Policy Gradients for Contextual Recommendations

Pan, Feiyang; Cai, Qingpeng; Tang, Pingzhong; Zhuang, Fuzhen; He, Qing

doi:10.1145/3308558.3313616

“…There are two kinds of methods to solve the cold-start problems. The first type actively solves cold-start by designing a decision making strategy, such as using contextual-bandits [14,20].…”

Section: Cold-start Recommendationmentioning

confidence: 99%

Personalized Transfer of User Preferences for Cross-domain Recommendation

Zhu

¹

,

Tang

²

,

Liu

³

et al. 2022

Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining

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Cold-start problem is still a very challenging problem in recommender systems. Fortunately, the interactions of the cold-start users in the auxiliary source domain can help cold-start recommendations in the target domain. How to transfer user's preferences from the source domain to the target domain, is the key issue in Crossdomain Recommendation (CDR) which is a promising solution to deal with the cold-start problem. Most existing methods model a common preference bridge to transfer preferences for all users. Intuitively, since preferences vary from user to user, the preference bridges of different users should be different. Along this line, we propose a novel framework named Personalized Transfer of User Preferences for Cross-domain Recommendation (PTUPCDR). Specifically, a meta network fed with users' characteristic embeddings is learned to generate personalized bridge functions to achieve personalized transfer of preferences for each user. To learn the meta network stably, we employ a task-oriented optimization procedure. With the meta-generated personalized bridge function, the user's preference embedding in the source domain can be transformed into the target domain, and the transformed user preference embedding can be utilized as the initial embedding for the cold-start user in the target domain. Using large real-world datasets, we conduct extensive experiments to evaluate the effectiveness of PTUPCDR on both cold-start and warm-start stages. The code has been available at https://github.com/easezyc/WSDM2022-PTUPCDR.

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“…There are two kinds of methods to solve the cold-start problems. The first type actively solves cold-start by designing a decision making strategy, such as using contextual-bandits [13,19].…”

Section: Cold-start Recommendationmentioning

confidence: 99%

Personalized Transfer of User Preferences for Cross-domain Recommendation

Zhu¹,

Tang²,

Liu³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Cold-start problem is still a very challenging problem in recommender systems. Fortunately, the interactions of the cold-start users in the auxiliary source domain can help cold-start recommendations in the target domain. How to transfer user's preferences from the source domain to the target domain, is the key issue in Crossdomain Recommendation (CDR) which is a promising solution to deal with the cold-start problem. Most existing methods model a common preference bridge to transfer preferences for all users. Intuitively, since preferences vary from user to user, the preference bridges of different users should be different. Along this line, we propose a novel framework named Personalized Transfer of User Preferences for Cross-domain Recommendation (PTUPCDR). Specifically, a meta network fed with users' characteristic embeddings is learned to generate personalized bridge functions to achieve personalized transfer of preferences for each user. To learn the meta network stably, we employ a task-oriented optimization procedure. With the meta-generated personalized bridge function, the user's preference embedding in the source domain can be transformed into the target domain, and the transformed user preference embedding can be utilized as the initial embedding for the cold-start user in the target domain. Using large real-world datasets, we conduct extensive experiments to evaluate the effectiveness of PTUPCDR on both cold-start and warm-start stages. The code has been available at https://github.com/easezyc/WSDM2022-PTUPCDR.

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“…The present cold-start problem is mainly studied in two directions. Some works tried to solve cold-start problems in the perspective of bandits [13]- [15]. A typical example is that Stephane et al embedded the preference of new users in a social network through utilizing the multi-armed bandits [13].…”

Section: Related Workmentioning

confidence: 99%

ML2E: Meta-Learning Embedding Ensemble for Cold-Start Recommendation

Wang

¹

,

Zhao

²

2020

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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. Inspired by the spirit of meta-learning, we identified that the appeal of cold-start problems and the superiority of meta-learning are congruous where meta-learning aims to learn a model from a small set of labeled examples (users' consuming history), and then this model can be quickly generalized to new tasks (recommendation for new users or new items). Therefore, faced with the extreme cold-start scenario, we proposed a meta-learning embedding ensemble (ML2E) recommendation algorithm to forecast new users' preference and generate desirable initial embedding for new items. It's worth pointing out that the training process of ML2E only uses few first-order gradient information, and ML2E not only has the incremental mining ability for different mini-batches on the same task but also the generalization ability for different tasks. Finally, we validated ML2E on two benchmark datasets, experimental results showed that our algorithm has significantly improved recommendation metric in comparison with three existing baselines. INDEX TERMS Cold-start setting, meta learning-based embedding, online recommendation algorithms.

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Policy Gradients for Contextual Recommendations

Cited by 30 publications

References 14 publications

Personalized Transfer of User Preferences for Cross-domain Recommendation

Personalized Transfer of User Preferences for Cross-domain Recommendation

Personalized Transfer of User Preferences for Cross-domain Recommendation

ML2E: Meta-Learning Embedding Ensemble for Cold-Start Recommendation

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