Thesis (M.A.). / Written for the Dept. of Art History and Communication Studies [Communications Graduate Program]. Title from title page of PDF (viewed 2008/05/12). Includes bibliographical references.

Thesis (B.A.)--Haverford College, Dept. of Computer Science, 2009. / Includes bibliographical references.

Thesis (Ph. D.)--University of Washington, 2006. / Vita. Includes bibliographical references (leaves 155-163).

Due to the rapid increase of social network resources and services, Internet users are now overwhelmed by the vast quantity of social media available. By utilizing the user’s context while consuming diverse multimedia contents, we can identify different personal preferences and settings. However, there is still a need to reinforce the recommendation process in a systematic way, with context-adaptive information. This thesis proposes a recommendation model, called HPEM, that establishes a bridge between the multimedia resources, user collaborative preferences, and the detected contextual information, including physiological parameters. The collection of contextual information and the delivery of the resulted recommendation is made possible by adapting the user’s environment using Ambient Intelligent (AmI) interfaces. Additionally, this thesis presents the potential of including a user’s biological signal and leveraging it within an adapted collaborative filtering algorithm in the recommendation process. First, the different versions of the proposed HPEM model utilize existing online social networks by incorporating social tags and rating information in ways that personalize the search for content in a particular detected context. By leveraging the social tagging, our proposed model computes the hidden preferences of users in certain contexts from other similar contexts, as well as the hidden assignment of contexts for items from other similar items. Second, we demonstrate the use of an optimization function to maximize the Mean Average Prevision (MAP) measure of the resulted recommendations. We demonstrate the feasibility of HPEM with two prototype applications that use contextual information for recommendations. Offline and online experiments have been conducted to measure the accuracy of delivering personalized recommendations, based on the user’s context; two real-world and one collected semi-synthetic datasets were used. Our evaluation results show a potential improvement to the quality of the recommendation when compared to state-of-the-art recommendation algorithms that consider contextual information. We also compare the proposed method to other algorithms, where user’s context is not used to personalize the recommendation results. Additionally, the results obtained demonstrate certain improvements on cold start situations, where relatively little information is known about a user or an item.

Cellular automata (CA) have been widely used as idealized models of spatially-extended dynamical systems and as models of massively parallel distributed computation devices. Despite their wide range of applications and the fact that CA are capable of universal computation (under particular constraints), the full potential of these models is unrealized to-date. This is for two reasons: (1) the absence of a programming paradigm to control these models to solve a given problem and (2) the lack of understanding of how these models compute a given task. This work addresses the notion of computation in two-dimensional cellular automata. Solutions using a decentralized parallel model of computation require information processing on a global level. CA have been used to solve the so-called density (or majority) classification task that requires a system-wide coordination of cells. To better understand and challenge the ability of CA to solve problems, I define, solve, and analyze novel tasks that require solutions with global information processing mechanisms. The ability of CA to perform parallel, collective computation is attributed to the complex pattern-forming system behavior. I further develop the computational mechanics framework to study the mechanism of collective computation in two-dimensional cellular automata. I define several approaches to automatically identify the spatiotemporal structures with information content. Finally, I demonstrate why an accurate model of information processing in two-dimensional cellular automata cannot be constructed from the space-time behavior of these structures.

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Esta dissertação apresenta novos resultados para a solução de problemas de implementação computacional na estimativa de sistemas singulares e sistemas Markovianos. São apresentados algoritmos alternativos para problemas de filtragem de maneira a minimizar problemas causados principalmente por erros de arredondamento e mal condicionamento de matrizes. O trabalho envolve basicamente algoritmos array e filtragem de informação para a estimativa de sistemas singulares nominais e robustos. Também é deduzido um algoritmo array para a filtragem de sistemas lineares sujeitos a saltos Markovianos. / This dissertation presents new results to solve computational implementation problems to estimate singular and Markovian systems. Alternative algorithms to handle computational filtering errors due rounding errors and ill-conditioned matrices are developed. This dissertation comprehends basically array algorithms and information filters for the estimate of nominal and robust singular systems. Also, it is developed an array algorithm for Markovian jump linear systems filtering.

As one of the social relations, "distrust" also performs an important role in online Web sites. We also observe that distrust information can also be incorporated to improve recommendation quality. Hence, the last part of this thesis studies the problem on how to improve recommender system by considering explicit distrust information among users. We make the assumption that users' distrust relations can be interpreted as the "dissimilar" relations since user ui distrusts user ud indicates that user ui disagrees with most of the opinions issued by user ud. Based on this intuition, the distrust relations between users can be easily modeled by adding the regularization term into the objective functions of the user-item matrix factorization. The experiments on the Epinions dataset indicate that distrust information is at least as important as trust information. / However, the data sparsity problem of the involved user-item matrix seriously affects the recommendation quality. Many existing approaches to recommender systems cannot easily deal with users who have made very few ratings. The objective of this thesis is to study how to build effective and efficient approaches to improve the recommendation performance. / In this thesis, we first propose two collaborative filtering methods which only utilize the user-item matrix for recommendations. The first method is a neighborhood-based collaborative filtering method which designs an effective missing data prediction algorithm to improve recommendation quality, while the second one is a model-based collaborative filtering method which employs matrix factorization technique to make the recommendation more accurate. / In view of the exponential growth of information generated by online users, social contextual information analysis is becoming important for many Web applications. Hence, based on the assumption that users can be easily influenced by the friends they trust and prefer their friends' recommendations, we propose two recommendation algorithms by incorporating users' social trust information. These two methods are based on probabilistic matrix factorization. The complexity analysis indicates that our approaches can be applied to very large datasets since they scale linearly with the number of observations, while the experimental results show that our methods perform better than the state-of-the-art approaches. / Recommender Systems are becoming increasingly indispensable nowadays since they focus on solving the information overload problem, by providing users with more proactive and personalized information services. Typically, recommender systems are based on Collaborative Filtering, which is a technique that automatically predicts the interest of an active user by collecting rating information from other similar users or items. Due to their potential commercial values and the associated great research challenges, Recommender systems have been extensively studied by both academia and industry recently. / Ma, Hao. / "December 2009." / Advisers: Irwin King; Michael R. Lyu. / Source: Dissertation Abstracts International, Volume: 72-01, Section: B, page: . / Thesis (Ph.D.)--Chinese University of Hong Kong, 2010. / Includes bibliographical references (leaves 136-154). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. Ann Arbor, MI : ProQuest Information and Learning Company, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese.

Mobile devices, such as smartphones and wearable devices, have become a popular platform to deliver recommendations and interact with users. To learn the decision rule of assigning recommendations, i.e. policy, neither one homogeneous policy for all users nor completely heterogeneous policy for each user is appropriate. Many attempts have been made to learn a policy for making recommendations using observational mobile health (mHealth) data. The majority of them focuses on a homogeneous policy, that is a one-fit-to-all policy for all users. It is a fair starting point for mHealth study, but it ignores the underlying user heterogeneity. Users with similar behavior pattern may have unobservable underlying heterogeneity. To solve this problem, we develop a personalized learning framework that models both population and personalized effect simultaneously. In the first part of this dissertation, we address the personalized policy learning problem using longitudinal mHealth application usage data. Personalized policy represents a paradigm shift from developing a single policy that may prescribe personalized decisions by tailoring. Specifically, we aim to develop the best policy, one per user, based on estimating random effects under generalized linear mixed model. With many random effects, we consider new estimation method and penalized objective to circumvent high-dimensional integrals for marginal likelihood approximation. We establish consistency and optimality of our method with endogenous application usage. We apply our method to develop personalized prompt schedules in 294 application users, with a goal to maximize the prompt response rate given past application usage and other contextual factors. We found the best push schedule given the same covariates varied among the users, thus calling for personalized policies. Using the estimated personalized policies would have achieved a mean prompt response rate of 23% in these users at 16 weeks or later: this is a remarkable improvement on the observed rate (11%), while the literature suggests 3%-15% user engagement at 3 months after download. The proposed method compares favorably to existing estimation methods including using the R function glmer in a simulation study. In the second part of this dissertation, we aim to solve a practical problem in the mHealth area. Low response rate has been a major issue that blocks researchers from collecting high quality mHealth data. Therefore, developing a prompting system is important to keep user engagement and increase response rate. We aim to learn personalized prompting time for users in order to gain a high response rate. An extension of the personalized learning algorithm is applied on the Intellicare data that incorporates penalties of the population effect parameters and personalized effect parameters into learning the personalized decision rule of sending prompts. The number of personalized policy parameters increases with sample size. Since there is a large number of users in the Intellicare data, it is challenging to estimate such high dimensional parameters. To solve the computational issue, we employ a bagging method that first bootstraps subsamples and then ensembles parameters learned from each subsample. The analysis of Intellicare data shows that sending prompts at a personalized hour helps achieve a higher response rate compared to a one-fit-to-all prompting hour.

The recent emergence of intelligent agent technology and advances in information gathering have been the important steps forward in efficiently managing and using the vast amount of information now available on the Web to make informed decisions. There are, however, still many problems that need to be overcome in the information gathering research arena to enable the delivery of relevant information required by end users. Good decisions cannot be made without sufficient, timely, and correct information. Traditionally it is said that knowledge is power, however, nowadays sufficient, timely, and correct information is power. So gathering relevant information to meet user information needs is the crucial step for making good decisions. The ideal goal of information gathering is to obtain only the information that users need (no more and no less). However, the volume of information available, diversity formats of information, uncertainties of information, and distributed locations of information (e.g. World Wide Web) hinder the process of gathering the right information to meet the user needs. Specifically, two fundamental issues in regard to efficiency of information gathering are mismatch and overload. The mismatch means some information that meets user needs has not been gathered (or missed out), whereas, the overload means some gathered information is not what users need. Traditional information retrieval has been developed well in the past twenty years. The introduction of the Web has changed people's perceptions of information retrieval. Usually, the task of information retrieval is considered to have the function of leading the user to those documents that are relevant to his/her information needs. The similar function in information retrieval is to filter out the irrelevant documents (or called information filtering). Research into traditional information retrieval has provided many retrieval models and techniques to represent documents and queries. Nowadays, information is becoming highly distributed, and increasingly difficult to gather. On the other hand, people have found a lot of uncertainties that are contained in the user information needs. These motivate the need for research in agent-based information gathering. Agent-based information systems arise at this moment. In these kinds of systems, intelligent agents will get commitments from their users and act on the users behalf to gather the required information. They can easily retrieve the relevant information from highly distributed uncertain environments because of their merits of intelligent, autonomy and distribution. The current research for agent-based information gathering systems is divided into single agent gathering systems, and multi-agent gathering systems. In both research areas, there are still open problems to be solved so that agent-based information gathering systems can retrieve the uncertain information more effectively from the highly distributed environments. The aim of this thesis is to research the theoretical framework for intelligent agents to gather information from the Web. This research integrates the areas of information retrieval and intelligent agents. The specific research areas in this thesis are the development of an information filtering model for single agent systems, and the development of a dynamic belief model for information fusion for multi-agent systems. The research results are also supported by the construction of real information gathering agents (e.g., Job Agent) for the Internet to help users to gather useful information stored in Web sites. In such a framework, information gathering agents have abilities to describe (or learn) the user information needs, and act like users to retrieve, filter, and/or fuse the information. A rough set based information filtering model is developed to address the problem of overload. The new approach allows users to describe their information needs on user concept spaces rather than on document spaces, and it views a user information need as a rough set over the document space. The rough set decision theory is used to classify new documents into three regions: positive region, boundary region, and negative region. Two experiments are presented to verify this model, and it shows that the rough set based model provides an efficient approach to the overload problem. In this research, a dynamic belief model for information fusion in multi-agent environments is also developed. This model has a polynomial time complexity, and it has been proven that the fusion results are belief (mass) functions. By using this model, a collection fusion algorithm for information gathering agents is presented. The difficult problem for this research is the case where collections may be used by more than one agent. This algorithm, however, uses the technique of cooperation between agents, and provides a solution for this difficult problem in distributed information retrieval systems. This thesis presents the solutions to the theoretical problems in agent-based information gathering systems, including information filtering models, agent belief modeling, and collection fusions. It also presents solutions to some of the technical problems in agent-based information systems, such as document classification, the architecture for agent-based information gathering systems, and the decision in multiple agent environments. Such kinds of information gathering agents will gather relevant information from highly distributed uncertain environments.