research-article

On Unexpectedness in Recommender Systems: Or How to Better Expect the Unexpected

Authors:

Panagiotis Adamopoulos,

Alexander TuzhilinAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 5, Issue 4

Article No.: 54, Pages 1 - 32

https://doi.org/10.1145/2559952

Published: 18 December 2014 Publication History

Abstract

Although the broad social and business success of recommender systems has been achieved across several domains, there is still a long way to go in terms of user satisfaction. One of the key dimensions for significant improvement is the concept of unexpectedness. In this article, we propose a method to improve user satisfaction by generating unexpected recommendations based on the utility theory of economics. In particular, we propose a new concept of unexpectedness as recommending to users those items that depart from what they would expect from the system - the consideration set of each user. We define and formalize the concept of unexpectedness and discuss how it differs from the related notions of novelty, serendipity, and diversity. In addition, we suggest several mechanisms for specifying the users’ expectations and propose specific performance metrics to measure the unexpectedness of recommendation lists. We also take into consideration the quality of recommendations using certain utility functions and present an algorithm for providing users with unexpected recommendations of high quality that are hard to discover but fairly match their interests. Finally, we conduct several experiments on “real-world” datasets and compare our recommendation results with other methods. The proposed approach outperforms these baseline methods in terms of unexpectedness and other important metrics, such as coverage, aggregate diversity and dispersion, while avoiding any accuracy loss.

Supplementary Material

a54-adamopoulos-apndx.pdf (adamopoulos.zip)

Supplemental movie, appendix, image and software files for, On Unexpectedness in Recommender Systems: Or How to Better Expect the Unexpected

Download
524.54 KB

References

[1]

Panagiotis Adamopoulos. 2013a. Beyond rating prediction accuracy: On new perspectives in recommender systems. In Proceedings of the 7th ACM Conference on Recommender Systems (RecSys’13). ACM, New York, NY, 4.

Digital Library

[2]

Panagiotis Adamopoulos. 2013b. What Makes a Great MOOC&quest; An interdisciplinary analysis of online course student retention. In Proceedings of the 34th International Conference on Information Systems (ICIS’13).

[3]

Panagiotis Adamopoulos and Alexander Tuzhilin. 2011. On unexpectedness in recommender systems: Or how to expect the unexpected. In DiveRS 2011 ACM RecSys 2011 Workshop on Novelty and Diversity in Recommender Systems (RecSys’11). ACM, New York, NY.

[4]

Panagiotis Adamopoulos and Alexander Tuzhilin. 2013a. Probabilistic Neighborhood Selection in Collaborative Filtering Systems. Working Paper CBA-13-04, New York University. Retrieved from http://hdl.handle.net/2451/31988.

[5]

Panagiotis Adamopoulos and Alexander Tuzhilin. 2013b. Recommendation opportunities: Improving item prediction using weighted percentile methods in collaborative filtering systems. In Proceedings of the 7th ACM Conference on Recommender Systems (RecSys’13). ACM, New York, NY, 4.

Digital Library

[6]

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 (WSDM'14). ACM, New York, NY, 655--660.

Digital Library

[7]

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 (RecSys'14). ACM, New York, NY, 153--160.

Digital Library

[8]

G. Adomavicius and Y. Kwon. 2009. Toward more diverse recommendations: Item re-ranking methods for recommender dystems. In Proceedings of the 19th Workshop on Information Technology and Systems (WITS’09).

[9]

G. Adomavicius and YoungOk Kwon. 2011. Maximizing aggregate recommendation diversity: A graph-theoretic approach. In DiveRS 2011 ACM RecSys 2011 Workshop on Novelty and Diversity in Recommender Systems (RecSys’11). ACM, New York, NY.

[10]

G. Adomavicius and YoungOk Kwon. 2012. Improving aggregate recommendation diversity using ranking-based techniques. IEEE Transactions on Knowledge and Data Engineering 24, 5 (May 2012), 896--911.

Digital Library

[11]

Gediminas Adomavicius and Alexander Tuzhilin. 2005. Toward the next generation of recommender Systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering 17, 6 (June 2005), 734--749.

Digital Library

[12]

Gediminas Adomavicius and Jingjing Zhang. 2012. Impact of data characteristics on recommender systems performance. ACM Transactions on Management of Information Systems 3, 1, Article 3 (April 2012), 17 pages.

Digital Library

[13]

Takayuki Akiyama, Kiyohiro Obara, and Masaaki Tanizaki. 2010. Proposal and evaluation of serendipitous recommendation method using general unexpectedness. In Proceedings of the ACM RecSys Workshop on Practical Use of Recommender Systems, Algorithms and Technologies (PRSAT 2010) (RecSys 2010). ACM, New York, NY, USA. http://ir.ii.uam.es/prsat2010/papers/paper1.pdf.

[14]

Amazon 2012. Amazon.com, Inc. Retrieved from http://www.amazon.com.

[15]

Paul André, Jaime Teevan, and Susan T. Dumais. 2009. From x-rays to Silly Putty via Uranus: Serendipity and its role in web search. In Proceedings of the 27th International Conference on Human Factors in Computing Systems (CHI’09). ACM, New York, NY, 2033--2036.

Digital Library

[16]

J. Yannis Bakos. 1997. Reducing buyer search costs: Implications for electronic marketplaces. Management Science 43, 12 (Dec. 1997), 1676--1692.

Digital Library

[17]

William J. Baumol and Edward A. Ide. 1956. Variety in retailing. Management Science 3, 1 (1956), 93--101. http://www.jstor.org/stable/2627176.

Digital Library

[18]

Robert M. Bell, Jim Bennett, Yehuda Koren, and Chris Volinsky. 2009. The million dollar programming prize. IEEE Spectrum 46, 5 (May 2009), 28--33.

Digital Library

[19]

Gideon Berger and Alexander Tuzhilin. 1998. Discovering unexpected patterns in temporal data using temporal logic. In Temporal Databases: Research and Practice. Etzion O., Jajodia S., and Sripada S. (Eds.), Vol. 1399, Springer-Verlag Berlin Heidelberg, 281--309.

[20]

Michael J. Berry and Gordon Linoff. 1997. Data Mining Techniques: For Marketing, Sales, and Customer Support. John Wiley & Sons, New York, NY.

Digital Library

[21]

Daniel Billsus and Michael J. Pazzani. 2000. User modeling for adaptive news access. User Modeling and User-Adapted Interaction 10, 2--3 (February 2000), 147--180. 1026501525781

Digital Library

[22]

BookCrossing. 2004. BookCrossing, Inc. Retrieved from http://www.bookcrossing.com.

[23]

Erik Brynjolfsson, Yu (Jeffrey) Hu, and Duncan Simester. 2011. Goodbye Pareto Principle, hello long tail: The effect of search costs on the concentration of product sales. Management Science 57, 8 (August 2011), 1373--1386.

Digital Library

[24]

Erik Brynjolfsson, Yu (Jeffrey) Hu, and Michael D. Smith. 2003. Consumer surplus in the digital economy: Estimating the value of increased product variety at online booksellers. Management Science 49, 11 (November 2003), 1580--1596.

Digital Library

[25]

Robin Burke. 2002. Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction 12, 4 (November 2002), 331--370.

Digital Library

[26]

Iván Cantador, Peter Brusilovsky, and Tsvi Kuflik. 2011. 2nd workshop on information heterogeneity and fusion in recommender systems (HetRec 2011). In Proceedings of the 5th ACM Conference on Recommender Systems (RecSys’11). ACM, New York, NY.

Digital Library

[27]

P. Castells, S. Vargas, and J. Wang. 2011. Novelty and diversity metrics for recommender dystems: Choice, discovery and relevance. In International Workshop on Diversity in Document Retrieval (DDR’11) at the 33rd European Conference on Information Retrieval (ECIR’11).

[28]

Òscar Celma and Perfecto Herrera. 2008. A new approach to evaluating novel recommendations. In Proceedings of the 2nd ACM Conference on Recommender Systems (RecSys’08). ACM, New York, NY, 179--186.

Digital Library

[29]

Helmuth Cremer and Jacques-Francois Thisse. 1991. Location models of horizontal differentiation: A special case of vertical differentiation models. Journal of Industrial Economics 39, 4 (1991), 383--390.

[30]

Paolo Cremonesi, Franca Garzotto, Sara Negro, Alessandro Vittorio Papadopoulos, and Roberto Turrin. 2011. Looking for “good” recommendations: A comparative evaluation of recommender systems. In Proceedings of the 13th IFIP TC 13 International Conference on Human-Computer Interaction - Volume Part III (INTERACT’11). Springer-Verlag, Berlin, 152--168.

Digital Library

[31]

R. Davidson and J. G. MacKinnon. 2004. Econometric Theory and Methods. Oxford University Press.

[32]

Daniel Fleder and Kartik Hosanagar. 2009. Blockbuster culture’s next rise or fall: The impact of recommender systems on sales diversity. Management Science 55, 5 (2009), 697--712.

Digital Library

[33]

Mouzhi Ge, Carla Delgado-Battenfeld, and Dietmar Jannach. 2010. Beyond accuracy: Evaluating recommender systems by coverage and serendipity. In Proceedings of the 4th ACM Conference on Recommender Systems (RecSys’10). ACM, New York, NY, 257--260.

Digital Library

[34]

C. Gini. 1909. Concentration and dependency ratios (in Italian). English translation in Rivista di Politica Economica 87 (1909), 769--789.

[35]

D. G. Goldstein and D. C. Goldstein. 2006. Profiting from the long tail. Harvard Business Review 84, 6 (2006), 24--28.

[36]

Google. 2012. Google Books. Retrieved from http://books.google.com.

[37]

Michele Gorgoglione, Umberto Panniello, and Alexander Tuzhilin. 2011. The effect of context-aware recommendations on customer purchasing behavior and trust. In Proceedings of the 5th ACM Conference on Recommender Systems (RecSys’11). ACM, New York, NY, 85--92.

Digital Library

[38]

W. H. Greene. 2012. Econometric Analysis. Prentice Hall.

[39]

GroupLens. 2011. GroupLens Research Group. Retrieved from http://www.grouplens.org.

[40]

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 (January 2004), 5--53.

Digital Library

[41]

Yoshinori Hijikata, Takuya Shimizu, and Shogo Nishida. 2009. Discovery-oriented collaborative filtering for improving user satisfaction. In Proceedings of the 14th International Conference on Intelligent User Interfaces (IUI’09). ACM, New York, NY, 67--76.

Digital Library

[42]

Edgar Hoover. 1985. An Introduction to Regional Economics. A. A. Knopf, New York.

[43]

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 8th International Conference on Hybrid Intelligent Systems (HIS’08). IEEE Computer Society, Washington, DC, 168--173.

Digital Library

[44]

IMDb. 2011. IMDb.com, Inc. Retrieved from http://www.imdb.com.

[45]

ISBNdb.com. 2012. The ISBN Database. Retrieved from http://isbndb.com.

[46]

Noriaki Kawamae. 2010. Serendipitous recommendations via innovators. In Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’10). ACM, New York, NY, 218--225.

Digital Library

[47]

Noriaki Kawamae, Hitoshi Sakano, and Takeshi Yamada. 2009. Personalized recommendation based on the personal innovator degree. In Proceedings of the 3rd ACM Conference on Recommender Systems (RecSys’09). ACM, New York, NY, 329--332.

Digital Library

[48]

M. Khabbaz, M. Xie, and L. V. S. Lakshmanan. 2011. TopRecs: Pushing the envelope on recommender systems. Data Engineering (2011), 61.

[49]

F. M. Khan and V. B. Zubek. 2008. Support vector regression for censored data (SVRc): A novel tool for survival analysis. In Proceedings of the 8th IEEE International Conference on Data Mining (ICDM’08). 863--868.

Digital Library

[50]

Joseph A. Konstan, Sean M. McNee, Cai-Nicolas Ziegler, Roberto Torres, Nishikant Kapoor, and John T. Riedl. 2006. Lessons on applying automated recommender systems to information-seeking tasks. In Proceedings of the 21st National Conference on Artificial Intelligence - Volume 2 (AAAI’06). AAAI Press, Palo Alto, CA, 1630--1633.

Digital Library

[51]

J. A. Konstan and J. T. Riedl. 2012. Recommender systems: From algorithms to user experience. User Modeling and User-Adapted Interaction 22 (2012), 101--123.

Digital Library

[52]

Kleanthis-Nikolaos Kontonasios, Eirini Spyropoulou, and Tijl De Bie. 2012. Knowledge discovery interestingness measures based on unexpectedness. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 2, 5 (2012), 386--399.

Digital Library

[53]

Yehuda Koren. 2010. Factor in the neighbors: Scalable and accurate collaborative filtering. ACM Transactions on Knowledge Discovery Data 4, 1 (January 2010), Article 1, 24 pages.

Digital Library

[54]

Yehuda Koren, Robert Bell, and Chris Volinsky. 2009. Matrix factorization techniques for recommender systems. Computer 42, 8 (August 2009), 30--37.

Digital Library

[55]

Neal Lathia, Stephen Hailes, Licia Capra, and Xavier Amatriain. 2010. Temporal diversity in recommender systems. In Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’10). ACM, New York, NY, 210--217.

Digital Library

[56]

Daniel Lemire and Anna Maclachlan. 2007. Slope one predictors for online rating-based collaborative filtering. CoRR abs/cs/0702144 (2007).

[57]

LibraryThing. 2012. LibraryThing. Retrieved from http://www.librarything.com.

[58]

J. S. Long. 1997. Regression Models for Categorical and Limited Dependent Variables. Vol. 7. Sage Publications, Inc.

[59]

M. O. Lorenz. 1905. Methods of measuring the concentration of wealth. Publications of the American Statistical Association 9, 70 (1905), 209--219.

[60]

Alfred Marshall. 1920. Principles of Economics. Vol. 1. Macmillan and Co., London, UK.

[61]

John F. McDonald and Robert A. Moffitt. 1980. The uses of Tobit analysis. The Review of Economics and Statistics 62, 2 (1980), 318--321.

[62]

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 (CHI EA’06). ACM, New York, NY, 1097--1101.

Digital Library

[63]

David McSherry. 2002. Diversity-conscious retrieval. In Proceedings of the 6th European Conference on Advances in Case-Based Reasoning (ECCBR’02). Springer-Verlag, London, UK, 219--233.

Digital Library

[64]

Tomoko Murakami, Koichiro Mori, and Ryohei Orihara. 2008. Metrics for evaluating the serendipity of recommendation lists. In Proceedings of the 2007 Conference on New Frontiers in Artificial Intelligence (JSAI’07). Springer-Verlag, Berlin, 40--46.

Digital Library

[65]

Makoto Nakatsuji, Yasuhiro Fujiwara, Akimichi Tanaka, Toshio Uchiyama, Ko Fujimura, and Toru Ishida. 2010. Classical music for rock fans&quest;: Novel recommendations for expanding user interests. In Proceedings of the 19th ACM International Conference on Information and Knowledge Management (CIKM’10). ACM, New York, NY, 949--958.

Digital Library

[66]

Netflix. 2012. Netflix, Inc. Retrieved from http://www.netflix.com.

[67]

Damien Neven. 1985. Two stage (perfect) equilibrium in Hotelling’s model. The Journal of Industrial Economics 33, 3 (1985), 317--325.

[68]

Randall J. Olsen. 1978. Note on the uniqueness of the maximum likelihood estimator for the Tobit model. Econometrica 46, 5 (1978), 1211--1215.

[69]

Balaji Padmanabhan and Alexander Tuzhilin. 1998. A belief-driven method for discovering unexpected patterns. In Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining (KDD’98). AAAI Press, Palo Alto, CA, 94--100.

[70]

Balaji Padmanabhan and Alexander Tuzhilin. 2000. Small is beautiful: Discovering the minimal set of unexpected patterns. In Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’00). ACM, New York, NY, 54--63.

Digital Library

[71]

Balaji Padmanabhan and Alexander Tuzhilin. 2006. On characterization and discovery of minimal unexpected patterns in rule discovery. IEEE Transactions on Knowledge and Data Engineering 18, 2 (February 2006), 202--216.

Digital Library

[72]

Umberto Panniello, Alexander Tuzhilin, Michele Gorgoglione, Cosimo Palmisano, and Anto Pedone. 2009. Experimental comparison of pre- vs. post-filtering approaches in context-aware recommender systems. In Proceedings of the 3rd ACM Conference on Recommender Systems (RecSys’09). ACM, New York, NY, 265--268.

Digital Library

[73]

S. Rabe-Hesketh, A. Skrondal, and A. Pickles. 2002. Reliable estimation of generalized linear mixed models using adaptive quadrature. Stata Journal 2, 1 (2002), 1--21.

[74]

Alan Said, Brijnesh J. Jain, Benjamin Kille, and Sahin Albayrak. 2012. Increasing diversity through furthest neighbor-based recommendation. In Proceedings of the WSDM’12 Workshop on Diversity in Document Retrieval (DDR’12).

[75]

Guy Shani and Asela Gunawardana. 2011. Evaluating recommendation systems. Recommender Systems Handbook 12, 19 (2011), 1--41.

[76]

P. K. Shivaswamy, Wei Chu, and M. Jansche. 2007. A support vector approach to censored targets. In Proceedings of the 7th IEEE International Conference on Data Mining (ICDM’07). 655--660.

Digital Library

[77]

A. Silberschatz and A. Tuzhilin. 1996. What makes patterns interesting in knowledge discovery systems. IEEE Transactions on Knowledge and Data Engineering 8, 6 (December 1996), 970--974.

Digital Library

[78]

Kazunari Sugiyama and Min-Yen Kan. 2011. Serendipitous recommendation for scholarly papers considering relations among researchers. In Proceedings of the 11th Annual International ACM/IEEE Joint Conference on Digital Libraries (JCDL’11). ACM, New York, NY, 307--310.

Digital Library

[79]

Jean Tirole. 1988. The Theory of Industrial Organization. MIT Press.

[80]

Akhmed Umyarov and Alexander Tuzhilin. 2011. Using external aggregate ratings for improving individual recommendations. ACM Transactions on the Web 5, 1 (February 2011), Article 3, 40 pages.

Digital Library

[81]

Saúl Vargas and Pablo Castells. 2011. Rank and relevance in novelty and diversity metrics for recommender systems. In Proceedings of the 5th ACM Conference on Recommender Systems (RecSys’11). ACM, New York, NY, 109--116.

Digital Library

[82]

Li-Tung Weng, Yue Xu, Yuefeng Li, and Richi Nayak. 2007. Improving recommendation novelty based on topic taxonomy. In Proceedings of the 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops (WI-IATW’07). IEEE Computer Society, Washington, DC, 115--118.

Digital Library

[83]

Wikipedia. 2012. Wikimedia Foundation, Inc. Retrieved from http://www.wikipedia.org.

[84]

J. M. Wooldridge. 2002. Econometric Analysis of Cross Section and Panel Data. MIT Press.

[85]

WorldCat 2012. OCLC Online Computer Library Center, Inc. Retrieved from http://www.worldcat.org.

[86]

Mi Zhang and Neil Hurley. 2008. Avoiding monotony: Improving the diversity of recommendation lists. In Proceedings of the 2008 ACM Conference on Recommender Systems (RecSys’08). ACM, New York, NY, 123--130.

Digital Library

[87]

Mi Zhang and Neil Hurley. 2009. Novel item recommendation by user profile partitioning. In Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01 (WI-IAT’09). IEEE Computer Society, Washington, DC, 508--515.

Digital Library

[88]

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 (WSDM’12). ACM, New York, NY, 13--22.

Digital Library

[89]

T. Zhou, Z. Kuscsik, J. G. Liu, M. Medo, J. R. Wakeling, and Y. C. Zhang. 2010. Solving the apparent diversity-accuracy dilemma of recommender systems. Proceedings of the National Academy of Sciences 107, 10 (2010), 4511.

[90]

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 (WWW’05). ACM, New York, NY, 22--32.

Digital Library

Cited By

Tseng TLiang WHuang H(2025)Exploring a social-curiosity-based algorithm for group recommender systemsThe Journal of Supercomputing10.1007/s11227-024-06590-z81:1Online publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1007/s11227-024-06590-z
Gupta SMishra A(2025)Design and implementation of adaptive filtering-based recommendation systems for maximizing publisher-side revenueMultimedia Tools and Applications10.1007/s11042-024-20573-5Online publication date: 4-Jan-2025
https://doi.org/10.1007/s11042-024-20573-5
Inoue SAyedoun ETakenouchi HTokumaru M(2024)Leveraging Interactive Evolutionary Computation to Induce Serendipity in Informal LearningMultimodal Technologies and Interaction10.3390/mti81101038:11(103)Online publication date: 12-Nov-2024
https://doi.org/10.3390/mti8110103
Show More Cited By

Index Terms

On Unexpectedness in Recommender Systems: Or How to Better Expect the Unexpected

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-...
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 ...
How does serendipity affect diversity in recommender systems? A serendipity-oriented greedy algorithm
Abstract
Most recommender systems suggest items that are popular among all users and similar to items a user usually consumes. As a result, the user receives recommendations that she/he is already familiar with or would find anyway, leading to low ...

Comments

Information & Contributors

Information

Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 5, Issue 4

Special Sections on Diversity and Discovery in Recommender Systems, Online Advertising and Regular Papers

January 2015

390 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/2699158

Editor:
Huan Liu
Arizona State University, USA

Issue’s Table of Contents

Copyright © 2014 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 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: 18 December 2014

Accepted: 01 November 2013

Revised: 01 August 2013

Received: 01 September 2012

Published in TIST Volume 5, Issue 4

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

147
Total Citations
View Citations
2,034
Total Downloads

Downloads (Last 12 months)186
Downloads (Last 6 weeks)14

Reflects downloads up to 05 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Tseng TLiang WHuang H(2025)Exploring a social-curiosity-based algorithm for group recommender systemsThe Journal of Supercomputing10.1007/s11227-024-06590-z81:1Online publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1007/s11227-024-06590-z
Gupta SMishra A(2025)Design and implementation of adaptive filtering-based recommendation systems for maximizing publisher-side revenueMultimedia Tools and Applications10.1007/s11042-024-20573-5Online publication date: 4-Jan-2025
https://doi.org/10.1007/s11042-024-20573-5
Inoue SAyedoun ETakenouchi HTokumaru M(2024)Leveraging Interactive Evolutionary Computation to Induce Serendipity in Informal LearningMultimodal Technologies and Interaction10.3390/mti81101038:11(103)Online publication date: 12-Nov-2024
https://doi.org/10.3390/mti8110103
Yuan YZhou YChen XXiong QOkere H(2024)Enhancing Recommendation Diversity and Novelty with Bi-LSTM and Mean Shift ClusteringElectronics10.3390/electronics1319384113:19(3841)Online publication date: 28-Sep-2024
https://doi.org/10.3390/electronics13193841
Mohini Thakkar (2024)The Distinction Between Search and Discovery Systems : A Scholarly AnalysisInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology10.32628/CSEIT24105104910:5(622-634)Online publication date: 15-Oct-2024
https://doi.org/10.32628/CSEIT241051049
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
Zhou WLin MXiao MFang L(2024)Higher Precision Is Not Always Better: Search Algorithm and Consumer EngagementManagement Science10.1287/mnsc.2023.00478Online publication date: 28-Oct-2024
https://doi.org/10.1287/mnsc.2023.00478
Adamopoulos P(2024)The Spillover Effect of Fraudulent Reviews on Product RecommendationsManagement Science10.1287/mnsc.2021.0261270:12(8818-8832)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1287/mnsc.2021.02612
Li PTuzhilin A(2024)When Variety Seeking Meets UnexpectednessInformation Systems Research10.1287/isre.2021.005335:3(1257-1273)Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1287/isre.2021.0053
Wang YWu JWu ZAdomavicius G(2024)Efficient and Flexible Long-Tail Recommendation Using Cosine PatternsINFORMS Journal on Computing10.1287/ijoc.2022.0194Online publication date: 10-May-2024
https://doi.org/10.1287/ijoc.2022.0194
Show More Cited By

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.

Figures

Tables

Media

View Issue’s Table of Contents