Recommender systems
Open accessAbstract
Recommender systems assist and augment a natural social process. In a typical recommender system people, provide recommendations as inputs, which tile system then aggregates and directs to appropriate recipients. In some cases, the primary transformation is in the aggregation; in others, the system's value lies in its ability to make good matches between recommenders and those seeking recommendations. This special section includes descriptions of five recommender systems. A sixth article analyzes incentives for provision of recommendations. Recommender systems introduce two interesting incentive problems. First, once one has established a profile of interests, it is easy to free ride by consuming evaluations provided by others. Second, if anyone can provide recommendations, content owners may generate mountains of positive recommendations for their own materials and negative recommendations for their competitors. Recommender systems also raise concerns about personal privacy.
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Based on some kind of input, recommender systems select a set of items from a large pool of available items and recommend a list of information links for users to choose.
“Based on some kind of input, recommender systems select a set of items from a large pool of available items and recommend a list of information links for users to choose (Resnick & Varian, 1997).”
From Do Search Algorithms Endanger Democracy? An Experimental Investigation of Algorithm Effects on Political Polarization (2020) · cites this paper as Resnick & Varian, 1997
The Recommender Systems section says systems take recommendations as inputs, aggregate them, and direct them to recipients; it also describes recommending interesting items. But it does not characterize recommender systems generally as selecting from a large pool and presenting users with a list of information links. It describes links as one possible type of recommendation alongside books, movies, and other items. (LLM, conf=0.84)
Experimental — accuracy not yet measuredModel predictive control, recommender systems, agent-based modeling, or Bayesian network analysis are plausible personalization algorithm strategies.
“Model predictive control [40–43], recommender systems [44], agent-based modeling [45], or Bayesian network analysis [46] are all plausible personalization algorithm strategies.”
From Agile science: creating useful products for behavior change in the real world (2016) · cites this paper as [44]
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Using collaborative filtering to weave an information tapestry
Goldberg, D., Nichols, D., Oki, B. M., & Terry, D. B. (1992) · Communications of the ACM · doi:10.1145/138859.138867 · Cited by 4,071 · see citing works
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Item-based collaborative filtering recommendation algorithms
Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001) · Proceedings of the 10th international conference on World Wide Web · doi:10.1145/371920.372071 · Cited by 9,135 · see citing works
Evaluating collaborative filtering recommender systems
Herlocker, J. L., Konstan, J. A., Terveen, L. G., & Riedl, J. T. (2004) · ACM Transactions on Information Systems · doi:10.1145/963770.963772 · Cited by 5,798 · see citing works
On-line trust: concepts, evolving themes, a model
Corritore, C. L., Kracher, B., & Wiedenbeck, S. (2003) · International Journal of Human-Computer Studies · doi:10.1016/s1071-5819(03)00041-7 · Cited by 1,200 · see citing works
Strong concepts: Intermediate-level knowledge in interaction design research
Höök, K., & Löwgren, J. (2012) · ACM Transactions on Computer-Human Interaction · doi:10.1145/2362364.2362371 · Cited by 540 · see citing works
Making sense of recommendations
Yeomans, M., Shah, A., Mullainathan, S., & Kleinberg, J. (2019) · Journal of Behavioral Decision Making · doi:10.1002/bdm.2118 · Cited by 459 · see citing works
PUMA: A Programmable Ultra-efficient Memristor-based Accelerator for Machine Learning Inference
Ankit, A., Hajj, I. E., Chalamalasetti, S. R., Ndu, G., Foltin, M., Williams, R. S., Faraboschi, P., Hwu, W.-M. W., Strachan, J. P., Roy, K., et al. (2019) · Proceedings of the Twenty-Fourth International Conference on Architectural Support for Programming Languages and Operating Systems · doi:10.1145/3297858.3304049 · Cited by 424 · see citing works
SCHATZMANN, J., WEILHAMMER, K., STUTTLE, M., & YOUNG, S. (2006) · The Knowledge Engineering Review · doi:10.1017/s0269888906000944 · Cited by 336 · see citing works
Credibility: A multidisciplinary framework
Rieh, S. Y., & Danielson, D. R. (2007) · Annual Review of Information Science and Technology · doi:10.1002/aris.2007.1440410114 · Cited by 336 · see citing works
Blockchain-empowered Federated Learning: Challenges, Solutions, and Future Directions
Zhu, J., Cao, J., Saxena, D., Jiang, S., & Ferradi, H. (2023) · ACM Computing Surveys · doi:10.1145/3570953 · Cited by 309 · see citing works
Agile science: creating useful products for behavior change in the real world
Hekler, E. B., Klasnja, P., Riley, W. T., Buman, M. P., Huberty, J., Rivera, D. E., & Martin, C. A. (2016) · Translational Behavioral Medicine · doi:10.1007/s13142-016-0395-7 · Cited by 208 · see citing works
How behavioural sciences can promote truth, autonomy and democratic discourse online
Lorenz-Spreen, P., Lewandowsky, S., Sunstein, C. R., & Hertwig, R. (2020) · Nature Human Behaviour · doi:10.1038/s41562-020-0889-7 · Cited by 223 · see citing works
The Connection Between Popularity Bias, Calibration, and Fairness in Recommendation
Abdollahpouri, H., Mansoury, M., Burke, R., & Mobasher, B. (2020) · Fourteenth ACM Conference on Recommender Systems · doi:10.1145/3383313.3418487 · Cited by 183 · see citing works
How the Design of YouTube Influences User Sense of Agency
Lukoff, K., Lyngs, U., Zade, H., Liao, J. V., Choi, J., Fan, K., Munson, S. A., & Hiniker, A. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445467 · Cited by 190 · see citing works
Advancing Models and Theories for Digital Behavior Change Interventions
Hekler, E. B., Michie, S., Pavel, M., Rivera, D. E., Collins, L. M., Jimison, H. B., Garnett, C., Parral, S., & Spruijt-Metz, D. (2016) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2016.06.013 · Cited by 174 · see citing works
Cho, J., Ahmed, S., Hilbert, M., Liu, B., & Luu, J. (2020) · Journal of Broadcasting & Electronic Media · doi:10.1080/08838151.2020.1757365 · Cited by 164 · see citing works
Moller, A. C., Merchant, G., Conroy, D. E., West, R., Hekler, E., Kugler, K. C., & Michie, S. (2017) · Journal of Behavioral Medicine · doi:10.1007/s10865-016-9818-7 · Cited by 151 · see citing works
Banker, S., & Khetani, S. (2019) · Journal of Public Policy & Marketing · doi:10.1177/0743915619858057 · Cited by 120 · see citing works
Evolution and impact of bias in human and machine learning algorithm interaction
Sun, W., Nasraoui, O., & Shafto, P. (2020) · PLOS ONE · doi:10.1371/journal.pone.0235502 · Cited by 116 · see citing works
A tourism recommender system based on collaboration and text analysis
LOH, S., LORENZI, F., SALDAÑA, R., & LICTHNOW, D. (2003) · Information Technology & Tourism · doi:10.3727/1098305031436980 · Cited by 111 · see citing works
Authorship Attribution with Topic Models
Seroussi, Y., Zukerman, I., & Bohnert, F. (2014) · Computational Linguistics · doi:10.1162/coli_a_00173 · Cited by 89 · see citing works
Ozok, A. A., Fan, Q., & Norcio, A. F. (2010) · Behaviour & Information Technology · doi:10.1080/01449290903004012 · Cited by 60 · see citing works
Wang, Y.-Y., Luse, A., Townsend, A. M., & Mennecke, B. E. (2015) · Information Systems and e-Business Management · doi:10.1007/s10257-014-0269-9 · Cited by 54 · see citing works
Recommender Systems: Do They Have a Viable Business Model in e-Tourism?
Rabanser, U., & Ricci, F. (2005) · Information and Communication Technologies in Tourism 2005 · doi:10.1007/3-211-27283-6_15 · Cited by 52 · see citing works
Piorkowski, D., Fleming, S., Scaffidi, C., Bogart, C., Burnett, M., John, B., Bellamy, R., & Swart, C. (2012) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/2207676.2208608 · Cited by 50 · see citing works
Slade, M., Rennick-Egglestone, S., Llewellyn-Beardsley, J., Yeo, C., Roe, J., Bailey, S., Smith, R. A., Booth, S., Harrison, J., Bhogal, A., et al. (2021) · JMIR Formative Research · doi:10.2196/24417 · Cited by 39 · see citing works
Discovering user communities on the Internet using unsupervised machine learning techniques
Paliouras, G., Papatheodorou, C., Karkaletsis, V, & Spyropoulos, C. D. (2002) · Interacting with Computers · doi:10.1016/s0953-5438(02)00015-2 · Cited by 38 · see citing works
Terra, R., Valente, M. T., Czarnecki, K., & Bigonha, R. S. (2015) · Software: Practice and Experience · doi:10.1002/spe.2228 · Cited by 37 · see citing works
Taylor, S., Korpusik, M., Das, S., Gilhooly, C., Simpson, R., Glass, J., & Roberts, S. (2021) · Journal of Medical Internet Research · doi:10.2196/26988 · Cited by 26 · see citing works
Personalization and probabilities: Impersonal propensities in online grocery shopping
Mackenzie, A. (2018) · Big Data & Society · doi:10.1177/2053951718778310 · Cited by 31 · see citing works
Zhang, X., Ferreira, P., de Matos, M. G., & Belo, R. (2021) · MIS Quarterly · doi:10.25300/misq/2021/14971 · Cited by 29 · see citing works
Dellarocas, C. N. (2001) · SSRN Electronic Journal · doi:10.2139/ssrn.289967 · Cited by 25 · see citing works
Unveiling the impact of managerial traits on investor decision prediction: ANFIS approach
Asemi, A., Asemi, A., & Ko, A. (2023) · Soft Computing · doi:10.1007/s00500-023-08102-2 · Cited by 14 · see citing works
Asemi, A., Asemi, A., & Ko, A. (2023) · Journal of Big Data · doi:10.1186/s40537-023-00784-7 · Cited by 13 · see citing works
A study of dynamic information display and decision-making in abstract trust games
Schaffer, J., O’Donovan, J., Marusich, L., Yu, M., Gonzalez, C., & Höllerer, T. (2018) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2018.01.002 · Cited by 10 · see citing works
- Authors
- Resnick, P., & Varian, H. R.
- Affiliations
- AT&T (United States) (US)
- University of California, Berkeley (US)
- ORCID
- Resnick, Paul
- Venue
- Communications of the ACM
- Published
- 1997-03
- Volume
- 40
- Issue
- 3
- Pages
- 56-58
- ISSN
- 0001-0782 (ISSN-L)
- 1557-7317
- OpenAlex
- W2341865734
- Cited by
- 3,772
- References
- 1
- Citations by year
- 2026: 88 · 2025: 106 · 2024: 134 · 2023: 132 · 2022: 124 · 2021: 178 · 2020: 194 · 2019: 148 · 2018: 160 · 2017: 147 · 2016: 163 · 2015: 157 · 2014: 183 · 2013: 173 · 2012: 194
- FWCI
- 146.04
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Communications of the ACM (publishedVersion)
- UCLA - Biblioteca de Ciencias y Tecnologia Felix Morales Bueno (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Social Sciences › Business, Management and Accounting › Strategy and Management › Digital Platforms and Economics
- Social Sciences › Sociology and Political Science › Digital Marketing and Social Media
- Keywords
- recommender systems, free-riding, content owners, personal privacy, social recommendation
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