Yue Zhao
ABSTRACT
To understand users’ needs is an essential problem of automatic recommendation. Current researches often describe users' needs through their side attributes. However, motivation is the direct reason of users’ needs and choice behavior. In real social scenario, it is a challenge to measure the complex motivation of users. Inspired by psychological definitions, in recommendation, we propose to distinguish the Intrinsic Motivation driven by each individual's internal interests and the Extrinsic Motivation coming from the influence of social group. With latent factors of users and items, two motivations are separately modeled and integrated for recommendation with Q-learning (Off-policy), which improve the effectiveness and explainability of recommendation. Experiments on real-world datasets demonstrate the validity of our model compared with state-of-the-art methods.
- S. Zhang, L. Yao, A. Sun, and Y. Tay, “Deep learning based recommender system: A survey and new perspectives,” ACM Comput. Surv., vol. 52, no. 1, pp. 5:1–5:38, 2019.Google Scholar
Digital Library
- X. Dong, L. Yu, Z. Wu, Y. Sun, L. Yuan, and F. Zhang, “A hybrid collaborative filtering model with deep structure for recommender systems,” in Proceedings of the 21st AAAI Conference on Artificial Intelligence, 2017, pp. 1309–1315.Google ScholarCross Ref
- D. H. Kim, C. Park, J. Oh, S. Lee, and H. Yu, “Convolutional matrix factorization for document context-aware recommendation,” in Proceedings of the 10th ACM RecSys Conference on Recommender Systems, 2016, pp. 233–240.Google ScholarDigital Library
- F. Zhang, N. J. Yuan, D. Lian, X. Xie, and W. Ma, “Collaborative knowledge base embedding for recommender systems,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016, pp. 353–362.Google ScholarDigital Library
- S. Sedhain, A. K. Menon, S. Sanner, and L. Xie, “Autorec : Autoencoders meet collaborative filtering,” in Proceedings of the 24th WWW International Conference on World Wide Web Companion, 2015, pp. 111–112.Google ScholarDigital Library
- Y. Shan, T. R. Hoens, J. Jiao, H. Wang, D. Yu, and J. C. Mao, “Deep crossing: Web-scale modeling without manually crafted combinatorial features,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016, pp. 255– 262.Google ScholarDigital Library
- X. He, L. Liao, H. Zhang, L. Nie, X. Hu, and T. Chua, “Neural collaborative filtering,” in Proceedings of the 26th International Conference on World Wide Web, WWW 2017, 2017, pp. 173–182.Google ScholarDigital Library
- Y. Qu, H. Cai, K. Ren, W. Zhang, Y. Yu, Y. Wen, and J. Wang, “Product-based neural networks for user response prediction,” in Proceedings of the 16th ICDM IEEE International Conference on Data Mining, 2016, pp. 1149–1154.Google ScholarCross Ref
- X. He and T. Chua, “Neural factorization machines for sparse predictive analytics,” in Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2017, pp. 355–364.Google ScholarDigital Library
- W. Zhang, T. Du, and J. Wang, “Deep learning over multi-field categorical data : A case study on user response prediction,” in Proceedings of the 38th ECIR European Conference on IR Research, vol. 9626, 2016, pp. 45–57.Google Scholar
- P. Covington, J. Adams, and E. Sargin, “Deep neural networks for YouTube recommendations,” in Proceedings of the 10th ACM Conference on Recommender Systems, 2016, pp. 191–198.Google ScholarDigital Library
- X. Wang, Y. Wang, and Y. Ling, “Attention-guide walk model in heterogeneous information network for multi-style recommendation explanation,” in Proceedings of the 34th AAAI Conference on Artificial Intelligence, 2020, pp. 6275–6282.Google ScholarCross Ref
- Q. Zhu, X. Zhou, J. Wu, J. Tan, and L. Guo, “A knowledge-aware attentional reasoning network for recommendation,” in Proceedings of the 34th AAAI Conference on Artificial Intelligence, 2020, pp. 6999– 7006.Google ScholarCross Ref
- L. Chen, Y. Liu, X. He, L. Gao, and Z. Zheng, “Matching user with item set: Collaborative bundle recommendation with deep attention network,” in Proceedings of the 28th IJCAI International Joint Conference on Artificial Intelligence, 2019, pp. 2095–2101.Google ScholarCross Ref
- J. Chen, H. Zhang, X. He, L. Nie, W. Liu, and T. Chua, “Attentive collaborative filtering: Multimedia recommendation with item- and component-level attention,” in Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2017, pp. 335–344.Google ScholarDigital Library
- G. Zhou, X. Zhu, C. Song, Y. Fan, H. Zhu, X. Ma, Y. Yan, J. Jin, H. Li, and K. Gai, “Deep interest network for click-through rate prediction,” in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Y. Guo and F. Farooq, Eds., 2018, pp. 1059–1068.Google ScholarDigital Library
- Y. Li and K. Mu, “Multi-attention item recommendation model based on social relations,” in Proceedings of the 12th KSEM International Conference on Knowledge Science, Engineering and Management, 2019, pp. 84–95.Google ScholarDigital Library
- N. Mu, D. Zha, Y. He, and Z. Tang, “Graph attention networks for neural social recommendation,” in 31st IEEE ICTAI International Conference on Tools with Artificial Intelligence, 2019, pp. 1320–1327.Google ScholarCross Ref
- J. Sanz-Cruzado, P. Castells, and E. Lo ́pez, “A simple multi-armed nearest-neighbor bandit for interactive recommendation,” in Proceedings of the 13th ACM RecSys Conference on Recommender Systems, 2019, pp. 358–362.Google ScholarDigital Library
- N. Gutowski, O. Camp, F. Chhel, T. Amghar, and P. Albers, “Improving bandit-based recommendations with spatial context reasoning: An online evaluation,” in 31st IEEE ICTAI International Conference on Tools with Artificial Intelligence, 2019, pp. 1366–1373.Google ScholarCross Ref
- N. Gutowski, T. Amghar, O. Camp, and F. Chhel, “Gorthaur: A portfolio approach for dynamic selection of multi-armed bandit algorithms for recommendation,” in 31st IEEE ICTAI International Conference on Tools with Artificial Intelligence, 2019, pp. 1164–1171.Google ScholarCross Ref
- H. Cheng, L. Koc, J. Harmsen, T. Shaked, T. Chandra, H. Aradhye, G. Anderson, G. Corrado, W. Chai, M. Ispir, R. Anil, Z. Haque, L. Hong, V. Jain, X. Liu, and H. Shah, “Wide & Deep learning for recommender systems,” in Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, DLRS@RecSys 2016, 2016, pp. 7–10.Google ScholarDigital Library
- X. Wang, X. He, F. Feng, L. Nie, and T. Chua, “TEM: tree-enhanced embedding model for explainable recommendation,” in Proceedings of the 27th WWW Conference on World Wide Web, 2018, pp. 1543–1552.Google ScholarDigital Library
- R. M. Ryan and E. L. Deci, “Intrinsic and extrinsic motivations: Classic definitions and new directions,” Contemporary educational psychology, vol. 25, no. 1, pp. 54–67, 2000.Google ScholarCross Ref
- R. S. Sutton and A. G. Barto, “Reinforcement learning: An introduction,” IEEE Trans. Neural Networks, vol. 9, no. 5, pp. 1054–1054, 1998.Google ScholarDigital Library
- B. M. Sarwar, G. Karypis, J. A. Konstan, and J. Riedl, “Item-based collaborative filtering recommendation algorithms,” in Proceedings of the 10th WWW International World Wide Web Conference, V. Y. Shen, N. Saito, M. R. Lyu, and M. E. Zurko, Eds., 2001, pp. 285–295.Google ScholarDigital Library
- S. Rendle, “Factorization machines,” in Proceedings of the 10th ICDM IEEE International Conference on Data Mining, 2010, pp. 995–1000.Google ScholarDigital Library
- Y. Koren, R. M. Bell, and C. Volinsky, “Matrix factorization techniques for recommender systems,” IEEE Computer, vol. 42, no. 8, pp. 30–37, 2009.Google ScholarDigital Library
- S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-Thieme, “BPR: bayesian personalized ranking from implicit feedback,” in Proceedings of the 29th UAI Conference on Uncertainty in Artificial Intelligence, 2009, pp. 452–461.Google Scholar
- X. He, J. Pan, O. Jin, T. Xu, B. Liu, T. Xu, Y. Shi, A. Atallah, R. Herbrich, S. Bowers, and J. Q. Candela, “Practical lessons from predicting clicks on ads at facebook,” in Proceedings of the 8th ADKDD International Workshop on Data Mining for Online Advertising, 2014, pp. 5:1–5:9.Google ScholarDigital Library
- H. Zhang, F. Shen, W. Liu, X. He, H. Luan, and T. Chua, “Discrete collaborative filtering,” in Proceedings of the 39th SIGIR International ACM SIGIR conference on Research and Development in Information Retrieval, 2016, pp. 325–334.Google ScholarDigital Library
- H. Guo, R. Tang, Y. Ye, Z. Li, and X. He, “DeepFM: A factorization-machine based neural network for CTR prediction,” in Proceedings of the 26th IJCAI International Joint Conference on Artificial Intelligence, 2017, pp. 1725–1731.Google ScholarCross Ref
- G. Zheng, F. Zhang, Z. Zheng, Y. Xiang, N. J. Yuan, X. Xie, and Z. Li, “DRN: A deep reinforcement learning framework for news recommendation,” in Proceedings of the 27th WWW Conference on World Wide Web, 2018, pp. 167–176.Google ScholarDigital Library
- C. J. C. H. Watkins and P. Dayan, “Technical note Q-learning,” Mach. Learn., vol. 8, pp. 279–292, 1992.Google ScholarDigital Library
- R. Salakhutdinov and A. Mnih, “Probabilistic matrix factorization,” in Proceedings of the 21st NIPS Annual Conference on Neural Information Processing Systems, 2007, pp. 1257–1264.Google Scholar
- H. Wang, N. Wang, and D. Yeung, “Collaborative deep learning for recommender systems,” in Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015, pp. 1235–1244.Google ScholarDigital Library
- Improving Automatic Recommendation by Modeling Intrinsic and Extrinsic Motivation with Q-learning
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