research-article

Neural Serendipity Recommendation: Exploring the Balance between Accuracy and Novelty with Sparse Explicit Feedback

Authors:

Hui XiongAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 14, Issue 4

Article No.: 50, Pages 1 - 25

https://doi.org/10.1145/3396607

Published: 16 June 2020 Publication History

Abstract

Recommender systems have been playing an important role in providing personalized information to users. However, there is always a trade-off between accuracy and novelty in recommender systems. Usually, many users are suffering from redundant or inaccurate recommendation results. To this end, in this article, we put efforts into exploring the hidden knowledge of observed ratings to alleviate this recommendation dilemma. Specifically, we utilize some basic concepts to define a concept, Serendipity, which is characterized by high-satisfaction and low-initial-interest. Based on this concept, we propose a two-phase recommendation problem which aims to strike a balance between accuracy and novelty achieved by serendipity prediction and personalized recommendation. Along this line, a Neural Serendipity Recommendation (NSR) method is first developed by combining Muti-Layer Percetron and Matrix Factorization for serendipity prediction. Then, a weighted candidate filtering method is designed for personalized recommendation. Finally, extensive experiments on real-world data demonstrate that NSR can achieve a superior serendipity by a 12% improvement in average while maintaining stable accuracy compared with state-of-the-art methods.

References

[1]

Gediminas Adomavicius and Alexander Tuzhilin. 2015. Context-aware recommender systems. In Recommender Systems Handbook. Springer, 191--226.

[2]

Jesús Bobadilla, Fernando Ortega, Antonio Hernando, and Abraham Gutiérrez. 2013. Recommender systems survey. Knowledge-based Systems 46 (2013), 109--132.

Digital Library

[3]

Jiajun Bu, Xin Shen, Bin Xu, Chun Chen, Xiaofei He, and Deng Cai. 2016. Improving collaborative recommendation via user-item subgroups. IEEE Transactions on Knowledge and Data Engineering 28, 9 (2016), 2363--2375.

Digital Library

[4]

Minmin Chen, Zhixiang Xu, Kilian Weinberger, and Fei Sha. 2012. Marginalized denoising autoencoders for domain adaptation. In Proceedings of the 29th International Conference on Machine Learning (ICML'12). http://icml.cc/2012/papers/416.pdf.

Digital Library

[5]

Peizhe Cheng, Shuaiqiang Wang, Jun Ma, Jiankai Sun, and Hui Xiong. 2017. Learning to recommend accurate and diverse items. In Proceedings of the 26th International Conference on World Wide Web. 183--192.

Digital Library

[6]

Li Deng and Dong Yu. 2014. Deep learning: Methods and applications. Foundations and Trends® in Signal Processing 7, 3--4 (2014), 197--387.

[7]

Anupriya Gogna and Angshul Majumdar. 2017. Balancing accuracy and diversity in recommendations using matrix completion framework. Knowledge-Based Systems 125 (2017), 83--95.

Digital Library

[8]

Carlos A. Gomez-Uribe and Neil Hunt. 2016. The Netflix recommender system: Algorithms, business value, and innovation. ACM Transactions on Management Information Systems 6, 4 (2016), 13.

Digital Library

[9]

Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative adversarial nets. In Proceedings of the Advances in Neural Information Processing Systems. 2672--2680.

Digital Library

[10]

Ian Goodfellow, Yoshua Bengio, and Aaron Courville. 2016. Deep Learning. MIT press.

Digital Library

[11]

Chen He, Denis Parra, and Katrien Verbert. 2016. Interactive recommender systems: A survey of the state of the art and future research challenges and opportunities. Expert Systems with Applications 56 (2016), 9--27.

Digital Library

[12]

Xiangnan He, Hanwang Zhang, Min Yen Kan, and Tat Seng Chua. 2016. Fast matrix factorization for online recommendation with implicit feedback. In Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval. 549--558.

Digital Library

[13]

Xiangnan He, Zhankui He, Xiaoyu Du, and Tat-Seng Chua. 2018. Adversarial personalized ranking for recommendation. In Proceedings of the 41st International ACM SIGIR Conference on Research 8 Development in Information Retrieval. ACM, 355--364.

Digital Library

[14]

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. 173--182.

Digital Library

[15]

Won Seok Hwang, Juan Parc, Sang Wook Kim, Jongwuk Lee, and Dongwon Lee. 2016. “Told you i didn’t like it”: Exploiting uninteresting items for effective collaborative filtering. In Proceedings of theIEEE International Conference on Data Engineering. 349--360.

[16]

Kalervo Järvelin and Jaana Kekäläinen. 2002. Cumulated gain-based evaluation of IR techniques. ACM Transactions on Information Systems 20, 4 (2002), 422--446.

Digital Library

[17]

Marius Kaminskas and Derek Bridge. 2016. Diversity, serendipity, novelty, and coverage: A survey and empirical analysis of beyond-accuracy objectives in recommender systems. ACM Transactions on Interactive Intelligent Systems 7, 1 (2016), 2.

[18]

Denis Kotkov, Jari Veijalainen, and Shuaiqiang Wang. 2016a. Challenges of serendipity in recommender systems. In Proceedings of the 12th International Conference on web Information Systems and Technologies. Volume 2, ISBN 978-989-758-186-1. SCITEPRESS.

[19]

Denis Kotkov, Shuaiqiang Wang, and Jari Veijalainen. 2016b. A survey of serendipity in recommender systems. Knowledge-Based Systems 111 (2016), 180--192.

Digital Library

[20]

Denis Kotkov, Shuaiqiang Wang, and Jari Veijalainen. 2016c. Improving serendipity and accuracy in cross-domain recommender systems. In Proceedings of the International Conference on Web Information Systems and Technologies. Springer, 105--119.

[21]

Dokyun Lee and Kartik Hosanagar. 2016. When do recommender systems work the best?: The moderating effects of product attributes and consumer reviews on recommender performance. In Proceedings of the 25th International Conference on World Wide Web. 85--97.

Digital Library

[22]

Zhaoyi Li, Fei Xiong, Ximeng Wang, Hongshu Chen, and Xi Xiong. 2019. Topological influence-aware recommendation on social networks. Complexity 2019 (2019) 12.

[23]

Yanchi Liu, Chuanren Liu, Bin Liu, Meng Qu, and Hui Xiong. 2016. Unified point-of-interest recommendation with temporal interval assessment. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 1015--1024.

Digital Library

[24]

Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian personalized ranking from implicit feedback. In Proceedings of the Conference on Uncertainty in Artificial Intelligence. 452--461.

[25]

Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl. 2001. Item-based collaborative filtering recommendation algorithms. In Proceedings of the International Conference on World Wide Web. 285--295.

Digital Library

[26]

Donald F. Specht. 1990. Probabilistic neural networks. Neural Networks 3, 1 (1990), 109--118.

Digital Library

[27]

Jing Sun, Yun Xiong, Yangyong Zhu, Junming Liu, Chu Guan, and Hui Xiong. 2015. Multi-source information fusion for personalized restaurant recommendation. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 983--986.

Digital Library

[28]

Hao Wang, Yanmei Fu, Qinyong Wang, Hongzhi Yin, Changying Du, and Hui Xiong. 2017. A location-sentiment-aware recommender system for both home-town and out-of-town users. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 1135--1143.

Digital Library

[29]

Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and Philip S. Yu. 2019. A comprehensive survey on graph neural networks. Arxiv Preprint Arxiv:1901.00596 (2019).

[30]

Lin Xiao, Zhang Min, Zhang Yongfeng, Liu Yiqun, and Ma Shaoping. 2017. Learning and transferring social and item visibilities for personalized recommendation. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. ACM, 337--346.

Digital Library

[31]

Fei Xiong, Ximeng Wang, Shirui Pan, Hong Yang, Haishuai Wang, and Chengqi Zhang. 2018. Social recommendation with evolutionary opinion dynamics. IEEE Transactions on Systems, Man, and Cybernetics: Systems PP, 99 (2018), 1--13.

[32]

Jingwei Xu, Yuan Yao, Hanghang Tong, Xianping Tao, and Jian Lu. 2017. R a P are: A generic strategy for cold-start rating prediction problem. IEEE Transactions on Knowledge and Data Engineering 29, 6 (2017), 1296--1309.

Digital Library

[33]

Yuanbo Xu, Yongjian Yang, Jiayu Han, En Wang, Fuzhen Zhuang, Jingyuan Yang, and Hui Xiong. 2019. NeuO: Exploiting the sentimental bias between ratings and reviews with neural networks. Neural Networks 111 (2019), 77--88.

Digital Library

[34]

Surong Yan, Kwei-Jay Lin, Xiaolin Zheng, Wenyu Zhang, and Xiaoqing Feng. 2017. An approach for building efficient and accurate social recommender systems using individual relationship networks. IEEE Transactions on Knowledge and Data Engineering 29, 10 (2017), 2086--2099.

Digital Library

[35]

Jingyuan Yang, Chuanren Liu, Mingfei Teng, Hui Xiong, March Liao, and Vivian Zhu. 2015. Exploiting temporal and social factors for B2B marketing campaign recommendations. In Proceedings of the IEEE International Conference on Data Mining. 499--508.

Digital Library

[36]

Yongjian Yang, Yuanbo Xu, En Wang, Jiayu Han, and Zhiwen Yu. 2018. Improving existing collaborative filtering recommendations via serendipity-based algorithm. IEEE Transactions on Multimedia 20, 7 (2018), 1888--1900.

[37]

Haochao Ying, Fuzhen Zhuang, Fuzheng Zhang, Yanchi Liu, Guandong Xu, Xing Xie, Hui Xiong, and Jian Wu. 2018. Sequential recommender system based on hierarchical attention networks. In Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI’18). 3926--3932.

Digital Library

[38]

Zhiwen Yu, Miao Tian, Zhu Wang, Bin Guo, and Tao Mei. 2016a. Shop-type recommendation leveraging the data from social media and location-based services. ACM Transactions on Knowledge Discovery from Data 11, 1 (2016), 1.

[39]

Zhiwen Yu, Huang Xu, Zhe Yang, and Bin Guo. 2016b. Personalized travel package with multi-point-of-interest recommendation based on crowdsourced user footprints. IEEE Transactions on Human-Machine Systems 46, 1 (2016), 151--158.

[40]

Hanwang Zhang, Fumin Shen, Wei Liu, Xiangnan He, Huanbo Luan, and Tat-Seng Chua. 2016. Discrete collaborative filtering. In Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 325--334.

Digital Library

[41]

Sheng Zhang, Weihong Wang, James Ford, Fillia Makedon, and Justin Pearlman. 2005. Using singular value decomposition approximation for collaborative filtering. In Proceedings of the 7th IEEE International Conference on E-Commerce Technology. IEEE, 257--264.

Digital Library

[42]

Xiaosong Zhou, Zhan Xu, Xu Sun, and Qingfeng Wang. 2017. A new information theory-based serendipitous algorithm design. In Proceedings of the International Conference on Human Interface and the Management of Information. Springer, 314--327.

[43]

Hengshu Zhu, Hui Xiong, Yong Ge, and Enhong Chen. 2014. Mobile app recommendations with security and privacy awareness. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 951--960.

Digital Library

[44]

Fuzhen Zhuang, Zhiqiang Zhang, Mingda Qian, Chuan Shi, Xing Xie, and Qing He. 2017. Representation learning via Dual-Autoencoder for recommendation. Neural Networks 90 (2017), 83--89.

[45]

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

[46]

Cai-Nicolas Ziegler, Thomas Hornung, Martin Przyjaciel-Zablocki, Sven Gauß, and Georg Lausen. 2014. Music recommenders based on hybrid techniques and serendipity. Web Intelligence and Agent Systems: An International Journal 12, 3 (2014), 235--248.

Cited By

Fu ZNiu XOosterhuis HBast HXiong C(2024)The Art of Asking: Prompting Large Language Models for Serendipity RecommendationsProceedings of the 2024 ACM SIGIR International Conference on Theory of Information Retrieval10.1145/3664190.3672521(157-166)Online publication date: 2-Aug-2024
https://dl.acm.org/doi/10.1145/3664190.3672521
Wei SWang ZAn XLi QXiao HXiao Y(2024)A recommendation model for e-commerce platforms oriented to explicit information compensation and hidden information miningKnowledge-Based Systems10.1016/j.knosys.2023.111359(111359)Online publication date: Jan-2024
https://doi.org/10.1016/j.knosys.2023.111359
Xu YLi TYang YChen WYue L(2024)An adaptive category-aware recommender based on dual knowledge graphsInformation Processing & Management10.1016/j.ipm.2023.10363661:3(103636)Online publication date: May-2024
https://doi.org/10.1016/j.ipm.2023.103636
Show More Cited By

Index Terms

Neural Serendipity Recommendation: Exploring the Balance between Accuracy and Novelty with Sparse Explicit Feedback
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Factorization methods
      2. Neural networks
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

Recommendations

Investigating serendipity in recommender systems based on real user feedback
SAC '18: Proceedings of the 33rd Annual ACM Symposium on Applied Computing

Over the past several years, research in recommender systems has emphasized the importance of serendipity, but there is still no consensus on the definition of this concept and whether serendipitous items should be recommended is still not a well-...
Directional and Explainable Serendipity Recommendation
WWW '20: Proceedings of The Web Conference 2020

Serendipity recommendation has attracted more and more attention in recent years; it is committed to providing recommendations which could not only cater to users’ demands but also broaden their horizons. However, existing approaches usually measure ...
Serendipitous Personalized Ranking for Top-N Recommendation
WI-IAT '12: Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01

Serendipitous recommendation has benefitted both e-retailers and users. It tends to suggest items which are both unexpected and useful to users. These items are not only profitable to the retailers but also surprisingly suitable to consumers' tastes. ...

Comments

Information & Contributors

Information

Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 14, Issue 4

August 2020

316 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3403605

Editors:
Charu Aggarwal
IBM T. J. Watson Research, USA
,
Xindong Wu
Minginglamp Academy of Sciences, China

Issue’s Table of Contents

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 June 2020

Online AM: 07 May 2020

Accepted: 01 April 2020

Revised: 01 October 2019

Received: 01 September 2018

Published in TKDD Volume 14, Issue 4

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed

Funding Sources

China Postdoctoral Science Foundation
Changchun Science and Technology Development Project
National Natural Science Foundation of China

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

19
Total Citations
View Citations
573
Total Downloads

Downloads (Last 12 months)95
Downloads (Last 6 weeks)13

Reflects downloads up to 18 Aug 2024

Other Metrics

View Author Metrics

Citations

Cited By

Fu ZNiu XOosterhuis HBast HXiong C(2024)The Art of Asking: Prompting Large Language Models for Serendipity RecommendationsProceedings of the 2024 ACM SIGIR International Conference on Theory of Information Retrieval10.1145/3664190.3672521(157-166)Online publication date: 2-Aug-2024
https://dl.acm.org/doi/10.1145/3664190.3672521
Wei SWang ZAn XLi QXiao HXiao Y(2024)A recommendation model for e-commerce platforms oriented to explicit information compensation and hidden information miningKnowledge-Based Systems10.1016/j.knosys.2023.111359(111359)Online publication date: Jan-2024
https://doi.org/10.1016/j.knosys.2023.111359
Xu YLi TYang YChen WYue L(2024)An adaptive category-aware recommender based on dual knowledge graphsInformation Processing & Management10.1016/j.ipm.2023.10363661:3(103636)Online publication date: May-2024
https://doi.org/10.1016/j.ipm.2023.103636
Stitini OGarcía-Magariño IKaloun SBencharef O(2023)Towards Ideal and Efficient Recommendation Systems Based on the Five Evaluation Concepts Promoting SerendipityJournal of Advances in Information Technology10.12720/jait.14.4.701-71714:4(701-717)Online publication date: 2023
https://doi.org/10.12720/jait.14.4.701-717
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
Gao CWang SLi SChen JHe XLei WLi BZhang YJiang P(2023)CIRS: Bursting Filter Bubbles by Counterfactual Interactive Recommender SystemACM Transactions on Information Systems10.1145/359487142:1(1-27)Online publication date: 18-Aug-2023
https://dl.acm.org/doi/10.1145/3594871
Fu ZNiu XYu LChen HDuh WHuang HKato MMothe JPoblete B(2023)Wisdom of Crowds and Fine-Grained Learning for Serendipity RecommendationsProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591787(739-748)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591787
Xu YWang EYang YXiong H(2023)GS$^{2}$-RS: A Generative Approach for Alleviating Cold start and Filter bubbles in Recommender SystemsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.3290140(1-14)Online publication date: 2023
https://doi.org/10.1109/TKDE.2023.3290140
Wang EXu YYang YJiang YYang FWu J(2023)Zone-Enhanced Spatio-Temporal Representation Learning for Urban POI RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.324323935:9(9628-9641)Online publication date: 1-Sep-2023
https://doi.org/10.1109/TKDE.2023.3243239
Jeon GKim SLee S(2023)Interactive Feedback Loop with Counterfactual Data Modification for Serendipity in a Recommendation SystemInternational Journal of Human–Computer Interaction10.1080/10447318.2023.2238369(1-17)Online publication date: 2-Aug-2023
https://doi.org/10.1080/10447318.2023.2238369
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View Issue’s Table of Contents