploiting the feedback ratings, users’ previous ratings and items’ attributes, we then generate accurate rating predictions for the other unselected users. Experimental results on two real-world datasets show the superiority of our proposed method over traditional methods.read more
Citations
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Proceedings ArticleDOI
Meta-learning on Heterogeneous Information Networks for Cold-start Recommendation
Yuanfu Lu,Yuan Fang,Chuan Shi +2 more
TL;DR: This work proposes a novel semantic-enhanced tasks constructor and a co-adaptation meta-learner to address the two questions for how to capture HIN-based semantics in the meta-learning setting, and how to learn the general knowledge that can be easily adapted to multifaceted semantics.
Proceedings ArticleDOI
Meta-Learning for User Cold-Start Recommendation
TL;DR: This work designs a recommendation framework that is trained to be reasonably good enough for a wide range of users and handles the user cold-start model much better than state-of-the art benchmark recommender systems.
Proceedings ArticleDOI
SamWalker: Social Recommendation with Informative Sampling Strategy
TL;DR: A new recommendation method SamWalker is proposed that leverages social information to infer data confidence and guide the sampling process by modeling data confidence with a social context-aware function and can adaptively specify different weights to different data based on users' social contexts.
Journal ArticleDOI
Joint Personalized Markov Chains with social network embedding for cold-start recommendation
Zhang Yijia,Zhang Yijia,Zhenkun Shi,Zhenkun Shi,Zhenkun Shi,Wanli Zuo,Wanli Zuo,Lin Yue,Lin Yue,Shining Liang,Shining Liang,Xue Li,Xue Li +12 more
TL;DR: This paper proposes a Joint Personalized Markov Chains (JPMC) model to address the cold-start issues for implicit feedback recommendation system and designed a two-level model based on Markov chains at both user level and user group level respectively to model user preferences dynamically.
Proceedings Article
Active Learning on a Budget: Opposite Strategies Suit High and Low Budgets
TL;DR: In a comparative empirical investigation of supervised learning, using a variety of architectures and image datasets, TypiClust outperforms all other active learning strategies in the low-budget regime and is proposed – a deep active learning strategy suited for low budgets.
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