University of Cambridge > Talks.cam > CJBS Marketing Group Seminars > When Diversity Becomes Relevant—A Multi-Category Utility Model of Consumer Response to Content Recommendations

When Diversity Becomes Relevant—A Multi-Category Utility Model of Consumer Response to Content Recommendations

Add to your list(s) Download to your calendar using vCal

If you have a question about this talk, please contact Luke Slater.

Sometimes we desire change, a break from the same, or an opportunity to fulfill different aspects of our needs. Noting that consumers seek variety and realizing that common recommender systems often concentrate only on a narrow set of products, several approaches have been developed to diversify suggested items. However, current diversification strategies operate under a one-shot paradigm and are not guided by the ability to enhance consumer utility by taking into account the evolution of preferences contingent on past consumption. This often leads to inaccurate predictions of what item will be most relevant for an individual at the current stage. By recognizing that choices in a session are the result of a sequence of utility maximizing selections from various categories, we show that one can increase recommendation accuracy by dynamically tailoring the diversity of items suggested to the diversity sought by the consumer. Our approach is based on a multi-category utility model that captures a consumer’s preference for different types of content, how quickly she satiates with one type and wishes to substitute it with another, and how she trades off her own costly search efforts with selecting from a recommended list to discover new content. Taken together, these three elements allow us to characterize how a consumer constructs a “basket” of content over the course of each session, and how likely she is to click on content recommended to her. We estimate the model using a clickstream dataset from a large media outlet and apply it to determine the most relevant content 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. Moreover, the proposed method recommends content that more closely matches the diversity sought by readers in their actual consumption—exhibiting the lowest concentration-diversification bias when compared to other personalized recommender systems. Using a policy simulation, we estimate that recommending content using our approach would result in visitors reading 23% additional articles at the studied website, which has direct revenue implication for the publisher of this site.

This talk is part of the CJBS Marketing Group Seminars series.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.

 

© 2006-2019 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity