Browsing by Author "Kapoor, Komal"
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Item A Hazard Based Approach to User Return Time Prediction(2013-11-18) Kapoor, Komal; Sun, Mingxuan; Srivastava, Jaideep; Ye, TaoIn the competitive environment of the internet, retaining and growing one's user base is of major concern to most web services. Furthermore, the economic model of many web services is allowing free access to most content, and generating revenue through advertising. This unique model requires securing user time on a site rather than the purchase of good. Hence, it is crucially important to create new kinds of metrics and solutions for growth and retention efforts for web services. In this work, we first propose a new retention metric for web services concentrating on the rate of user return. Secondly, we apply predictive analysis to the proposed retention metric on a service. Finally, we set up a simple yet effective framework to evaluate a multitude of factors that contribute to user return. Specifically, we define the problem of return time prediction for free web services. Our solution is based on the Cox's proportional hazard model from survival analysis. The hazard based approach offers several benefits including the ability to work with censored data, to model the dynamics in user return rates, and to easily incorporate different types of covariates in the model. We compare the performance of our hazard based model in predicting the user return time and in categorizing users into buckets based on their predicted return time, against several baseline regression and classification methods and find the hazard based approach to far surpass our baselines.Item Measuring spontaneous devaluations in user preferences(2013-04-09) Kapoor, Komal; Srivastava, Nisheeth; Srivastava, Jaideep; Schrater, PaulSpontaneous devaluation in preferences is ubiquitous, where yesterday's hit is today's affliction. Despite technological advances facilitating access to a wide range of media commodities, finding engaging content is a major enterprise with few principled solutions. Systems tracking spontaneous devaluation in user preferences can allow prediction of the onset of boredom in users potentially catering to their changed needs. In this work, we study the music listening histories of Last.fm users focusing on the changes in their preferences based on their choices for different artists at different points in time. A hazard function, commonly used in statistics for survival analysis, is used to capture the rate at which a user returns to an artist as a function of exposure to the artist. The analysis provides the first evidence of spontaneous devaluation in preferences of music listeners. Better understanding of the temporal dynamics of this phenomenon can inform solutions to the similarity-diversity dilemma of recommender systems.Item Models for Dynamic User Preferences and Their Applications(2013-11-18) Kapoor, KomalComputational models of preferences have been applied in various domains including economics, consumer research and marketing. They are also commonly used for designing recommender agents for suggesting new content to the users based on their inferred preferences. A major challenge for such systems is to cater to the changing needs of the users over time. Although, user preferences are known to be dynamic in nature, there are few methods for predicting these dynamics in a reliable way. In this thesis, the problem of defining predictive models of dynamic user preferences is addressed. A solution to this problem is provided by formulating a framework that incorporates history and time dependent changes in user preferences for items. Two types of changes in user preferences are identified. Firstly, user's interests are modeled as either favoring familiarity or looking for exploring new content. Secondly, user's preferences for familiar items are defined to change as a function of exposure for incorporating the psychological effects of boredom from repetition. Such a framework for estimating dynamic preferences of users provides unprecedented insights to user changing needs. These insights are proposed to be incorporated in solving two important problems for content services; user retention and temporally-aware recommendations.Item Models of dynamic user preferences and their applications to recommendation and retention(2014-12) Kapoor, KomalComputational 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.Item Revised: A Hazard Based Approach to User Return Time Prediction(2014-07-23) Kapoor, Komal; Sun, Mingxuan; Srivastava, Jaideep; Ye, TaoIn the competitive environment of the internet, retaining and growing one’s user base is of major concern to most web services. Furthermore, the economic model of many web services is allowing free access to most content, and generating revenue through advertising. This unique model requires securing user time on a site rather than the purchase of good which makes it crucially important to create new kinds of metrics and solutions for growth and retention efforts for web services. In this work, we address this problem by proposing a new retention metric for web services by concentrating on the rate of user return. We further apply predictive analysis to the proposed retention metric on a service, as a means for characterizing lost customers. Finally, we set up a simple yet effective framework to evaluate a multitude of factors that contribute to user return. Specifically, we define the problem of return time prediction for free web services. Our solution is based on the Cox’s proportional hazard model from survival analysis. The hazard based approach offers several benefits including the ability to work with censored data, to model the dynamics in user return rates, and to easily incorporate different types of covariates in the model. We compare the performance of our hazard based model in predicting the user return time and in categorizing users into buckets based on their predicted return time, against several baseline regression and classification methods and find the hazard based approach to be superior.