Computational models of preferences are indispensable in today's era of information overload. They help facilitate access to all types of resources such as videos, songs, images etc. via several means such as content recommendation, site personalization and customization, and promotional targeting and marketing. They further serve as important business intelligence tools providing content providers insights to improving their practices. Vanilla models of preferences such as the static and time decay models commonly used today, albeit powerful, are limited in their abilities to cater to the volatile and shifting tastes and needs of the users. On the other hand, researchers in the domain of behavioral psychology have studied various aspects of the formation and evolution of individual preferences over several decades. Despite several advances, findings from behavioral research have had little or no impact on the design of computational models for dynamic preferences on the web. This is because, most of these studies have been qualitative and/or have relied on carefully constructed user experiments and surveys for testing their methods. The recent proliferation of online interfaces, however, allows the accumulation and analysis of large quantities of user preference logs, opening new avenues for understanding user dynamic behavior via data driven means. In this thesis, we therefore focus on developing a repertoire of tools and techniques for analyzing, modeling and predicting temporal and history dependent dynamics in preferences of online users. For this purpose, we adapt techniques from survival analysis, a branch of statistics used for analyzing duration data, to empirically measure changes in user preferences from their activity streams. We specifically use hazard functions which allow us to relate user dynamic preferences to user's dynamic choice probabilities for items, a quantity that can be conveniently measured from temporal logs of user consumption behavior. The dynamics in user preferences is further studied by analyzing their consumption behavior separately with respect to their (a) consumption of known (familiar) items; and (b) consumption of new items. We show that user consumption of a familiar item over time is driven by boredom. That is, we find that users move on to a new item when they get bored and return to the same item when their interest is restored. To model this behavior, we propose a Hidden Semi-Markov Model (HSMM) which includes two latent psychological preference states of the user for items - sensitization and boredom. In the sensitization state the user is highly engaged with the item, while in the boredom state the user is disinterested. We find that the gaps between consumption activities characterize these two states in the most natural way. We further find that our two state model for item consumption not only better predicts the revisit time of the user for items, but also, improves how items are recommended to the users, compared to existing state-of-the-art. This is because our model has two advantages over other methods. First, by modeling boredom it can avoid devalued items in the user recommendation list and second, by identifying items which the user would want to consume again, it can re-introduce items which have not been consumed for some time. >We further focus on a user's incorporation of new items in their consumption list (novelty seeking). We find that a user's preferences for novelty vary with time and such dynamics can be related to their boredom with familiar items. We then introduce for the first time, a novel approach to selectively incorporate novelty in a user's recommendation list using our prediction of their novelty seeking behavior. We further show that our approach is robust in terms of a new metric for accuracy more suitable to the problem of selective novelty recommendation based on user's novelty seeking preference. Finally, in the last section of this thesis we use hazard models to estimating the dynamic interest of the user in the content provider. This is achieved by using a Cox Proportional Hazard model to estimate the dynamic rate of a users' return to the service as a function of time since the user's last visit. We use our model to address the problem of retention for web services and show that our model allows better user segmentation based on predicted return time. The model further incorporates several behavioral and temporal features of the users interaction with the service which provides valuable insights to the service's practices. Based on the experimental findings on various real world datasets, from different sections of the thesis, the benefits of well-grounded dynamics preference models is apparent for improving user experience on the web in several important ways. We hope that the rigorous treatment of the problem of dynamics in user preferences provided in this work, assists and motivates future research in this area.
University of Minnesota Ph.D. dissertation. December 2014. Major: Computer Science. Advisor: Jaideep Srivastava and Prof. Paul R. Schrater. 1 computer file (PDF); xii, 114 pages.
Models of dynamic user preferences and their applications to recommendation and retention.
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