Frontiers: Recommending What to Search: Sales Volume and Consumption Diversity Effects of a Query Recommender System

References

• Adipat B, Zhang D, Zhou L (2011) The effects of tree-view based presentation adaptation on mobile web browsing. MIS Quart. 35(1):99–121.Crossref, Google Scholar
• Aridor G, Gonçalves D, Kluver D, Kong R, Konstan J (2022) The informational role of online recommendations: Evidence from a field experiment. Preprint, submitted November 25, https://arxiv.org/abs/2211.14219.Google Scholar
• Bacciu A, Palumbo E, Damianou A, Tonellotto N, Silvestri F (2024) Generating query recommendations via LLMs. Preprint, submitted May 30, https://arxiv.org/abs/2405.19749.Google Scholar
• Brynjolfsson E, Hu Y, Simester D (2011) Goodbye Pareto principle, hello long tail: The effect of search costs on the concentration of product sales. Management Sci. 57(8):1373–1386.Link, Google Scholar
• Chevalier S (2024) Distribution of retail website visits and orders worldwide in 3rd quarter 2024, by device. Accessed December 10, 2024, https://www.statista.com/statistics/568684/e-commerce-website-visit-and-orders-by-device/.Google Scholar
• Chintala SC, Liaukonytė J, Yang N (2024) Browsing the aisles or browsing the app? How online grocery shopping is changing what we buy. Marketing Sci. 43(3):506–522.Link, Google Scholar
• De P, Hu Y, Rahman MS (2010) Technology usage and online sales: An empirical study. Management Sci. 56(11):1930–1945.Link, Google Scholar
• Donnelly R, Kanodia A, Morozov I (2024) Welfare effects of personalized rankings. Marketing Sci. 43(1):92–113.Link, Google Scholar
• Fleder D, Hosanagar K (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
• Ghose A, Goldfarb A, Han SP (2013) How is the mobile internet different? Search costs and local activities. Inform. Systems Res. 24(3):613–631.Link, Google Scholar
• Gini C (1912) Variabilità e mutabilità: Contributo allo studio delle distribuzioni e delle relazioni statistiche. [Fasc. I.] (P. Cuppini, Bologna), 159.Google Scholar
• Goli A, Singh A (2024) Frontiers: Can large language models capture human preferences? Marketing Sci. 43(4):709–722.Link, Google Scholar
• Gupta A, Keerthi SS, Acharya A, Cheng M, Ocejo Elizondo B, Ramanath R, Mazumder R, Basu K, Tay JK, Gupta R (2023) Practical design of performant recommender systems using large-scale linear programming-based global inference. KDD’23: Proc. 29th ACM SIGKDD Conf. Knowledge Discovery Data Mining (Association for Computing Machinery, New York), 5781–5782.Google Scholar
• Hinz O, Eckert J, Skiera B (2011) Drivers of the long tail phenomenon: An empirical analysis. J. Management Inform. Systems 27(4):43–70.Crossref, Google Scholar
• Holtz D, Carterette B, Chandar P, Nazari Z, Cramer H, Aral S (2020) The engagement-diversity connection: Evidence from a field experiment on Spotify. EC’20: Proc. 21st ACM Conf. Econom. Comput. (Association for Computing Machinery, New York), 75–76.Google Scholar
• Hosanagar K, Fleder D, Lee D, Buja A (2014) Will the global village fracture into tribes? Recommender systems and their effects on consumer fragmentation. Management Sci. 60(4):805–823.Link, Google Scholar
• Huang Z, Mamoulis N (2017) Location-aware query recommendation for search engines at scale. Gertz M, Renz M, Zhou X, Hoel E, Ku W-S, Voisard A, Zhang C, et al., eds. Adv. Spatial Temporal Databases. SSTD 2017, Lecture Notes in Computer Science, vol. 10411 (Springer, Cham), 203–220.Google Scholar
• Jerath K, Ma L, Park YH (2014) Consumer click behavior at a search engine: The role of keyword popularity. J. Marketing Res. 51(4):480–486.Crossref, Google Scholar
• Kotler P (2002) Marketing Management: Custom Edition for University of Phoenix (Pearson Custom Publishing, Hoboken, NJ).Google Scholar
• Lee D, Hosanagar K (2019) How do recommender systems affect sales diversity? A cross-category investigation via randomized field experiment. Inform. Systems Res. 30(1):239–259.Link, Google Scholar
• Lei X, Chen Y, Sen A (2025) Trade-offs in leveraging external data capabilities: Evidence from a field experiment in an online search market. Management Sci. Forthcoming.Google Scholar
• Li L, Chen J, Raghunathan S (2020) Informative role of recommender systems in electronic marketplaces: A boon or a bane for competing sellers. MIS Quart. 44(4):1957–1985.Crossref, Google Scholar
• Li X, Grahl J, Hinz O (2022) How do recommender systems lead to consumer purchases? A causal mediation analysis of a field experiment. Inform. Systems Res. 33(2):620–637.Link, Google Scholar
• Li Y, Lin X, Wang W, Feng F, Pang L, Li W, Nie L, He X, Chua TS (2024) A survey of generative search and recommendation in the era of large language models. Preprint, submitted April 25, https://arxiv.org/abs/2404.16924.Google Scholar
• Lin X, Wang W, Li Y, Feng F, Ng SK, Chua TS (2024) Bridging items and language: A transition paradigm for large language model-based recommendation. KDD’24: Proc. 30th ACM SIGKDD Conf. Knowledge Discovery Data Mining (Association for Computing Machinery, New York), 1816–1826.Google Scholar
• Lorenz MO (1905) Methods of measuring the concentration of wealth. Publ. Amer. Statist. Assoc. 9(70):209–219.Crossref, Google Scholar
• Newman JW (1977) Consumer external search: Amount and determinants. Consumer and Industrial Buying Behavior (North-Holland Publishing Co., New York), 79–94.Google Scholar
• Olteanu A, Diaz F, Kazai G (2020) When are search completion suggestions problematic? Proc. ACM Human-Comput. Interaction, vol. 4 (Association for Computing Machinery, New York), 1–25.Google Scholar
• Punj GN, Staelin R (1983) A model of consumer information search behavior for new automobiles. J. Consumer Res. 9(4):366–380.Crossref, Google Scholar
• Rafieian O, Yoganarasimhan H (2022) Variety effects in mobile advertising. J. Marketing Res. 59(4):718–738.Crossref, Google Scholar
• Ratchford BT (2009) Consumer search behavior and its effect on markets. Foundations Trends Marketing 3(1):1–74.Crossref, Google Scholar
• Shokouhi M (2013) Learning to personalize query auto-completion. SIGIR’13: Proc. 36th Internat. ACM SIGIR Conf. Res. Development Inform. Retrieval (Association for Computing Machinery, New York), 103–112.Google Scholar
• Stamenkovic D, Karatzoglou A, Arapakis I, Xin X, Katevas K (2022) Choosing the best of both worlds: Diverse and novel recommendations through multi-objective reinforcement learning. Proc. 15th ACM Internat. Conf. Web Search Data Mining (Association for Computing Machinery, New York), 957–965.Google Scholar
• Sun T, Yuan Z, Li C, Zhang K, Xu J (2024) The value of personal data in internet commerce: A high-stakes field experiment on data regulation policy. Management Sci. 70(4):2645–2660.Link, Google Scholar
• Tucker C, Zhang J (2011) How does popularity information affect choices? A field experiment. Management Sci. 57(5):828–842.Link, Google Scholar
• Venkatesh V, Aloysius J, Hoehle H, Burton S (2017) Design and evaluation of auto-id enabled shopping assistance artifacts in customers’ mobile phones: Two retail store laboratory experiments. MIS Quart. 41(1):83–117.Crossref, Google Scholar
• Wan X, Kumar A, Li X (2024) How do product recommendations help consumers search? Evidence from a field experiment. Management Sci. 70(9):5776–5794.Abstract, Google Scholar
• Wang Y, Tao L, Zhang XX (2024) Recommending for a multi-sided marketplace: A multi-objective hierarchical approach. Marketing Sci., ePub ahead of print August 14, https://doi.org/10.1287/mksc.2022.0238.Google Scholar
• Xi Y, Liu W, Lin J, Cai X, Zhu H, Zhu J, Chen B, Tang R, Zhang W, Yu Y (2024) Towards open-world recommendation with knowledge augmentation from large language models. RecSys’24: Proc. 18th ACM Conf. Recommender Systems (Association for Computing Machinery, New York), 12–22.Google Scholar
• Zahera HM, El-Hady GF, Abd El-Wahed WF (2013) Query recommendation for improving search engine results. Lu Z, ed. Information Retrieval Methods for Multidisciplinary Applications (Information Science Reference, Hershey, PA), 46–53.Crossref, Google Scholar
• Zhang T, Yang L, Xiao Z, Jiang W, Ning W (2024) On practical diversified recommendation with controllable category diversity framework. WWW ‘24: Companion Proc. ACM Web Conf. 2024 (Association for Computing Machinery, New York), 255–263.Google Scholar
• Zhou B, Zou T (2023) Competing for recommendations: The strategic impact of personalized product recommendations in online marketplaces. Marketing Sci. 42(2):360–376.Link, Google Scholar