Abstract
Most choices people make are about ‘matters of taste’, on which there is no universal, objective truth. Nevertheless, people can learn from the experiences of individuals with similar tastes who have already evaluated the available options—a potential harnessed by recommender systems. We mapped recommender system algorithms to models of human judgement and decision-making about ‘matters of fact’ and recast the latter as social learning strategies for matters of taste. Using computer simulations on a large-scale, empirical dataset, we studied how people could leverage the experiences of others to make better decisions. Our simulations showed that experienced individuals can benefit from relying mostly on the opinions of seemingly similar people; by contrast, inexperienced individuals cannot reliably estimate similarity and are better off picking the mainstream option despite differences in taste. Crucially, the level of experience beyond which people should switch to similarity-heavy strategies varies substantially across individuals and depends on how mainstream (or alternative) an individual’s tastes are and the level of dispersion in taste similarity with the other people in the group.
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Change history
22 August 2018
The version of the Supplementary Information file that was originally published with this Article was not the latest version provided by the authors. In the captions of Supplementary Figs. 2 and 8, the median standard error values were reported to be 0.0028 in both cases; instead, in both instances, the values should have been 0.0015. These have now been updated and the Supplementary Information file replaced.
References
Resnick, P. & Varian, H. R. Recommender systems. Commun. ACM 40, 56–58 (1997).
Adomavicius, G. & Tuzhilin, A. Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17, 734–749 (2005).
Herlocker, J. L., Konstan, J. A., Borchers, A. & Riedl, J. An algorithmic framework for performing collaborative filtering. In Proc. 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 230–237 (ACM, 1999).
Brock, T. C. Communicator–recipient similarity and decision change. J. Pers. Soc. Psychol. 1, 650–654 (1965).
Simons, H. W., Berkowitz, N. N. & Moyer, R. J. Similarity, credibility, and attitude change: a review and a theory. Psychol. Bull. 73, 1–16 (1970).
Cohen, J. B. & Golden, E. Informational social influence and product evaluation. J. Appl. Psychol. 56, 54–59 (1972).
Burnkrant, R. E. & Cousineau, A. Informational and normative social influence in buyer behavior. J. Consum. Res. 2, 206–215 (1975).
Jussim, L. & Osgood, D. W. Influence and similarity among friends: an integrative model applied to incarcerated adolescents. Soc. Psychol. Q. 52, 98–112 (1989).
Fawcett, C. A. & Markson, L. Children reason about shared preferences. Dev. Psychol. 46, 299–309 (2010).
Eggleston, C. M., Wilson, T. D., Lee, M. & Gilbert, D. T. Predicting what we will like: asking a stranger can be as good as asking a friend. Organ. Behav. Hum. Decis. Process. 128, 1–10 (2015).
Yaniv, I., Choshen-Hillel, S. & Milyavsky, M. Receiving advice on matters of taste: similarity, majority influence, and taste discrimination. Organ. Behav. Hum. Decis. Process. 115, 111–120 (2011).
Müller-Trede, J., Choshen-Hillel, S., Barneron, M. & Yaniv, I. The wisdom of crowds in matters of taste. Manag. Sci. 64, 1779–1803 (2017).
Gershman, S., Hillard, T. P. & Gweon, H. Learning the structure of social influence. Cogn. Sci. 41, 545–575 (2017).
Clemen, R. T. Combining forecasts: a review and annotated bibliography. Int. J. Forecast. 5, 559–583 (1989).
Hastie, R. & Kameda, T. The robust beauty of majority rules in group decisions. Psychol. Rev. 112, 494–508 (2005).
Goldstone, R. L., Wisdom, T. N., Roberts, M. E. & Frey, S. Learning along with others. Psychol. Learn. Motiv. 58, 1–45 (2013).
Rendell, L. et al. Cognitive culture: theoretical and empirical insights into social learning strategies. Trends Cogn. Sci. 15, 68–76 (2011).
Larrick, R. P., Mannes, A. E. & Soll, J. B. in Frontiers in Social Psychology: Social Judgment and Decision Making (ed. Krueger, J. I.) 227–242 (Psychology Press, New York, NY, 2012).
Ellison, G. & Fudenberg, D. Rules of thumb for social learning. J. Polit. Econ. 101, 612–643 (1993).
Laland, K. N. Social learning strategies. Anim. Learn. Behav. 32, 4–14 (2004).
Boyd, R. & Richerson, P. J. Culture and the Evolutionary Process (Univ. Chicago Press, Chicago, IL, 1985).
Nosofsky, R. M. Similarity scaling and cognitive process models. Annu. Rev. Psychol. 43, 25–53 (1992).
Goldstone, R. L. & Son, J. Y. Similarity (Cambridge Univ. Press, Cambridge, 2005).
Desrosiers, C. & Karypis, G. in Recommender Systems Handbook (eds Ricci, F. et al.) 107–144 (Springer, New York, NY, 2011).
Mannes, A. E., Soll, J. B. & Larrick, R. P. The wisdom of select crowds. J. Pers. Soc. Psychol. 107, 276–299 (2014).
Winkler, R. L. & Makridakis, S. The combination of forecasts. J. R. Stat. Soc. Ser. A 146, 150–157 (1983).
Gigerenzer, G. & Todd, P. M. Simple Heuristics that Make Us Smart (Oxford Univ. Press, New York, NY, 1999).
Davis-Stober, C. P., Dana, J. & Budescu, D. V. Why recognition is rational: optimality results on single-variable decision rules. Judgm. Decis. Mak. 5, 216–229 (2010).
Şimşek, Ö. & Buckmann, M. Learning from small samples: an analysis of simple decision heuristics. In Advances in Neural Information Processing Systems 28 (NIPS 2015) (eds Cortes, C. et al.) 3141–3149 (Curran Associates, 2015).
Hogarth, R. M. & Karelaia, N. Ignoring information in binary choice with continuous variables: when is less “more”? J. Math. Psychol. 49, 115–124 (2005).
Gigerenzer, G. & Brighton, H. Homo heuristicus: why biased minds make better inferences. Top. Cogn. Sci. 1, 107–143 (2009).
Hastie, T., Tibshirani, R. J. & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction (2nd edn.) (Springer, New York, NY, 2009).
Şimşek, Ö. Linear decision rule as aspiration for simple decision heuristics. In Advances in Neural Information Processing Systems 26 (NIPS 2013) (eds Burges, C. J. C. et al.) 2904–2912 (Curran Associates, 2013).
Einhorn, H. J., Hogarth, R. M. & Klempner, E. Quality of group judgment. Psychol. Bull. 84, 158–172 (1977).
Davis-Stober, C. P., Budescu, D. V., Dana, J. & Broomell, S. B. When is a crowd wise? Decision 1, 79–101 (2014).
Goldstein, D. G., McAfee, R. P. & Suri, S. The wisdom of smaller, smarter crowds. In Proc. 15th ACM Conference on Economics and Computation 471–488 (ACM, 2014).
Mellers, B. et al. Psychological strategies for winning a geopolitical forecasting tournament. Psychol. Sci. 25, 1106–1115 (2014).
Ekstrand, M. D., Riedl, J. T. & Konstan, J. A. Collaborative filtering recommender systems. Found. Trends Hum. Comput. Interact. 4, 81–173 (2011).
Dawes, R. M. The robust beauty of improper linear models in decision making. Am. Psychol. 34, 571–582 (1979).
Dana, J. & Dawes, R. M. The superiority of simple alternatives to regression for social science predictions. J. Educ. Behav. Stat. 29, 317–331 (2004).
Einhorn, H. J. & Hogarth, R. M. Unit weighting schemes for decision making. Organ. Behav. Hum. Perform. 13, 171–192 (1975).
Katsikopoulos, K. V. Psychological heuristics for making inferences: definition, performance, and the emerging theory and practice. Decis. Anal. 8, 10–29 (2011).
Goldberg, K., Roeder, T., Gupta, D. & Perkins, C. Eigentaste: a constant time collaborative filtering algorithm. Inf. Retr. 4, 133–151 (2001).
Goldstone, R. L. & Lupyan, G. Discovering psychological principles by mining naturally occurring data sets. Top. Cogn. Sci. 8, 548–568 (2016).
Paxton, A. & Griffiths, T. L. Finding the traces of behavioral and cognitive processes in big data and naturally occurring datasets. Behav. Res. Methods 49, 1630–1638 (2017).
Dunbar, R. How Many Friends Does One Person Need? Dunbar’s Number and Other Evolutionary Quirks (Faber & Faber, London, 2010).
Hume, D. Of the standard of taste. In Four Dissertations (A. Millar in the Strand, London, 1757).
Geman, S., Bienenstock, E. & Doursat, R. Neural networks and the bias/variance dilemma. Neural Comput. 4, 1–58 (1992).
Briscoe, E. & Feldman, J. Conceptual complexity and the bias/variance tradeoff. Cognition 118, 2–16 (2011).
Geurts, P. in Data Mining and Knowledge Discovery Handbook (eds Maimon, O. & Rokach, L.) 733–746 (Springer, New York, NY, 2010).
Arlot, S. & Celisse, A. A survey of cross-validation procedures for model selection. Stat. Surv. 4, 40–79 (2010).
Vapnik, V. N. The Nature of Statistical Learning Theory (Springer, New York, NY, 1995).
Akaike, H. in Selected Papers of Hirotugu Akaike (eds Parzen, E., Tanabe, K. & Kitagawa, G.) 199–213 (Springer, New York, NY, 1998).
Schwarz, G. Estimating the dimension of a model. Ann. Stat. 6, 461–464 (1978).
Rissanen, J. Modeling by shortest data description. Automatica 14, 465–471 (1978).
Pitt, M. A. & Myung, I. J. When a good fit can be bad. Trends Cogn. Sci. 6, 421–425 (2002).
Bobadilla, J., Ortega, F., Hernando, A. & Gutiérrez, A. Recommender systems survey. Knowl. Based Syst. 46, 109–132 (2013).
Pazzani, M. & Billsus, D. in The Adaptive Web (eds Brusilovsky, P., Kobsa, A. & Nejdl, W.) 325–341 (Springer, Berlin, 2007).
Nunes, M. A. S. & Hu, R. Personality-based recommender systems: an overview. In Proc. 6th ACM Conference on Recommender Systems 5–6 (ACM, 2012).
Fernández-Tobas, I., Braunhofer, M., Elahi, M., Ricci, F. & Cantador, I. Alleviating the new user problem in collaborative filtering by exploiting personality information. User Model. User Adapt. Interact. 26, 221–255 (2016).
Gino, F., Shang, J. & Croson, R. The impact of information from similar or different advisors on judgment. Organ. Behav. Hum. Decis. Process. 108, 287–302 (2009).
West, P. M. Predicting preferences: an examination of agent learning. J. Consum. Res. 23, 68–80 (1996).
Breiman, L. Arcing classifier (with discussion and a rejoinder by the author). Ann. Stat. 26, 801–849 1998).
Burke, R. Hybrid recommender systems: survey and experiments. User Model. User Adapt. Interact. 12, 331–370 (2002).
Herzog, S. M. & Hertwig, R. The wisdom of many in one mind: improving individual judgments with dialectical bootstrapping. Psychol. Sci. 20, 231–237 (2009).
Herzog, S. M. & von Helversen, B. Strategy selection versus strategy blending: a predictive perspective on single- and multi-strategy accounts in multiple-cue estimation. J. Behav. Decis. Mak. 31, 233–249 (2018).
Herzog, S. M. & Hertwig, R. Harnessing the wisdom of the inner crowd. Trends Cogn. Sci. 18, 504–506 (2014).
Bell, R. M. & Koren, Y. Lessons from the Netflix prize challenge. SIGKDD Explor. 9, 75–79 (2007).
Repacholi, B. M. & Gopnik, A. Early reasoning about desires: evidence from 14- and 18-month-olds. Dev. Psychol. 33, 12–21 (1997).
Mata, J., Scheibehenne, B. & Todd, P. M. Predicting children’s meal preferences: how much do parents know? Appetite 50, 367–375 (2008).
Davis, H. L., Hoch, S. J. & Ragsdale, E. E. An anchoring and adjustment model of spousal predictions. J. Consum. Res. 13, 25–37 (1986).
Sharma, A. & Cosley, D. Studying and modeling the connection between people’s preferences and content sharing. In Proc. 18th ACM Conference on Computer Supported Cooperative Work & Social Computing 1246–1257 (ACM, 2015).
Sinha, R. R. & Swearingen, K. Comparing recommendations made by online systems and friends. In Proc. DELOS-NSF Workshop on Personalisation and Recommender Systems in Digital Libraries (ACM, 2001).
Krishnan, V., Narayanashetty, P. K., Nathan, M., Davies, R. T. & Konstan, J. A. Who predicts better?—Results from an online study comparing humans and an online recommender system. In Proc. 2008 ACM Conference on Recommender Systems 211–218 (ACM, 2008).
Yeomans, M., Shah, A. K., Mullainathan, S. & Kleinberg, J. Making sense of recommendations. Preprint at http://scholar.harvard.edu/files/sendhil/files/recommenders55_01.pdf (2016).
Thurstone, L. L. A law of comparative judgment. Psychol. Rev. 34, 273–286 (1927).
Rosenblatt, F. The perceptron: a probabilistic model for information storage and organization in the brain. Psychol. Rev. 65, 386–408 (1958).
Gigerenzer, G. From tools to theories: a heuristic of discovery in cognitive psychology. Psychol. Rev. 98, 254–267 (1991).
Analytis, P. P., Kothiyal, A. & Katsikopoulos, K. Multi-attribute utility models as cognitive search engines. Judgm. Decis. Mak. 9, 403–419 (2014).
Gordon, K. Group judgments in the field of lifted weights. J. Exp. Psychol. 7, 398–400 (1924).
Hogarth, R. M. A note on aggregating opinions. Organ. Behav. Hum. Perform. 21, 40–46 (1978).
Treynor, J. L. Market efficiency and the bean jar experiment. Financ. Anal. J. 43, 50–53 (1987).
Bollen, D., Knijnenburg, B. P., Willemsen, M. C. & Graus, M. Understanding choice overload in recommender systems. In Proc. 4th ACM Conference on Recommender Systems 63–70 (ACM, 2010).
Ekstrand, M. D., Harper, F. M., Willemsen, M. C. & Konstan, J. A. User perception of differences in recommender algorithms. In Proc. 8th ACM Conference on Recommender Systems 161–168 (ACM, 2014).
Rich, E. User modeling via stereotypes. Cogn. Sci. 3, 329–354 (1979).
Sarwar, B., Karypis, G., Konstan, J. & Riedl, J. Application of Dimensionality Reduction in Recommender System—A Case Study Technical Report (Minnesota Univ. Department of Computer Science, 2000).
Schnabel, T., Swaminathan, A., Singh, A., Chandak, N. & Joachims, T. Recommendations as treatments: debiasing learning and evaluation. In ICML'16 Proc. 33rd International Conference on International Conference on Machine Learning (eds Balcan, M. F. & Weinberger, K. Q.) 1670–1679 (JMLR, 2016).
Herlocker, J. L., Konstan, J. A., Terveen, L. G. & Riedl, J. T. Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22, 5–53 (2004).
Gigerenzer, G. & Goldstein, D. G. Reasoning the fast and frugal way: models of bounded rationality. Psychol. Rev. 103, 650–669 (1996).
Richerson, P. J. & Boyd, R. Not by Genes Alone: How Culture Transformed Human Evolution (Univ. Chicago Press, Chicago, IL, 2008).
Yetton, P. W. & Bottger, P. C. Individual versus group problem solving: an empirical test of a best-member strategy. Organ. Behav. Hum. Perform. 29, 307–321 (1982).
Shardanand, U. & Maes, P. Social information filtering: algorithms for automating “word of mouth”. In Proc. SIGCHI Conference on Human Factors in Computing Systems (eds Katz, I. R. et al.) 210–217 (ACM/Addison-Wesley, 1995).
Hammond, K. R., Hursch, C. J. & Todd, F. J. Analyzing the components of clinical inference. Psychol. Rev. 71, 438–456 (1964).
Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P. & Riedl, J. GroupLens: an open architecture for collaborative filtering of netnews. In Proc. 1994 ACM Conference on Computer Supported Cooperative Work 175–186 (ACM, 1994).
Kruschke, J. K. ALCOVE: an exemplar-based connectionist model of category learning. Psychol. Rev. 99, 22–44 (1992).
Juslin, P. & Persson, M. PROBabilities from EXemplars (PROBEX): a “lazy” algorithm for probabilistic inference from generic knowledge. Cogn. Sci. 26, 563–607 (2002).
Nosofsky, R. M. Attention, similarity, and the identification–categorization relationship. J. Exp. Psychol. Gen. 115, 39–57 (1986).
Altman, N. S. An introduction to kernel and nearest-neighbor nonparametric regression. Am. Stat. 46, 175–185 (1992).
Sarwar, B., Karypis, G., Konstan, J. & Riedl, J. Item-based collaborative filtering recommendation algorithms. In Proc. 10th International Conference on World Wide Web 285–295 (ACM, 2001).
Gilbert, D. T., Killingsworth, M. A., Eyre, R. N. & Wilson, T. D. The surprising power of neighborly advice. Science 323, 1617–1619 (2009).
Cavalli-Sforza, L. L. & Feldman, M. W. Cultural Transmission and Evolution: a Quantitative Approach (Princeton Univ. Press, Princeton, NJ, 1981).
Acknowledgements
We thank D. Cosley, T. Joachims, J. Müller-Trede, T. Schnabel, D. Wolpert and C. M. Wu for their insightful comments. We are indebted to the members of the ABC and ARC research groups at the Max Planck Institute for Human Development and the participants in the Microsoft Artificial Intelligence seminar at Cornell University for their constructive feedback during presentations. We are grateful to A. Todd and S. Goss for editing the manuscript. This research was supported in part through NSF Award IIS-1513692. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
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P.P.A., D.B. and S.M.H. conceived the research, developed the simulation framework for the main results and the bias–variance decomposition, analysed the simulation results and wrote the paper.
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Analytis, P.P., Barkoczi, D. & Herzog, S.M. Social learning strategies for matters of taste. Nat Hum Behav 2, 415–424 (2018). https://doi.org/10.1038/s41562-018-0343-2
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DOI: https://doi.org/10.1038/s41562-018-0343-2
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