When and how to diversify-a multicategory utility model for personalized content recommendation

Yicheng Song, Nachiketa Sahoo, Elie Ofek

Research output: Contribution to journalArticlepeer-review

21 Scopus citations


Sometimes we desire change, a break from the same, or an opportunity to fulfill different aspects of our needs. Noting that consumers seek variety, several approaches have been developed to diversify items recommended by personalized recommender systems. However, current diversification strategies operate under a one-shot paradigm without considering the evolution of preferences resulting from recent consumption. Therefore, such methods often sacrifice accuracy. In the context of onlinemedia,we showthat by recognizing that consumption in a session is the result of a sequence of utility-maximizing selections from various categories, one can increase recommendation accuracy by dynamically tailoring the diversity of suggested items to the diversity sought by the consumer. Our approach is based on a multicategory utility model that captures a consumer's preference for different categories of content, how quickly the consumer satiates with one category and wishes to substitute it with another, and how the consumer trades off costly search efforts with selecting from a recommended list to discover new content. Taken together, these three elements allowus to characterize howan individual selects a diverse set of items to consume over the course of a session and how likely the individual is to click on recommended content. We estimate the model using a clickstream data set from a large media outlet and apply it to determine themost relevant content to recommend at different stages of an online session. We find that our approach generates recommendations that are on average about 10% more accurate than optimized alternatives and about 25% more accurate than those diversified using existing diversification strategies. Moreover, the proposed method recommends content with diversity that more closely matches the diversity sought by readers, exhibiting lower concentration-diversification bias than other personalized recommender systems. Using a policy simulation, we estimate that recommending content using the proposed approach would result in visitors reading 23% additional articles at the studied website and deriving 35% higher utility. This could lead to immediate gains in revenue for the publisher and longer-term improvements in customer satisfaction and retention at the site.

Original languageEnglish (US)
Pages (from-to)3737-3757
Number of pages21
JournalManagement Science
Issue number8
StatePublished - Aug 1 2019

Bibliographical note

Funding Information:
History: Accepted by Chris Forman, information systems. Funding: Partial support for the research was provided by the Rafik B. Hariri Institute for Computing and Computational Science & Engineering at Boston University in the form of a graduate student fellowship to Y. Song. Supplemental Material: The online appendices are available at https://doi.org/10.1287/mnsc.2018.3127.

Publisher Copyright:
© 2019 INFORMS.

Copyright 2020 Elsevier B.V., All rights reserved.


  • Clickstream analysis
  • Collaborative filtering
  • Consumer utility models
  • Content consumption
  • Digital media
  • Learning-to-rank
  • Personalization
  • Recommendation diversity
  • Recommender systems
  • Variety seeking


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