survey

Deep Learning Models for Serendipity Recommendations: A Survey and New Perspectives

Authors:

Mary Lou MaherAuthors Info & Claims

ACM Computing Surveys, Volume 56, Issue 1

Article No.: 19, Pages 1 - 26

https://doi.org/10.1145/3605145

Published: 26 August 2023 Publication History

Abstract

Serendipitous recommendations have emerged as a compelling approach to deliver users with unexpected yet valuable information, contributing to heightened user satisfaction and engagement. This survey presents an investigation of the most recent research in serendipity recommenders, with a specific emphasis on deep learning recommendation models. We categorize these models into three types, distinguishing their integration of the serendipity objective across distinct stages: pre-processing, in-processing, and post-processing. Additionally, we provide a review and summary of the serendipity definition, available ground truth datasets, and evaluation experiments employed in the field. We propose three promising avenues for future exploration: (1) leveraging user reviews to identify and explore serendipity, (2) employing reinforcement learning to construct a model for discerning appropriate timing for serendipitous recommendations, and (3) utilizing cross-domain learning to enhance serendipitous recommendations. With this review, we aim to cultivate a deeper understanding of serendipity in recommender systems and inspire further advancements in this domain.

References

[1]

Shuai Zhang, Lina Yao, Aixin Sun, and Yi Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM Computing Surveys 52, 1 (2019), 1–38.

Digital Library

[2]

Zhe Fu, Li Yu, and Xi Niu. 2022. TRACE: Travel reinforcement recommendation based on location-aware context extraction. ACM Transactions on Knowledge Discovery from Data 16, 4 (2022), 1–22.

Digital Library

[3]

Youfang Leng, Li Yu, and Xi Niu. 2022. Dynamically aggregating individuals’ social influence and interest evolution for group recommendations. Information Sciences 614 (2022), 223–239.

Digital Library

[4]

Xi Niu and Xiangyu Fan. 2019. Deep learning of human information foraging behavior with a search engine. In Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval. 185–192.

Digital Library

[5]

Mukund Deshpande and George Karypis. 2004. Item-based top-n recommendation algorithms. ACM Transactions on Information Systems 22, 1 (2004), 143–177.

Digital Library

[6]

Xi Niu and Ahmad Al-Doulat. 2021. LuckyFind: Leveraging surprise to improve user satisfaction and inspire curiosity in a recommender system. In Proceedings of the 2021 Conference on Human Information Interaction and Retrieval. 163–172.

Digital Library

[7]

Xi Niu. 2018. An adaptive recommender system for computational serendipity. In Proceedings of the 2018 ACM SIGIR International Conference on Theory of Information Retrieval. 215–218.

Digital Library

[8]

Eli Pariser. 2011. The Filter Bubble: How the New Personalized Web Is Changing What We Read and How We Think. Penguin.

Digital Library

[9]

Engin Bozdag. 2013. Bias in algorithmic filtering and personalization. Ethics and Information Technology 15, 3 (2013), 209–227.

Digital Library

[10]

Chris Anderson. 2006. The Long Tail: Why the Future of Business Is Selling Less of More. Hachette Books.

Digital Library

[11]

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. 11–18.

Digital Library

[12]

A. G. Borselli. 1965. Serendipity and the three princes: From the Peregrinaggio of 1557, Theodore G. Remer (Ed.). University of Oklahoma Press, Norman, OK. (The compilation of the work here is regarded as probably by Michele Tramezzino, and translated by Augusto G. Borselli and Theresa L. Borselli.)

[13]

Lori McCay-Peet and Elaine G. Toms. 2017. Researching serendipity in digital information environments. Synthesis Lectures on Information Concepts, Retrieval, and Services 9, 6 (2017), i–91.

[14]

Jonathan L. Herlocker, Joseph A. Konstan, Loren G. Terveen, and John T. Riedl. 2004. Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems 22, 1 (2004), 5–53.

Digital Library

[15]

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. 1341–1350.

Digital Library

[16]

Qingchen Zhang, Laurence T. Yang, Zhikui Chen, and Peng Li. 2018. A survey on deep learning for big data. Information Fusion 42 (2018), 146–157.

[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), 1–42.

Digital Library

[18]

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

Digital Library

[19]

Reza Jafari Ziarani and Reza Ravanmehr. 2021. Serendipity in recommender systems: A systematic literature review. Journal of Computer Science and Technology 36, 2 (2021), 375–396.

Digital Library

[20]

Fakhri Abbas and Xi Niu. 2019. Computational serendipitous recommender system frameworks: A literature survey. In Proceedings of the 2019 IEEE/ACS 16th International Conference on Computer Systems and Applications (AICCSA’19). IEEE, Los Alamitos, CA, 1–8.

[21]

Zhe Fu, Xi Niu, and Li Yu. 2023. Wisdom of crowds and fine-grained learning for serendipity recommendations. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval.

Digital Library

[22]

Ahmad Hassan Afridi. 2018. User control and serendipitous recommendations in learning environments. Procedia Computer Science 130 (2018), 214–221.

Digital Library

[23]

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 17, 3 (2018), 1–22.

Digital Library

[24]

Pan Li, Maofei Que, Zhichao Jiang, Yao Hu, and Alexander Tuzhilin. 2020. PURS: Personalized unexpected recommender system for improving user satisfaction. In Proceedings of the 14th ACM Conference on Recommender Systems. 279–288.

Digital Library

[25]

Satoko Inoue and Masataka Tokumaru. 2020. Serendipity recommender system for academic disciplines. In Proceedings of the 2020 Joint 11th International Conference on Soft Computing and Intelligent Systems and the 21st International Symposium on Advanced Intelligent Systems (SCIS-ISIS’20). IEEE, Los Alamitos, CA, 1–4.

[26]

Ningxia Wang and Li Chen. 2021. User bias in beyond-accuracy measurement of recommendation algorithms. In Proceedings of the 15th ACM Conference on Recommender Systems. 133–142.

Digital Library

[27]

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 and Knowledge Management. 2568–2577.

Digital Library

[28]

Fuzheng Zhang, Kai Zheng, Nicholas Jing Yuan, Xing Xie, Enhong Chen, and Xiaofang Zhou. 2015. A novelty-seeking based dining recommender system. In Proceedings of the 24th International Conference on World Wide Web. 1362–1372.

Digital Library

[29]

Wei Lu, Fu-Lai Chung, Wenhao Jiang, Martin Ester, and Wei Liu. 2018. A deep Bayesian tensor-based system for video recommendation. ACM Transactions on Information Systems 37, 1 (2018), 1–22.

Digital Library

[30]

Shoujin Wang, Liang Hu, Longbing Cao, Xiaoshui Huang, Defu Lian, and Wei Liu. 2018. Attention-based transactional context embedding for next-item recommendation. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 32.

[31]

Ludovik Coba, Panagiotis Symeonidis, and Markus Zanker. 2018. Novelty-aware matrix factorization based on items’ popularity. In Proceedings of the International Conference of the Italian Association for Artificial Intelligence. 516–527.

[32]

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 14, 4 (2020), 1–25.

Digital Library

[33]

Pranita Shrestha, Min Zhang, Yiqun Liu, and Shaoping Ma. 2020. Curiosity-inspired personalized recommendation. In Proceedings of the 2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT’20). IEEE, Los Alamitos, CA, 33–40.

[34]

Kachun Lo and Tsukasa Ishigaki. 2021. PPNW: Personalized pairwise novelty loss weighting for novel recommendation. Knowledge and Information Systems 63, 5 (2021), 1117–1148.

Digital Library

[35]

Jungkyu Han and Hayato Yamana. 2019. Geographic diversification of recommended POIs in frequently visited areas. ACM Transactions on Information Systems 38, 1 (2019), 1–39.

Digital Library

[36]

Xiaoyu Ge, Panos K. Chrysanthis, Konstantinos Pelechrinis, Demetrios Zeinalipour-Yazti, and Mohamed A. Sharaf. 2020. Serendipity-based points-of-interest navigation. ACM Transactions on Internet Technology 20, 4 (2020), 1–32.

Digital Library

[37]

Jianing Sun, Wei Guo, Dengcheng Zhang, Yingxue Zhang, Florence Regol, Yaochen Hu, Huifeng Guo, et al. 2020. A framework for recommending accurate and diverse items using Bayesian graph convolutional neural networks. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2030–2039.

Digital Library

[38]

Zhihua Cui, Xianghua Xu, Fei Xue, Xingjuan Cai, Yang Cao, Wensheng Zhang, and Jinjun Chen. 2020. Personalized recommendation system based on collaborative filtering for IoT scenarios. IEEE Transactions on Services Computing 13, 4 (2020), 685–695.

[39]

Wanyu Chen, Pengjie Ren, Fei Cai, Fei Sun, and Maarten De Rijke. 2021. Multi-interest diversification for end-to-end sequential recommendation. ACM Transactions on Information Systems 40, 1 (2021), 1–30.

Digital Library

[40]

Jongsoo Lee, Jung Ah Shin, and Dong-Kyu Chae. 2022. A diversity personalization approach towards recommending POIs for Jeju island. In Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing. 1804–1807.

Digital Library

[41]

Marco De Gemmis, Pasquale Lops, Giovanni Semeraro, and Cataldo Musto. 2015. An investigation on the serendipity problem in recommender systems. Information Processing & Management 51, 5 (2015), 695–717.

Digital Library

[42]

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 Proceedings of the World Wide Web Conference. 240–250.

Digital Library

[43]

Xi Niu and Fakhri Abbas. 2019. Computational surprise, perceptual surprise, and personal background in text understanding. In Proceedings of the 2019 Conference on Human Information Interaction and Retrieval. 343–347.

Digital Library

[44]

Xi Niu, Wlodek Zadrozny, Kazjon Grace, and Weimao Ke. 2018. Computational surprise in information retrieval. In Proceedings of the 41st International ACM SIGIR Conference on Research and Development in Information Retrieval. 1427–1429.

Digital Library

[45]

Paul Hugh Cleverley and Simon Burnett. 2015. Creating sparks: Comparing search results using discriminatory search term word co-occurrence to facilitate serendipity in the enterprise. Journal of Information & Knowledge Management 14, 01 (2015), 1550007.

[46]

Xi Niu and Fakhri Abbas. 2017. A framework for computational serendipity. In Adjunct Publication of the 25th Conference on User Modeling, Adaptation, and Personalization. 360–363.

Digital Library

[47]

Xi Niu, Fakhri Abbas, Mary Lou Maher, and Kazjon Grace. 2018. Surprise me if you can: Serendipity in health information. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. 1–12.

Digital Library

[48]

Xiangyu Fan and Xi Niu. 2018. Implementing and evaluating serendipity in delivering personalized health information. ACM Transactions on Management Information Systems 9, 2 (2018), 1–19.

Digital Library

[49]

Benard Maake, Tranos Zuva, and Sunday O. Ojo. 2019. A serendipitous research paper recommender system. International Journal of Business and Management Studies 11, 1 (2019), 39–53.

[50]

P. Moreira Gabriel De Souza, Dietmar Jannach, and Adilson Marques Da Cunha. 2019. Contextual hybrid session-based news recommendation with recurrent neural networks. IEEE Access 7 (2019), 169185–169203.

[51]

Jacek Wasilewski and Neil Hurley. 2019. Bayesian personalized ranking for novelty enhancement. In Proceedings of the 27th ACM Conference on User Modeling, Adaptation, and Personalization. 144–148.

Digital Library

[52]

Marwa Hussien Mohamed, Mohamed Helmy Khafagy, Heba Elbeh, and Ahmed Mohamed Abdalla. 2019. Sparsity and cold start recommendation system challenges solved by hybrid feedback. International Journal of Engineering Research and Technology 12, 12 (2019), 2735–2742.

[53]

Yashar Deldjoo, Maurizio Ferrari Dacrema, Mihai Gabriel Constantin, Hamid Eghbal-Zadeh, Stefano Cereda, Markus Schedl, Bogdan Ionescu, and Paolo Cremonesi. 2019. Movie genome: Alleviating new item cold start in movie recommendation. User Modeling and User-Adapted Interaction 29, 2 (2019), 291–343.

Digital Library

[54]

Pramit Mazumdar, Bidyut Kr. Patra, and Korra Sathya Babu. 2020. Cold-start point-of-interest recommendation through crowdsourcing. ACM Transactions on the Web 14, 4 (2020), 1–36.

Digital Library

[55]

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

[56]

Jorge Díez, David Martínez-Rego, Amparo Alonso-Betanzos, Oscar Luaces, and Antonio Bahamonde. 2019. Optimizing novelty and diversity in recommendations. Progress in Artificial Intelligence 8, 1 (2019), 101–109.

[57]

Pablo Castells, Neil J. Hurley, and Saul Vargas. 2015. Novelty and diversity in recommender systems. In Recommender Systems Handbook. Springer, 881–918.

[58]

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. 1503–1512.

Digital Library

[59]

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.

Digital Library

[60]

Ataur Rahman and Max L. Wilson. 2015. Exploring opportunities to facilitate serendipity in search. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. 939–942.

Digital Library

[61]

Tao Wang, Xiaoyu Shi, and Mingsheng Shang. 2020. Diversity-aware top-n recommendation: A deep reinforcement learning way. In Proceedings of the CCF Conference on Big Data. 226–241.

[62]

Shoshana Loeb, Ralph Hill, and Tom Brinck. 1992. Lessons from LyricTime: A prototype multimedia system. ACM SIGCOMM Computer Communication Review 22, 3 (1992), 35–36.

Digital Library

[63]

José Campos and Antonio Dias de Figueiredo. 2002. Programming for Serendipity. AAAI Technical Report FS-02-01. AAAI Press.

[64]

Leo Iaquinta, Marco De Gemmis, Pasquale Lops, Giovanni Semeraro, Michele Filannino, and Piero Molino. 2008. Introducing serendipity in a content-based recommender system. In Proceedings of the 2008 8th International Conference on Hybrid Intelligent Systems. IEEE, Los Alamitos, CA, 168–173.

Digital Library

[65]

Kensuke Onuma, Hanghang Tong, and Christos Faloutsos. 2009. TANGENT: A novel, ‘Surprise me’, recommendation algorithm. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 657–666.

Digital Library

[66]

Yuan Cao Zhang, Diarmuid Ó. Séaghdha, Daniele Quercia, and Tamas Jambor. 2012. Auralist: Introducing serendipity into music recommendation. In Proceedings of the 5th ACM International Conference on Web Search and Data Mining. 13–22.

Digital Library

[67]

Panagiotis Adamopoulos and Alexander Tuzhilin. 2014. On unexpectedness in recommender systems: Or how to better expect the unexpected. ACM Transactions on Intelligent Systems and Technology 5, 4 (2014), 1–32.

Digital Library

[68]

Yejin Kim, Kwangseob Kim, Chanyoung Park, and Hwanjo Yu. 2019. Sequential and diverse recommendation with long tail. In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI’19), Vol. 19. 2740–2746.

[69]

Mengmeng Li, Tian Gan, Meng Liu, Zhiyong Cheng, Jianhua Yin, and Liqiang Nie. 2019. Long-tail hashtag recommendation for micro-videos with graph convolutional network. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management. 509–518.

Digital Library

[70]

Max Welling and Thomas N. Kipf. 2016. Semi-supervised classification with graph convolutional networks. In Proceedings of the International Conference on Learning Representations (ICLR’17).

[71]

Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014. GloVe: Global vectors for word representation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP’14). 1532–1543.

[72]

Kazjon Grace, Mary Lou Maher, Nicholas Davis, and Omar Eltayeby. 2018. Surprise walks: Encouraging users towards novel concepts with sequential suggestions. In Proceedings of the 9th International Conference on Computational Creativity (ICCC’18).

[73]

Younghoon Lee. 2020. Serendipity adjustable application recommendation via joint disentangled recurrent variational auto-encoder. Electronic Commerce Research and Applications 44 (2020), 101017.

[74]

Xueqi Li, Wenjun Jiang, Weiguang Chen, Jie Wu, Guojun Wang, and Kenli Li. 2020. Directional and explainable serendipity recommendation. In Proceedings of The Web Conference 2020. 122–132.

Digital Library

[75]

John Boaz Lee, Ryan A. Rossi, Sungchul Kim, Nesreen K. Ahmed, and Eunyee Koh. 2019. Attention models in graphs: A survey. ACM Transactions on Knowledge Discovery from Data 13, 6 (2019), 1–25.

Digital Library

[76]

Shaina Raza and Chen Ding. 2021. Deep neural network to tradeoff between accuracy and diversity in a news recommender system. In Proceedings of the 2021 IEEE International Conference on Big Data (Big Data’21). IEEE, Los Alamitos, CA, 5246–5256.

[77]

Preksha Nema, Mitesh M. Khapra, Anirban Laha, and Balaraman Ravindran. 2017. Diversity driven attention model for query-based abstractive summarization. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 1063–1072.

[78]

Ruobing Xie, Qi Liu, Shukai Liu, Ziwei Zhang, Peng Cui, Bo Zhang, and Leyu Lin. 2021. Improving accuracy and diversity in matching of recommendation with diversified preference network. IEEE Transactions on Big Data 8, 4 (2021), 955–967.

[79]

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Proceedings of the 31st Conference on Neural Information Processing Systems (NIPS’17).

[80]

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

[81]

Travis Ebesu, Bin Shen, and Yi Fang. 2018. Collaborative memory network for recommendation systems. In Proceedings of the 41st International ACM SIGIR Conference on Research and Development in Information Retrieval. 515–524.

Digital Library

[82]

Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, and Tat-Seng Chua. 2019. Neural graph collaborative filtering. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. 165–174.

Digital Library

[83]

Ruomu Zou. 2019. A deep, forgetful novelty-seeking movie recommender model. arXiv Preprint arXiv:1909.01811 (2019).

[84]

Sindhu Padakandla. 2021. A survey of reinforcement learning algorithms for dynamically varying environments. ACM Computing Surveys 54, 6 (2021), 1–25.

Digital Library

[85]

Guanjie Zheng, Fuzheng Zhang, Zihan Zheng, Yang Xiang, Nicholas Jing Yuan, Xing Xie, and Zhenhui Li. 2018. DRN: A deep reinforcement learning framework for news recommendation. In Proceedings of the 2018 World Wide Web Conference. 167–176.

Digital Library

[86]

Rupert G. Miller Jr. 2011. Survival Analysis. John Wiley & Sons.

[87]

Joseph G. Ibrahim, Ming-Hui Chen, Danjie Zhang, and Debajyoti Sinha. 2013. Bayesian analysis of the Cox model. In Handbook of Survival Analysis, John P. Klein, Hans C. van Houwelingen, Joseph G. Ibrahim, and Thomas H. Scheike (Eds.). Chapman & Hall/CRC Handbooks of Modern Statistical Methods. Chapman & Hall/CRC, Boca Raton, FL, 27–48.

[88]

Keigo Sakurai, Ren Togo, Takahiro Ogawa, and Miki Haseyama. 2020. Music playlist generation based on reinforcement learning using acoustic feature map. In Proceedings of the 2020 IEEE 9th Global Conference on Consumer Electronics (GCCE’20). IEEE, Los Alamitos, CA, 942–943.

[89]

Dusan Stamenkovic, Alexandros Karatzoglou, Ioannis Arapakis, Xin Xin, and Kleomenis Katevas. 2022. Choosing the best of both worlds: Diverse and novel recommendations through multi-objective reinforcement learning. In Proceedings of the 15th ACM International Conference on Web Search and Data Mining. 957–965.

Digital Library

[90]

Himan Abdollahpouri, Robin Burke, and Bamshad Mobasher. 2019. Managing popularity bias in recommender systems with personalized re-ranking. In Proceedings of the 32nd International Florida Artificial Intelligence Research Society Conference (FLAIRS’19).

[91]

Chung-Ming Huang and Chen-Yi Wu. 2021. The point of interest (POI) recommendation for mobile digital culture heritage (M-DCH) based on the behavior analysis using the recurrent neural networks (RNN) and user-collaborative filtering. Journal of Internet Technology 22, 4 (2021), 821–833.

[92]

F. Maxwell Harper and Joseph A. Konstan. 2015. The MovieLens datasets: History and context. ACM Transactions on Interactive Intelligent Systems 5, 4 (2015), 1–19.

Digital Library

[93]

Nabiha Asghar. 2016. Yelp dataset challenge: Review rating prediction. arXiv Preprint arXiv:1605.05362 (2016).

[94]

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. 22–32.

Digital Library

[95]

James Bennett and Stan Lanning. 2007. The Netflix Prize. In Proceedings of the KDD Cup and Workshop, Vol. 2007. ACM, New York, NY, 1–4.

[96]

Jinoh Oh, Sun Park, Hwanjo Yu, Min Song, and Seung-Taek Park. 2011. Novel recommendation based on personal popularity tendency. In Proceedings of the 2011 IEEE 11th International Conference on Data Mining. IEEE, Los Alamitos, CA, 507–516.

Digital Library

[97]

Fuzheng Zhang, Nicholas Jing Yuan, Defu Lian, and Xing Xie. 2014. Mining novelty-seeking trait across heterogeneous domains. In Proceedings of the 23rd International Conference on World Wide Web. 373–384.

Digital Library

[98]

Yong Wang, Jingyuan Wang, and Li Li. 2018. Enhancing long tail recommendation based on user’s experience evolution. In Proceedings of the 2018 IEEE 22nd International Conference on Computer Supported Cooperative Work in Design (CSCWD’18). IEEE, Los Alamitos, CA, 25–30.

[99]

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. 1771–1774.

Digital Library

[100]

Reza Jafari Ziarani and Reza Ravanmehr. 2021. Deep neural network approach for a serendipity-oriented recommendation system. Expert Systems with Applications 185 (2021), 115660.

Digital Library

[101]

Maximilian Jenders, T. Lindhauer, Gjergji Kasneci, Ralf Krestel, and Felix Naumann. 2015. A serendipity model for news recommendation. In KI 2015: Advances in Artificial Intelligence. Lecture Notes in Computer Science, Vol. 9324. Springer, 111–123.

[102]

Ningxia Wang, Li Chen, and Yonghua Yang. 2020. The impacts of item features and user characteristics on users’ perceived serendipity of recommendations. In Proceedings of the 28th ACM Conference on User Modeling, Adaptation, and Personalization. 266–274.

Digital Library

[103]

Todd B. Kashdan, Matthew W. Gallagher, Paul J. Silvia, Beate P. Winterstein, William E. Breen, Daniel Terhar, and Michael F. Steger. 2009. The Curiosity and Exploration Inventory-II: Development, factor structure, and psychometrics. Journal of Research in Personality 43, 6 (2009), 987–998.

[104]

Fuzhen Zhuang, Yingmin Zhou, Fuzheng Zhang, Xiang Ao, Xing Xie, and Qing He. 2017. Sequential transfer learning: Cross-domain novelty seeking trait mining for recommendation. In Proceedings of the 26th International Conference on World Wide Web Companion. 881–882.

Digital Library

[105]

Muhammad Murad Khan, Roliana Ibrahim, and Imran Ghani. 2017. Cross domain recommender systems: A systematic literature review. ACM Computing Surveys 50, 3 (2017), 1–34.

Digital Library

[106]

Jingjing Li, Mengmeng Jing, Ke Lu, Lei Zhu, Yang Yang, and Zi Huang. 2019. From zero-shot learning to cold-start recommendation. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 4189–4196.

Digital Library

[107]

Zhaoxin Huan, Gongduo Zhang, Xiaolu Zhang, Jun Zhou, Qintong Wu, Lihong Gu, Jinjie Gu, Yong He, Yue Zhu, and Linjian Mo. 2022. An industrial framework for cold-start recommendation in zero-shot scenarios. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 3403–3407.

Digital Library

[108]

Fuzhen Zhuang, Zhiyuan Qi, Keyu Duan, Dongbo Xi, Yongchun Zhu, Hengshu Zhu, Hui Xiong, and Qing He. 2020. A comprehensive survey on transfer learning. Proceedings of the IEEE 109, 1 (2020), 43–76.

Cited By

Li JDeng KLi JRen Y(2025)Session-Oriented Fairness-Aware Recommendation via Dual Temporal Convolutional NetworksIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.350945437:2(923-935)Online publication date: 1-Feb-2025
https://dl.acm.org/doi/10.1109/TKDE.2024.3509454
Wang QJin EZhang HChen YYue YDorado DHu ZXu M(2024)Enhancing Personalized Recommendations: A Study on the Efficacy of Multi-Task Learning and Feature IntegrationInformation10.3390/info1506031215:6(312)Online publication date: 27-May-2024
https://doi.org/10.3390/info15060312
Tokutake YOkamoto K(2024)Can Large Language Models Assess Serendipity in Recommender Systems?Journal of Advanced Computational Intelligence and Intelligent Informatics10.20965/jaciii.2024.p126328:6(1263-1272)Online publication date: 20-Nov-2024
https://doi.org/10.20965/jaciii.2024.p1263
Show More Cited By

Index Terms

Deep Learning Models for Serendipity Recommendations: A Survey and New Perspectives
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

Recommendations

A Deep Learning Model for Cross-Domain Serendipity Recommendations
Serendipity means unexpected discoveries that are valuable, with positive outcomes ranging from personal benefits to scientific breakthroughs. This study proposes a cross-domain recommendation model, called SerenCDR, to model serendipity. SerenCDR ...
News Session-Based Recommendations using Deep Neural Networks
DLRS 2018: Proceedings of the 3rd Workshop on Deep Learning for Recommender Systems

News recommender systems are aimed to personalize users experiences and help them to discover relevant articles from a large and dynamic search space. Therefore, news domain is a challenging scenario for recommendations, due to its sparse user profiling, ...
A survey of serendipity in recommender systems

We summarize most efforts on serendipity in recommender systems.We compare definitions of serendipity in recommender systems.We classify the state-of-the-art serendipity-oriented recommendation algorithms.We review methods to assess serendipity in ...

Comments

Information & Contributors

Information

Published In

cover image ACM Computing Surveys

ACM Computing Surveys Volume 56, Issue 1

January 2024

918 pages

EISSN:1557-7341

DOI:10.1145/3613490

Editor:
Albert Zomaya
University of Sydney, Australia

Issue’s Table of Contents

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 the author(s) 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: 26 August 2023

Online AM: 20 June 2023

Accepted: 31 May 2023

Revised: 22 March 2023

Received: 11 September 2022

Published in CSUR Volume 56, Issue 1

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Survey

Funding Sources

National Science Foundation (NSF)

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

8
Total Citations
View Citations
1,671
Total Downloads

Downloads (Last 12 months)951
Downloads (Last 6 weeks)76

Reflects downloads up to 03 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Li JDeng KLi JRen Y(2025)Session-Oriented Fairness-Aware Recommendation via Dual Temporal Convolutional NetworksIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.350945437:2(923-935)Online publication date: 1-Feb-2025
https://dl.acm.org/doi/10.1109/TKDE.2024.3509454
Wang QJin EZhang HChen YYue YDorado DHu ZXu M(2024)Enhancing Personalized Recommendations: A Study on the Efficacy of Multi-Task Learning and Feature IntegrationInformation10.3390/info1506031215:6(312)Online publication date: 27-May-2024
https://doi.org/10.3390/info15060312
Tokutake YOkamoto K(2024)Can Large Language Models Assess Serendipity in Recommender Systems?Journal of Advanced Computational Intelligence and Intelligent Informatics10.20965/jaciii.2024.p126328:6(1263-1272)Online publication date: 20-Nov-2024
https://doi.org/10.20965/jaciii.2024.p1263
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
Li NBan XLing CGao CHu LJiang PGai KLi YLiao QHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Modeling User Fatigue for Sequential RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657802(996-1005)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657802
Kazienko PCambria E(2024)Toward Responsible Recommender SystemsIEEE Intelligent Systems10.1109/MIS.2024.339819039:3(5-12)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.1109/MIS.2024.3398190
Xu ZMatsumura A(2024)A Serendipitous Recommendation System Considering User CuriosityInformation Integration and Web Intelligence10.1007/978-3-031-78093-6_3(33-48)Online publication date: 4-Dec-2024
https://doi.org/10.1007/978-3-031-78093-6_3
Brusseau J(2023)Mapping AI avant-gardes in time: posthumanism, transhumanism, genhumanismDiscover Artificial Intelligence10.1007/s44163-023-00080-63:1Online publication date: 3-Oct-2023
https://doi.org/10.1007/s44163-023-00080-6

View Options

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.

Full Text

View this article in Full Text.

Figures

Tables

Media

View full text|Download PDF

View Issue’s Table of Contents