[Nachiketa Sahoo] # Personalized Recommendation When User Preferences are Changing

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# Personalized Recommendation When User Preferences are Changing

Nachiketa Sahoo

Junior Faculty Fellow, Hariri Institute for Computing
Assistant Professor, Department of Information Systems
Boston University

Abstract: Most of the works in the recommender systems literature have been developed under the assumption that user preference has a static pattern. However, this is a strong assumption especially when the user is observed over a long period of time. With the help of a data set on employees’ blog reading behavior, we show that users’ product selection behaviors change over time. We propose a hidden Markov model to correctly interpret the users’ product selection behaviors and make personalized recommendations. The user preference is modeled as a hidden Markov sequence. A variable number of product selections of different types by each user in each time period requires a novel observation model. We propose a negative binomial mixture of multinomial to model such observations. This allows us to identify stable global preferences of users and to track individual users through these preferences. We evaluate our model using three real-world data sets with different characteristics. They include data on employee blog reading behavior inside a firm, users’ movie rating behavior at Netflix, and users’ music listening behavior collected through last.fm. We compare the recommendation performance of the proposed model with that of a number of collaborative filtering algorithms and a recently proposed temporal link prediction algorithm. We find that the proposed HMM-based collaborative filter performs as well as the best among the alternative algorithms when the data is sparse or static. However, it outperforms the existing algorithms when the data is less sparse and the user preference is changing. We further examine the performances of the algorithms using simulated data with different characteristics and highlight the scenarios where it is beneficial to use a dynamic model to generate product recommendation.

Bio: Nachiketa Sahoo is an assistant professor in Information Systems at the Questrom School of Business in Boston University. He completed his MS in Computer Science and PhD in Information Systems and Management from Carnegie Mellon University. Before joining Boston University Nachiketa worked as a Visiting Assistant Professor at the Tepper School of Business, Carnegie Mellon University. Nachiketa’s research interests lie at the intersection of statistical machine learning and information systems. One of the topics he has worked in is personalized Information Filtering that has applications in personalized product recommendation and targeted advertisements. His work has been published in Information Systems journal such as Information Systems Research and MIS Quarterly.