Dimitrios Rafailidis
Fabio Crestani
ABSTRACT
Nowadays, users open multiple accounts on social media platforms and e-commerce sites, expressing their personal preferences on different domains. However, users' behaviors change across domains, depending on the content that users interact with, such as movies, music, clothing and retail products. The main challenge is how to capture users' complex preferences when generating cross-domain recommendations, that is exploiting users' preferences from source domains to generate recommendations in a target domain. In this study, we propose a Neural Attentive Cross-domain model, namely NAC. We design a neural architecture, to carefully transfer the knowledge of user preferences across domains by taking into account the cross-domain latent effects of multiple source domains on users' selections in a target domain. In addition, we introduce a cross-domain behavioral attention mechanism to adaptively perform the weighting of users' preferences from the source domains, and consequently generate accurate cross-domain recommendations. Our experiments on ten cross-domain recommendation tasks show that the proposed NAC model achieves higher recommendation accuracy than other state-of-the-art methods for both ordinary and cold-start users. Furthermore, we study the effect of the proposed cross-domain behavioral attention mechanism and show that it is a key factor to our model's performance.
- Mohammad Aliannejadi, Dimitrios Rafailidis, and Fabio Crestani. 2017. Personalized Keyword Boosting for Venue Suggestion Based on Multiple LBSNs. In Proceedings of ECIR. 291--303.Google Scholar
Cross Ref
- Stefanos Antaris, Dimitrios Rafailidis, and Alexandros Nanopoulos. 2014. Link injection for boosting information spread in social networks. Social Netw. Analys. Mining , Vol. 4, 1 (2014), 236.Google ScholarCross Ref
- Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015. Neural Machine Translation by Jointly Learning to Align and Translate. In Proceedings of ICLR.Google Scholar
- Shlomo Berkovsky, Tsvi Kuflik, and Francesco Ricci. 2007. Distributed collaborative filtering with domain specialization. In Proceedings of ACM RecSys . 33--40.Google ScholarDigital Library
- Paolo Cremonesi, Yehuda Koren, and Roberto Turrin. 2010. Performance of recommender algorithms on top-n recommendation tasks. In Proceedings of RecSys. 39--46.Google ScholarDigital Library
- Paolo Cremonesi, Antonio Tripodi, and Roberto Turrin. 2011. Cross-Domain Recommender Systems. In Proceedings of ICDMW. 496--503.Google ScholarDigital Library
- Travis Ebesu, Bin Shen, and Yi Fang. 2018. Collaborative Memory Network for Recommendation Systems. In Proceedings of SIGIR. 515--524.Google ScholarDigital Library
- Sheng Gao, Hao Luo, Da Chen, Shantao Li, Patrick Gallinari, and Jun Guo. 2013a. Cross-Domain Recommendation via Cluster-Level Latent Factor Model. In Proceedings of ECML PKDD. 161--176.Google ScholarCross Ref
- Shenghua Gao, Ivor Wai-Hung Tsang, and Liang-Tien Chia. 2013b. Laplacian Sparse Coding, Hypergraph Laplacian Sparse Coding, and Applications. IEEE Trans. Pattern Anal. Mach. Intell. , Vol. 35, 1 (2013), 92--104.Google ScholarDigital Library
- Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017a. Neural Collaborative Filtering. In Proceedings of WWW. 173--182.Google ScholarDigital Library
- Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017b. Neural Collaborative Filtering. In Proceedings of WWW. 173--182.Google ScholarDigital Library
- Guangneng Hu, Yu Zhang, and Qiang Yang. 2018. CoNet: Collaborative Cross Networks for Cross-Domain Recommendation. In Proceedings of CIKM. 667--676.Google ScholarDigital Library
- Liang Hu, Jian Cao, Guandong Xu, Longbing Cao, Zhiping Gu, and Can Zhu. 2013. Personalized recommendation via cross-domain triadic factorization. In Proceedings of WWW. 595--606.Google ScholarDigital Library
- Yehuda Koren, Robert M. Bell, and Chris Volinsky. 2009. Matrix Factorization Techniques for Recommender Systems. IEEE Computer , Vol. 42, 8 (2009), 30--37.Google ScholarDigital Library
- Bin Li, Qiang Yang, and Xiangyang Xue. 2009. Can Movies and Books Collaborate? Cross-Domain Collaborative Filtering for Sparsity Reduction. In Proceedings of IJCAI. 2052--2057.Google Scholar
- Qiao Liu, Yifu Zeng, Refuoe Mokhosi, and Haibin Zhang. 2018. STAMP: Short-Term Attention/Memory Priority Model for Session-based Recommendation. In Proceedings of KDD. 1831--1839.Google ScholarDigital Library
- Babak Loni, Yue Shi, Martha Larson, and Alan Hanjalic. 2014. Cross-Domain Collaborative Filtering with Factorization Machines. In Proceedings of ECIR. 656--661.Google ScholarCross Ref
- Jarana Manotumruksa, Craig Macdonald, and Iadh Ounis. 2018. A Contextual Attention Recurrent Architecture for Context-Aware Venue Recommendation. In Proceedings of SIGIR. 555--564.Google ScholarDigital Library
- Simon Meyffret, Emmanuel Guillot, Lionel Medini, Frederique Laforest Marcin Pilipczuk, Michal Pilipczuk, and Jakub Onufry Wojtaszczyk. 2012. RED: a Rich Epinions Dataset for Recommender Systems. LIRIS , Vol. hal-01010246 (2012).Google Scholar
- Ishan Misra, Abhinav Shrivastava, Abhinav Gupta, and Martial Hebert. 2016. Cross-Stitch Networks for Multi-task Learning. In Proceedings of IEEE, CVPR. 3994--4003.Google ScholarCross Ref
- Weike Pan, Evan Wei Xiang, Nathan Nan Liu, and Qiang Yang. 2010. Transfer Learning in Collaborative Filtering for Sparsity Reduction. In Proceedings of AAAI.Google ScholarCross Ref
- Dimitrios Rafailidis and Fabio Crestani. 2016. Collaborative Ranking with Social Relationships for Top-N Recommendations. In Proceedings of SIGIR. 785--788.Google Scholar
- Dimitrios Rafailidis and Alexandros Nanopoulos. 2015. Repeat Consumption Recommendation Based on Users Preference Dynamics and Side Information. In Proceedings of WWW - Companion Volume . 99--100.Google ScholarDigital Library
- Dimitrios Rafailidis, Theodoros Semertzidis, Michalis Lazaridis, Michael G. Strintzis, and Petros Daras. 2013. A Data-Driven Approach for Social Event Detection. In Proceedings of the MediaEval 2013 Multimedia Benchmark Workshop .Google Scholar
- Steffen Rendle. 2012. Factorization Machines with libFM. ACM TIST , Vol. 3, 3 (2012), 57:1--57:22.Google ScholarDigital Library
- Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian Personalized Ranking from Implicit Feedback. In Proceedings of UAI . 452--461.Google Scholar
- Peijie Sun, Le Wu, and Meng Wang. 2018. Attentive Recurrent Social Recommendation. In Proceedings of SIGIR. 185--194.Google ScholarDigital Library
- Yi Tay, Anh Tuan Luu, and Siu Cheung Hui. 2018. Multi-Pointer Co-Attention Networks for Recommendation. In Proceedings of KDD. 2309--2318.Google ScholarDigital Library
- Jun Xiao, Hao Ye, Xiangnan He, Hanwang Zhang, Fei Wu, and Tat-Seng Chua. 2017. Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks. In Proceedings of IJCAI. 3119--3125.Google ScholarCross Ref
- Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron C. Courville, Ruslan Salakhutdinov, Richard S. Zemel, and Yoshua Bengio. 2015. Show, Attend and Tell: Neural Image Caption Generation with Visual Attention. In Proceedings of ICML. 2048--2057.Google ScholarDigital Library
- Lie Xu, Chiu-sing Choy, and Yi-Wen Li. 2016. Deep sparse rectifier neural networks for speech denoising. In Proceedings of the IEEE International Workshop on Acoustic Signal Enhancement . 1--5.Google ScholarCross Ref
Index Terms
- Neural Attentive Cross-Domain Recommendation
Recommendations
Domain ranking for cross domain collaborative filtering
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