When and How to Diversify—A Multicategory Utility Model for Personalized Content Recommendation
Published Online:3 Apr 2019https://doi.org/10.1287/mnsc.2018.3127
References
- (2009) Toward more diverse recommendations: Item re-ranking methods for recommender systems. Presentation, 19th Workshop on Information Technologies and Systems, December 14–15, Phoenix, AZ.Google Scholar
- (2014) Optimization-based approaches for maximizing aggregate recommendation diversity. INFORMS J. Comput. 26(2):351–369.Link, Google Scholar
- (2005) Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Trans. Knowledge Data Engrg. 17(6):734–749.Crossref, Google Scholar
- (2011) Context-aware recommender systems. Ricci F, Rokach L, Shapira B, Kantor PB, eds. Recommender Systems Handbook (Springer, Boston), 217–253.Crossref, Google Scholar
- (2015) Bundling effects on variety seeking for digital information goods. J. Management Inform. Systems 31(4):182–212.Crossref, Google Scholar
- (2005) Incorporating contextual information in recommender systems using a multidimensional approach. ACM Trans. Inform. Systems 23(1):103–145.Crossref, Google Scholar
- (2016) Context-sensitive recommender systems. Recommender Systems (Springer, Cham, Switzerland), 255–281.Crossref, Google Scholar
- (1961) Capital-labor substitution and economic efficiency. Rev. Econom. Statist. 43(3):225–250.Crossref, Google Scholar
- (2001) Utility‐consistent brand demand systems with endogenous category consumption: Principles and marketing applications. Decision Sci. 32(3):399–422.Crossref, Google Scholar
- (1999) Extended framework for modeling choice behavior. Marketing Lett. 10(3):187–203.Crossref, Google Scholar
- (2000) User modeling for adaptive news access. User Model. User-Adapted Interaction 10(2/3):147–180.Crossref, Google Scholar
- (2003) Latent Dirichlet allocation. J. Machine Learn. Res. 3(January):993–1022.Google Scholar
- (1984) Position bias in multiple-choice questions. J. Marketing Res. 21(2):216–220.Crossref, Google Scholar
- (2001) Improving recommendation diversity. O'Donoghue D, ed. Proc. 12th Irish Conf. Artificial Intelligence Cognitive Sci., Maynooth, Ireland, 75–84.Google Scholar
- (1998) Empirical analysis of predictive algorithms for collaborative filtering. Proc. 14th Conf. Uncertainty Artificial Intelligence (Morgan Kaufmann Publishers, San Francisco), 43–52.Google Scholar
- (2009) A dynamic Bayesian network click model for web search ranking. Proc. 18th Internat. Conf. World Wide Web (ACM, New York), 1–10.Crossref, Google Scholar
- (2010) Future directions in learning to rank. Proc. Machine Learn. Res. 14:91–100.Google Scholar
- (2005) Context-aware collaborative filtering system: Predicting the user’s preference in the ubiquitous computing environment. Strang T, Linnhoff-Popien C, eds. Location- and Context-Awareness, Lecture Notes in Computer Science, vol. 3479 (Springer, Berlin), 244–253.Google Scholar
- (2008) An experimental comparison of click position-bias models. Proc. 2008 Internat. Conf. Web Search Data Mining (ACM, New York), 87–94.Crossref, Google Scholar
- (1969) Position bias in paired product tests. J. Marketing Res. 6(1):98–100.Crossref, Google Scholar
- (1977) Monopolistic competition and optimum product diversity. Amer. Econom. Rev. 67(3):297–308.Google Scholar
- (2011) Temporal link prediction using matrix and tensor factorizations. ACM Trans. Knowledge Discovery Data 5(2), Article No. 10:1–27.Crossref, Google Scholar
- (2013) Newspapers: Stabilizing, but still threatened. Accessed July 18, 2016, http://www.stateofthemedia.org/2013/newspapers-stabilizing-but-still-threatened/.Google Scholar
- (1996) Decision-making under uncertainty: Capturing dynamic brand choice processes in turbulent consumer goods markets. Marketing Sci. 15(1):1–20.Link, Google Scholar
- (2015) 5 words you never thought you'd hear at the elearning summit: The cognitive science of clickbait. Presentation, Minnesota eLearning Summit, July 29, Minnesota Learning Commons, Minneapolis/St. Paul.Google Scholar
- (2009) Blockbuster culture’s next rise or fall: The impact of recommender systems on sales diversity. Management Sci. 55(5):697–712.Link, Google Scholar
- (2005) How subjective grouping of options influences choice and allocation: Diversification bias and the phenomenon of partition dependence. J. Experiment. Psych.: General 134(4):538–551.Crossref, Google Scholar
- (1992) Using collaborative filtering to weave an information tapestry. Comm. ACM 35(12):61–70.Crossref, Google Scholar
- (2015) The Netflix recommender system: Algorithms, business value, and innovation. ACM Trans. Management Inform. Systems 6(4):1–19.Crossref, Google Scholar
- (2014) Taboola and Outbrain are battling a bad reputation (and each other). Fortune (August 18), http://fortune.com/2014/08/18/taboola-outbrain-battle-bad-reputation-each-other/.Google Scholar
- (2014) Dynamics of human trust in recommender systems. Proc. 8th ACM Conf. Recommender Systems (ACM, New York), 305–308.Crossref, Google Scholar
- (1979) Algorithm as 136: A K-means clustering algorithm. J. Roy. Statist. Soc. Ser. C (Appl. Statist.) 28(1):100–108.Google Scholar
- (2016) Fusing similarity models with Markov chains for sparse sequential recommendation. Proc. IEEE 16th Internat. Conf. Data Mining (Curran Associates, Inc., Red Hook, NY), 191–200.Google Scholar
- (2007) Analyzing consumer-product graphs: Empirical findings and applications in recommender systems. Management Sci. 53(7):1146–1164.Link, Google Scholar
- (1974) Multi-attribute utility models: A review of field and field-like studies. Management Sci. 20(10):1393–1402.Link, Google Scholar
- (2002) Modeling consumer demand for variety. Marketing Sci. 21(3):229–250.Link, Google Scholar
- (2006) The effects of personalization and familiarity on trust and adoption of recommendation agents. MIS Quart. 30(4):941–960.Crossref, Google Scholar
- (2010) Temporal diversity in recommender systems. Proc. 33rd Internat. ACM SIGIR Conf. Res. Development Inform. Retrieval (ACM, New York), 210–217.Crossref, Google Scholar
- (2014) Impact of recommender systems on sales volume and diversity. Proc. 35th Internat. Conf. Inform. Systems, Auckland, New Zealand (Association for Information Systems).Google Scholar
- (2003) Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Comput. 7(1):76–80.Crossref, Google Scholar
- (2016) Structural analysis of user choices for mobile app recommendation. ACM Trans. Knowledge Discovery Data 11(2):1–23.Crossref, Google Scholar
- (2011) Content-based recommender systems: State of the art and trends. Ricci F, Rokach L, Shapira B, Kantor PB, eds. Recommender Systems Handbook (Springer, Boston), 73–105.Crossref, Google Scholar
- (2009) Introduction to Information Retrieval (Cambridge University Press Cambridge, UK).Google Scholar
- (2015) Bezos takes hands-on role at Washington Post. Wall Street Journal. (December 20), http://www.wsj.com/articles/bezos-takes-hands-on-role-at-washington-post-1450658089.Google Scholar
- (1982) A dynamic attribute satiation model of variety-seeking behavior. J. Consumer Res. 9(2):141–150.Crossref, Google Scholar
- (1980) Econometric models for probabilistic choice among products. J. Bus. 53(3):S13–S29.Crossref, Google Scholar
- (1986) The choice theory approach to market research. Marketing Sci. 5(4):275–297.Link, Google Scholar
- (2006) Being accurate is not enough: How accuracy metrics have hurt recommender systems. Proc. CHI ’06 Extended Abstracts Human Factors Comput. Systems (ACM, New York), 1097–1101.Crossref, Google Scholar
- (2016) State of the News Media 2016. Accessed June 15, 2016, http://www.journalism.org/2016/06/15/state-of-the-news-media-2016/.Google Scholar
- (2004) Modeling online browsing and path analysis using clickstream data. Marketing Sci. 23(4):579–595.Link, Google Scholar
- (2003) The role of the management sciences in research on personalization. Management Sci. 49(10):1344–1362.Link, Google Scholar
- (2007) In Google we trust: Users’ decisions on rank, position, and relevance. J. Comput.-Mediated Comm. 12(3):801–823.Crossref, Google Scholar
- (2016) A coverage-based approach to recommendation diversity on similarity graph. Proc. 10th ACM Conf. Recommender Systems (ACM, Boston), 15–22.Crossref, Google Scholar
- (1995) Diversification bias: Explaining the discrepancy in variety seeking between combined and separated choices. J. Experiment. Psych.: Appl. 1(1):34–49.Crossref, Google Scholar
- (2001) Which is better: Simultaneous or sequential choice? Organ. Behav. Human Decision Processes 84(1):54–70.Crossref, Google Scholar
- (1994) Grouplens: An open architecture for collaborative filtering of netnews. Proc. CSCW '94 Conf. Comput.-Supported Cooperative Work (ACM, New York), 175–186.Crossref, Google Scholar
- (1996) A framework for investigating habits, “the hand of the past,” and heterogeneity in dynamic brand choice. Marketing Sci. 15(3):280–299.Link, Google Scholar
- (2012) A hidden Markov model for collaborative filtering. MIS Quart. 36(4):1329–1356.Crossref, Google Scholar
- SimilarTech (2016) Outbrain and Taboola market share and web usage statistics. Accessed July 13, 2016, https://www.similartech.com/technologies/outbrain, https://www.similartech.com/technologies/taboola.Google Scholar
- (1990) The effect of purchase quantity and timing on variety-seeking behavior. J. Marketing Res. 27(2):150–162.Crossref, Google Scholar
- (2014) How to attract and retain readers in enterprise blogging? Inform. Systems Res. 25(1):35–52.Link, Google Scholar
- (2008) If you liked this, you’re sure to love that. New York Times (November 21), https://www.nytimes.com/2008/11/23/magazine/23Netflix-t.html.Google Scholar
- (2013) Upworthy: I thought this website was crazy, but what happened next changed everything. The Atlantic (November 14), http://www.theatlantic.com/business/archive/2013/11/clickbait-crusaders-upworthys-viral-factory/281472/.Google Scholar
- (2015) The power of rankings: Quantifying the effects of rankings on online consumer search and choice. Working paper, University of Chicago, Chicago.Google Scholar
- (2011) Rank and relevance in novelty and diversity metrics for recommender systems. Proc. 5th ACM Conf. Recommender Systems (ACM, Chicago), 109–116.Crossref, Google Scholar
- (2013) Exploiting the diversity of user preferences for recommendation. Proc. 10th Conf. Open Res. Areas Inform. Retrieval (Le Centre De Hautes Etudes Internationales D'informatique Documentaire, Paris), 129–136.Google Scholar
- (2011) Intent-oriented diversity in recommender systems. Proc. 34th Internat. ACM SIGIR Conf. Res. Development Inform. Retrieval (ACM, New York), 1211–1212.Crossref, Google Scholar
- (2010) Intermediate Microeconomics: A Modern Approach (Norton, New York).Google Scholar
- (2014) Mind the ‘curiosity gap’: How can Upworthy be ‘noble’ and right when its clickbait headlines feel so wrong? National Post (May 23), http://news.nationalpost.com/news/mind-the-curiosity-gap-how-can-upworthy-be-noble-and-right-when-its-clickbait-headlines-feel-so-wrong.Google Scholar
- (2016a) Incorporating diversity in a learning to rank recommender system. Markov Z, Russell I, eds. Proc. 29th Internat. Florida Artificial Intelligence Res. Soc. Conf. (FLAIRS) (AAAI, Palo Alto, CA), 572–577.Google Scholar
- (2016b) Intent-aware diversification using a constrained PLSA. Proc. 10th ACM Conf. Recommender Systems (ACM, New York), 39–42.Crossref, Google Scholar
- (2011). Collaborative competitive filtering: Learning recommender using context of user choice. Proc. 34th Internat. ACM SIGIR Conf. Res. Development Inform. Retrieval (ACM, New York), 295–304.Crossref, Google Scholar
- (2013) The glory days of American journalism. Slate (March 19), https://slate.com/business/2013/03/pews-state-of-the-media-ignore-the-doomsaying-american-journalism-has-never-been-healthier.html.Google Scholar
- (2008) Avoiding monotony: Improving the diversity of recommendation lists. Proc. 2008 ACM Conf. Recommender Systems (ACM, New York), 123–130.Crossref, Google Scholar
- (2010) Solving the apparent diversity-accuracy dilemma of recommender systems. Proc. Natl. Acad. Sci. 107(10):4511–4515.Crossref, Google Scholar
- (2005). Improving recommendation lists through topic diversification. Proc. 14th Internat. Conf. World Wide Web (ACM, New York), 22–32.Crossref, Google Scholar

