research-article

PURS: Personalized Unexpected Recommender System for Improving User Satisfaction

Authors:

Alexander TuzhilinAuthors Info & Claims

RecSys '20: Proceedings of the 14th ACM Conference on Recommender Systems

Pages 279 - 288

https://doi.org/10.1145/3383313.3412238

Published: 22 September 2020 Publication History

Abstract

Classical recommender system methods typically face the filter bubble problem when users only receive recommendations of their familiar items, making them bored and dissatisfied. To address the filter bubble problem, unexpected recommendations have been proposed to recommend items significantly deviating from user’s prior expectations and thus surprising them by presenting ”fresh” and previously unexplored items to the users. In this paper, we describe a novel Personalized Unexpected Recommender System (PURS) model that incorporates unexpectedness into the recommendation process by providing multi-cluster modeling of user interests in the latent space and personalized unexpectedness via the self-attention mechanism and via selection of an appropriate unexpected activation function. Extensive offline experiments on three real-world datasets illustrate that the proposed PURS model significantly outperforms the state-of-the-art baseline approaches in terms of both accuracy and unexpectedness measures. In addition, we conduct an online A/B test at a major video platform Alibaba-Youku, where our model achieves over 3% increase in the average video view per user metric. The proposed model is in the process of being deployed by the company.

References

[1]

Panagiotis Adamopoulos. 2014. On discovering non-obvious recommendations: Using unexpectedness and neighborhood selection methods in collaborative filtering systems. In Proceedings of the 7th ACM international conference on Web search and data mining. ACM, 655–660.

Digital Library

[2]

Panagiotis Adamopoulos and Alexander Tuzhilin. 2014. On over-specialization and concentration bias of recommendations: Probabilistic neighborhood selection in collaborative filtering systems. In Proceedings of the 8th ACM Conference on Recommender systems. ACM, 153–160.

Digital Library

[3]

Panagiotis Adamopoulos and Alexander Tuzhilin. 2015. On unexpectedness in recommender systems: Or how to better expect the unexpected. ACM Transactions on Intelligent Systems and Technology (TIST) 5, 4(2015), 54.

[4]

Gediminas Adomavicius and YoungOk Kwon. [n.d.]. Overcoming accuracy-diversity tradeoff in recommender systems: a variance-based approach. Citeseer.

[5]

Valerii V Buldygin and Yu V Kozachenko. 1980. Sub-Gaussian random variables. Ukrainian Mathematical Journal 32, 6 (1980), 483–489.

[6]

Li Chen, Yonghua Yang, Ningxia Wang, Keping Yang, and Quan Yuan. 2019. How Serendipity Improves User Satisfaction with Recommendations? A Large-Scale User Evaluation. In The World Wide Web Conference. ACM, 240–250.

Digital Library

[7]

Laming Chen, Guoxin Zhang, and Eric Zhou. 2018. Fast greedy MAP inference for Determinantal Point Process to improve recommendation diversity. In Advances in Neural Information Processing Systems. 5622–5633.

[8]

Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado, Wei Chai, Mustafa Ispir, 2016. Wide & deep learning for recommender systems. In Proceedings of the 1st workshop on deep learning for recommender systems. ACM, 7–10.

Digital Library

[9]

Yizong Cheng. 1995. Mean shift, mode seeking, and clustering. IEEE transactions on pattern analysis and machine intelligence 17, 8(1995), 790–799.

[10]

Kyunghyun Cho, Bart Van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078(2014).

[11]

Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555(2014).

[12]

Dorin Comaniciu and Peter Meer. 2002. Mean shift: A robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis & Machine Intelligence5 (2002), 603–619.

[13]

Philip J Davis. 1959. Leonhard Euler’s integral: A historical profile of the Gamma function: In memoriam: Milton Abramowitz. The American Mathematical Monthly 66, 10 (1959), 849–869.

[14]

Yuxiao Dong, Nitesh V Chawla, and Ananthram Swami. 2017. metapath2vec: Scalable representation learning for heterogeneous networks. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 135–144.

Digital Library

[15]

Yufei Feng, Fuyu Lv, Weichen Shen, Menghan Wang, Fei Sun, Yu Zhu, and Keping Yang. 2019. Deep session interest network for click-through rate prediction. arXiv preprint arXiv:1905.06482(2019).

[16]

Mike Gartrell, Ulrich Paquet, and Noam Koenigstein. 2017. Low-rank factorization of determinantal point processes. In Thirty-First AAAI Conference on Artificial Intelligence.

[17]

Mouzhi Ge, Carla Delgado-Battenfeld, and Dietmar Jannach. 2010. Beyond accuracy: evaluating recommender systems by coverage and serendipity. In Proceedings of the fourth ACM conference on Recommender systems. ACM, 257–260.

Digital Library

[18]

Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He. 2017. DeepFM: a factorization-machine based neural network for CTR prediction. arXiv preprint arXiv:1703.04247(2017).

[19]

Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural collaborative filtering. In Proceedings of the 26th international conference on world wide web. International World Wide Web Conferences Steering Committee, 173–182.

Digital Library

[20]

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) 22, 1 (2004), 5–53.

Digital Library

[21]

Geoffrey E Hinton and Ruslan R Salakhutdinov. 2006. Reducing the dimensionality of data with neural networks. science 313, 5786 (2006), 504–507.

[22]

Leo Iaquinta, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro, and Piero Molino. 2010. Can a recommender system induce serendipitous encounters?In E-commerce. InTech.

[23]

Sergey Ioffe and Christian Szegedy. 2015. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In International conference on machine learning. 448–456.

Digital Library

[24]

Komal Kapoor, Vikas Kumar, Loren Terveen, Joseph A Konstan, and Paul Schrater. 2015. I like to explore sometimes: Adapting to dynamic user novelty preferences. In Proceedings of the 9th ACM Conference on Recommender Systems. ACM, 19–26.

Digital Library

[25]

Komal Kapoor, Karthik Subbian, Jaideep Srivastava, and Paul Schrater. 2015. Just in time recommendations: Modeling the dynamics of boredom in activity streams. In Proceedings of the Eighth ACM International Conference on Web Search and Data Mining. ACM, 233–242.

Digital Library

[26]

Pan Li and Alexander Tuzhilin. 2019. Latent Modeling of Unexpectedness for Recommendations. Proceedings of ACM RecSys 2019 Late-breaking Results (2019), 7–10.

[27]

Pan Li and Alexander Tuzhilin. 2020. Latent Unexpected Recommendations. Forthcoming at ACM Transactions on Intelligent Systems and Technology (TIST) (2020).

[28]

Qiuxia Lu, Tianqi Chen, Weinan Zhang, Diyi Yang, and Yong Yu. 2012. Serendipitous personalized ranking for top-n recommendation. In Web Intelligence and Intelligent Agent Technology (WI-IAT), 2012 IEEE/WIC/ACM International Conferences on, Vol. 1. IEEE, 258–265.

[29]

Malte Ludewig and Dietmar Jannach. 2018. Evaluation of session-based recommendation algorithms. User Modeling and User-Adapted Interaction 28, 4-5 (2018), 331–390.

Digital Library

[30]

Leigh McAlister and Edgar Pessemier. 1982. Variety seeking behavior: An interdisciplinary review. Journal of Consumer research 9, 3 (1982), 311–322.

[31]

Sean M McNee, John Riedl, and Joseph A Konstan. 2006. Being accurate is not enough: how accuracy metrics have hurt recommender systems. In CHI’06 extended abstracts on Human factors in computing systems. 1097–1101.

[32]

Tomoko Murakami, Koichiro Mori, and Ryohei Orihara. 2007. Metrics for evaluating the serendipity of recommendation lists. In Annual conference of the Japanese society for artificial intelligence. Springer, 40–46.

[33]

Tien T Nguyen, Pik-Mai Hui, F Maxwell Harper, Loren Terveen, and Joseph A Konstan. 2014. Exploring the filter bubble: the effect of using recommender systems on content diversity. In Proceedings of the 23rd international conference on World wide web. ACM, 677–686.

Digital Library

[34]

Eli Pariser. 2011. The filter bubble: How the new personalized web is changing what we read and how we think. Penguin.

Digital Library

[35]

Yoon-Joo Park and Alexander Tuzhilin. 2008. The long tail of recommender systems and how to leverage it. In Proceedings of the 2008 ACM conference on Recommender systems. ACM, 11–18.

Digital Library

[36]

Yanru Qu, Han Cai, Kan Ren, Weinan Zhang, Yong Yu, Ying Wen, and Jun Wang. 2016. Product-based neural networks for user response prediction. In 2016 IEEE 16th International Conference on Data Mining (ICDM). IEEE, 1149–1154.

[37]

David E Rumelhart, Geoffrey E Hinton, and Ronald J Williams. 1985. Learning internal representations by error propagation. Technical Report. California Univ San Diego La Jolla Inst for Cognitive Science.

[38]

Suvash Sedhain, Aditya Krishna Menon, Scott Sanner, and Lexing Xie. 2015. Autorec: Autoencoders meet collaborative filtering. In Proceedings of the 24th International Conference on World Wide Web. ACM, 111–112.

Digital Library

[39]

Guy Shani and Asela Gunawardana. 2011. Evaluating recommendation systems. In Recommender systems handbook. Springer, 257–297.

[40]

Peter Shaw, Jakob Uszkoreit, and Ashish Vaswani. 2018. Self-attention with relative position representations. arXiv preprint arXiv:1803.02155(2018).

[41]

Chuan Shi, Binbin Hu, Xin Zhao, and Philip Yu. 2018. Heterogeneous Information Network Embedding for Recommendation. IEEE Transactions on Knowledge and Data Engineering (2018).

[42]

Chuan Shi, Yitong Li, Jiawei Zhang, Yizhou Sun, and S Yu Philip. 2017. A survey of heterogeneous information network analysis. IEEE Transactions on Knowledge and Data Engineering 29, 1(2017), 17–37.

Digital Library

[43]

Jasper Snoek, Hugo Larochelle, and Ryan P Adams. 2012. Practical bayesian optimization of machine learning algorithms. In Advances in neural information processing systems. 2951–2959.

[44]

Shoujin Wang, Longbing Cao, and Yan Wang. 2019. A survey on session-based recommender systems. arXiv preprint arXiv:1902.04864(2019).

[45]

Shuai Zhang, Lina Yao, Aixin Sun, and Yi Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM Computing Surveys (CSUR) 52, 1 (2019), 5.

Digital Library

[46]

Yuan Cao Zhang, Diarmuid Ó Séaghdha, Daniele Quercia, and Tamas Jambor. 2012. Auralist: introducing serendipity into music recommendation. In Proceedings of the fifth ACM international conference on Web search and data mining. ACM, 13–22.

Digital Library

[47]

Qianru Zheng, Chi-Kong Chan, and Horace HS Ip. 2015. An unexpectedness-augmented utility model for making serendipitous recommendation. In Industrial Conference on Data Mining. Springer, 216–230.

[48]

Guorui Zhou, Na Mou, Ying Fan, Qi Pi, Weijie Bian, Chang Zhou, Xiaoqiang Zhu, and Kun Gai. 2019. Deep interest evolution network for click-through rate prediction. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 5941–5948.

Digital Library

[49]

Guorui Zhou, Xiaoqiang Zhu, Chenru Song, Ying Fan, Han Zhu, Xiao Ma, Yanghui Yan, Junqi Jin, Han Li, and Kun Gai. 2018. Deep interest network for click-through rate prediction. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, 1059–1068.

Digital Library

[50]

Tao Zhou, Zoltán Kuscsik, Jian-Guo Liu, Matúš Medo, Joseph Rushton Wakeling, and Yi-Cheng Zhang. 2010. Solving the apparent diversity-accuracy dilemma of recommender systems. Proceedings of the National Academy of Sciences 107, 10(2010), 4511–4515.

[51]

Cai-Nicolas Ziegler, Sean M McNee, Joseph A Konstan, and Georg Lausen. 2005. Improving recommendation lists through topic diversification. In Proceedings of the 14th international conference on World Wide Web. ACM, 22–32.

Digital Library

Cited By

Fu ZNiu XWu XRahman R(2024)A Deep Learning Model for Cross-Domain Serendipity RecommendationsACM Transactions on Recommender Systems10.1145/3690654Online publication date: 29-Aug-2024
https://doi.org/10.1145/3690654
Wang JKaratzoglou AArapakis IXin XGe XJose JSerra ESpezzano F(2024)Sparks of Surprise: Multi-objective Recommendations with Hierarchical Decision Transformers for Diversity, Novelty, and SerendipityProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679533(2358-2368)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679533
Wang YZhang DWulamu A(2024)A Multi-User-Multi-Scenario-Multi-Mode aware network for personalized recommender systemsEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.108169133(108169)Online publication date: Jul-2024
https://doi.org/10.1016/j.engappai.2024.108169
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PURS: Personalized Unexpected Recommender System for Improving User Satisfaction
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals

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cover image ACM Conferences

RecSys '20: Proceedings of the 14th ACM Conference on Recommender Systems

September 2020

796 pages

ISBN:9781450375832

DOI:10.1145/3383313

Copyright © 2020 ACM.

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Published: 22 September 2020

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RecSys '20: Fourteenth ACM Conference on Recommender Systems

September 22 - 26, 2020

Virtual Event, Brazil

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Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Cited By

Fu ZNiu XWu XRahman R(2024)A Deep Learning Model for Cross-Domain Serendipity RecommendationsACM Transactions on Recommender Systems10.1145/3690654Online publication date: 29-Aug-2024
https://doi.org/10.1145/3690654
Wang JKaratzoglou AArapakis IXin XGe XJose JSerra ESpezzano F(2024)Sparks of Surprise: Multi-objective Recommendations with Hierarchical Decision Transformers for Diversity, Novelty, and SerendipityProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679533(2358-2368)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679533
Wang YZhang DWulamu A(2024)A Multi-User-Multi-Scenario-Multi-Mode aware network for personalized recommender systemsEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.108169133(108169)Online publication date: Jul-2024
https://doi.org/10.1016/j.engappai.2024.108169
Li PTuzhilin A(2023)When Variety-Seeking Meets Unexpectedness: Incorporating Variety-Seeking Behaviors into Design of Unexpected Recommender SystemsSSRN Electronic Journal10.2139/ssrn.4554781Online publication date: 2023
https://doi.org/10.2139/ssrn.4554781
Li RGhose AXu KLi B(2023)Predicting Consumer In-Store Purchase Using Real-Time Retail Video AnalyticsSSRN Electronic Journal10.2139/ssrn.4513385Online publication date: 2023
https://doi.org/10.2139/ssrn.4513385
Fu ZNiu X(2023)Modeling Users’ Curiosity in Recommender SystemsACM Transactions on Knowledge Discovery from Data10.1145/361759818:1(1-23)Online publication date: 16-Oct-2023
https://dl.acm.org/doi/10.1145/3617598
Fu ZNiu XMaher M(2023)Deep Learning Models for Serendipity Recommendations: A Survey and New PerspectivesACM Computing Surveys10.1145/360514556:1(1-26)Online publication date: 20-Jun-2023
https://dl.acm.org/doi/10.1145/3605145
Wang ZZou YDai AHou LQiao NZou LMa MDing ZXu S(2023)An Industrial Framework for Personalized Serendipitous Recommendation in E-commerceProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3610234(1015-1018)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3610234
Hasan TBunescu R(2023)Topic-Level Bayesian Surprise and Serendipity for Recommender SystemsProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608851(933-939)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3608851
Pan MShi WZhou KLi ZWang DDing ZZhao XXu SFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Satisfaction-Aware User Interest Network for Click-Through Rate PredictionProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615288(4234-4238)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3615288
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