ABSTRACT
Serendipity is a notion that means an unexpected but valuable discovery. Due to its elusive and subjective nature, serendipity is difficult to study even with today's advances in machine learning and deep learning techniques. Both ground truth data collecting and model developing are the open research questions. This paper addresses both the data and the model challenges for identifying serendipity in recommender systems. For the ground truth data collecting, it proposes a new and scalable approach by using both user generated reviews and a crowd sourcing method. The result is a large-scale ground truth data on serendipity. For model developing, it designed a self-enhanced module to learn the fine-grained facets of serendipity in order to mitigate the inherent data sparsity problem in any serendipity ground truth dataset. The self-enhanced module is general enough to be applied with many base deep learning models for serendipity. A series of experiments have been conducted. As the result, a base deep learning model trained on our collected ground truth data, as well as with the help of the self-enhanced module, outperforms the state-of-the-art baseline models in predicting serendipity.
Supplemental Material
- Keping Bi, Qingyao Ai, and W Bruce Croft. 2020. A transformer-based embedding model for personalized product search. In Proceedings of the 43rd International ACM Conference on Research and Development in Information Retrieval (SIGIR). ACM, 1521--1524.Google ScholarDigital Library
- Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018).Google Scholar
- F Maxwell Harper and Joseph A Konstan. 2015. The movielens datasets: History and context. Acm Transactions on Interactive Intelligent Systems (TIIS), Vol. 5, 4 (2015), 1--19.Google ScholarDigital Library
- Jonathan L Herlocker, Joseph A Konstan, Loren G Terveen, and John T Riedl. 2004. Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems (TOIS), Vol. 22, 1 (2004), 5--53.Google ScholarDigital Library
- Jizhou Huang, Shiqiang Ding, Haifeng Wang, and Ting Liu. 2018. Learning to recommend related entities with serendipity for web search users. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP), Vol. 17, 3 (2018), 1--22.Google ScholarDigital Library
- Wang-Cheng Kang and Julian McAuley. 2018. Self-attentive sequential recommendation. In 2018 IEEE International Conference on Data Mining (ICDM). IEEE, 197--206.Google ScholarCross Ref
- Denis Kotkov, Joseph A Konstan, Qian Zhao, and Jari Veijalainen. 2018. Investigating serendipity in recommender systems based on real user feedback. In Proceedings of the 33rd Annual ACM Symposium on Applied Computing (SAC). ACM, 1341--1350.Google ScholarDigital Library
- Tuck Wah Leong, Peter Wright, Frank Vetere, and Steve Howard. 2010. Understanding experience using dialogical methods: The case of serendipity. In Proceedings of the 22nd Conference of the Computer-Human Interaction Special Interest Group of Australia on Computer-Human Interaction. 256--263.Google ScholarDigital Library
- Pan Li, Maofei Que, Zhichao Jiang, Yao Hu, and Alexander Tuzhilin. 2020b. PURS: personalized unexpected recommender system for improving user satisfaction. In Proceedings of the 14th ACM Conference on Recommender Systems (RecSys). ACM, 279--288.Google ScholarDigital Library
- Xueqi Li, Wenjun Jiang, Weiguang Chen, Jie Wu, and Guojun Wang. 2019. Haes: A new hybrid approach for movie recommendation with elastic serendipity. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM). ACM, 1503--1512.Google ScholarDigital Library
- Xueqi Li, Wenjun Jiang, Weiguang Chen, Jie Wu, Guojun Wang, and Kenli Li. 2020a. Directional and explainable serendipity recommendation. In Proceedings of The Web Conference 2020. ACM, 122--132.Google ScholarDigital Library
- Julian McAuley, Christopher Targett, Qinfeng Shi, and Anton Van Den Hengel. 2015. Image-based recommendations on styles and substitutes. In Proceedings of the 38th ACM International Conference on Research and Development in Information Retrieval (SIGIR). ACM, 43--52.Google ScholarDigital Library
- Lori McCay-Peet and Elaine G Toms. 2010. The process of serendipity in knowledge work. In Proceedings of the 3rd Symposium on Information Interaction in Context (IIix). 377--382.Google ScholarDigital Library
- Robert K Merton and Elinor Barber. 2011. The travels and adventures of serendipity. In The Travels and Adventures of Serendipity. Princeton University Press.Google Scholar
- Wulf-Uwe Meyer, Rainer Reisenzein, and Achim Schützwohl. 1997. Toward a process analysis of emotions: The case of surprise. Motivation and Emotion, Vol. 21, 3 (1997), 251--274.Google ScholarCross Ref
- Gaurav Pandey, Denis Kotkov, and Alexander Semenov. 2018. Recommending serendipitous items using transfer learning. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management (CIKM). ACM, 1771--1774.Google ScholarDigital Library
- Theodore G Remer. 1965. Serendipity and the three princes: From the Peregrinaggio of 1557. Norman, U. Oklahoma P.Google Scholar
- Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian personalized ranking from implicit feedback. In Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence. 452--461.Google ScholarDigital Library
- Fei Sun, Jun Liu, Jian Wu, Changhua Pei, Xiao Lin, Wenwu Ou, and Peng Jiang. 2019. BERT4Rec: Sequential recommendation with bidirectional encoder representations from transformer. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM). ACM, 1441--1450.Google ScholarDigital Library
- Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need., Vol. 30 (2017).Google Scholar
- Liwei Wu, Shuqing Li, Cho-Jui Hsieh, and James Sharpnack. 2020. SSE-PT: Sequential recommendation via personalized transformer. In Proceedings of the 14th ACM Conference on Recommender Systems (RecSys). ACM, 328--337.Google ScholarDigital Library
- Yuanbo Xu, Yongjian Yang, En Wang, Jiayu Han, Fuzhen Zhuang, Zhiwen Yu, and Hui Xiong. 2020. Neural serendipity recommendation: Exploring the balance between accuracy and novelty with sparse explicit feedback. ACM Transactions on Knowledge Discovery from Data (TKDD), Vol. 14, 4 (2020), 1--25.Google ScholarDigital Library
- Mingwei Zhang, Yang Yang, Rizwan Abbas, Ke Deng, Jianxin Li, and Bin Zhang. 2021. SNPR: A Serendipity-Oriented Next POI Recommendation Model. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management (CIKM). ACM, 2568--2577.Google ScholarDigital Library
Index Terms
- Wisdom of Crowds and Fine-Grained Learning for Serendipity Recommendations
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