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Understanding User Podcast Consumption Using Sequential Treatment Effect Estimation
Vishwali Mhasawade · Praveen Chandar · Ghazal Fazelnia · Benjamin Carterette

Podcast recommendation systems are increasingly becoming prevalent with the growing popularity of podcasts. Accounting for the sequential nature of user interaction with such systems is cardinal to ensure that the recommendations are meaningful to the users. However, while there is growing work in effectively designing the recommendation systems, there is limited understanding of how the recommendations affect user behavior, i.e., what is the causal effect of a recommendation on long-term user behavior. In this work, we seek to explain the sequential causal effect of consuming a podcast category on long-term user behavior. We estimate the direct and indirect effect of user consumption, here, podcast category, on consecutive outcomes, i.e, aggregate weekly user podcast consumption observed across four weeks. We observe nonzero, non-homogeneous direct and indirect effects on outcomes across all weeks for multiple treatments, i.e. podcast categories. Moreover, consuming certain categories such as true crime have overall higher causal effects than others, highlighting that some podcast categories can alter user behavior resulting in long-term increased consumption.

Author Information

Vishwali Mhasawade (New York University)
Praveen Chandar (Spotify)

Praveen Chandar is a Senior Research Scientist at Spotify working on search and recommendations. His research interests are in machine learning, information retrieval, and recommendation systems with a focus on experimentation and evaluation. Praveen received his Ph.D. from the University of Delaware, working on novelty and diversity aspects of search evaluation. He was previously a Research Staff Member at IBM Research. He has published papers at top conferences including, SIGIR, KDD, WSDM, WWW, CIKM, CHI, and UAI.

Ghazal Fazelnia (Spotify Research)
Benjamin Carterette (Spotify)

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