Thesis (Ph. D.)--Washington State University, August 2008. / Includes bibliographical references (p. 118-128).

Thesis (M.S.)--Oregon State University, 2000. / Typescript (photocopy). Includes bibliographical references (leaves 155-157). Also available on the World Wide Web.

Thesis (M.S.)--Rochester Institute of Technology, 2009. / Typescript. Includes bibliographical references (leaf 76).

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(1) Relational fusion of multiple features for the classical regression task (single measure and dimension). Originally, the task of recommender systems is formulated as a regression task. Many CF algorithms and fusion methods have been proposed. The limitation of previous fusion methods is that only local features are utilized and the global relational dependency is ignored, which would impair the performance of CF. We propose a relational fusion approach based on conditional random fields (CRF) to improve traditional fusion methods by incorporating global relational dependency. / (2) Fusion of regression-oriented and ranking-oriented algorithms for multi-measure adaption. Beyond the level of classical regression, ranking the items directly is another important task for recommender systems. A good algorithm should adapt to both regression-oriented and ranking-oriented measures. Traditionally, algorithms separately adapt to a single one, thus they cannot adapt to the other. We propose methods to combine them to improve the performances in both measures. / (3) Fusion of quality-based and relevance-based algorithms for multi-dimensional adaption. Recommender systems should consider the performances of multiple dimensions, such as quality and relevance. Traditional algorithms, however, only recommend either high-quality or high-relevance items. But they cannot adapt to the other dimension. We propose both fusion metrics and fusion approaches to effectively combine multiple dimensions for better performance in multi-dimensional recommendations. / (4) Investigation of impression efficiency optimization in recommendation. Besides performance, impression efficiency, which describes how much profit can be obtained per impression of recommendation, is also a very important issue. From recent study, over-quantity recommendation impression is intrusive to users. Thus the impression efficiency should be formulated and optimized. But this issue has rarely been investigated. We formulate the issue under the classical secretary problem framework and extend an online secretary algorithm to solve it. / Recommender systems are important nowadays. With the explosive growth of resources on the Web, users encounter information overload problem. The research issue of recommender systems is a kind of information filtering technique that suggests user-interested items (e.g., movies, books, products, etc.) to solve this problem. Collaborative filtering (CF) is the key approach. Over the decades, recommender systems have been demonstrated important in E-business. Thus designing accurate algorithms for recommender systems has attracted much attention. / This thesis is to investigate effective fusion-based approaches for recommender systems. Effective fusion of various features and algorithms becomes important along with the development of recommendation techniques. Because each feature/algorithm has its own advantages and disadvantages. A combination to get the best performance is desired in applications. The fusion-based approaches investigated are from the following four levels. / Xin, Xin. / Advisers: Wai Lam; Irwin Kuo Chin King; Michael Rung Tsong Lyu. / Source: Dissertation Abstracts International, Volume: 73-06, Section: B, page: . / Thesis (Ph.D.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (leaves 152-172). / 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, [201-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese.

This thesis covers open quantum systems and information transfer in the face of dissipation and disorder through numerical simulation. In Chapter 3 we present work on an open quantum system comprising a two-level system, single bosonic mode and dissipative environment; we have included the bosonic mode in the exact system treatment. This model allows us to gain an understanding of an environment's role in small energy transfer systems. We observe how the two-level system-mode coupling strength and the spectral density form characterising the environment interplay, affecting the system's coherent behaviour. We find strong coupling and a spectral density resonantly peaked on the two-level system oscillation frequency enhances the system's coherent oscillatory dynamics. Chapter 4 focusses on a physically motivated study of chain and ladder spin geometries and their use for entanglement transfer between qubits. We consider a nitrogen vacancy centre qubit implementation with nitrogen impurity spin-channels and demonstrate how matrix product operator techniques can be used in simulations of this physical system. We investigate coupling parameters and environmental decay rates with respect to transfer efficiency effects. Then, in turn, we simulate the effects of missing channel spins and disorder in the spin-spin coupling. We conclude by highlighting where our considered channel geometries outperform each other. The work in Chapter 5 is an investigation into the feasibility of routing entanglement between distant qubits in 2D spin networks. We no longer consider a physical implementation, but keep in mind the effects of dissipative environments on entanglement transfer systems. Starting with a single sending qubit-ancilla and multiple addressable receivers, we show it is possible to target a specific receiver and establish transferred entanglement between it and the sender's ancilla through eigenstate tunnelling techniques. We proceed to show that eigenstate tunnelling-mediated entanglement transfer can be achieved simultaneously from two senders across one spin network.

Thesis (Ph.D.) -- University of Texas at Arlington, 2009.

Thesis (Ph.D.)--University of Texas at Arlington, 2009.

Thesis (Ph. D.)--University of North Texas, Aug., 2009. / Title from title page display. Includes bibliographical references.

In this thesis, I propose a method for establishing a personalized recommendation system for re-ranking web search results and recommending web contents. The method is based on personal reading interest which can be reflected by the user’s dwell time on each document or webpage. I acquire document-level dwell times via a customized web browser, or a mobile device. To obtain better precision, I also explore the possibility of tracking gaze position and facial expression, from which I can determine the attractiveness of different parts of a document. Inspired by idea of Google Knowledge Graph, I also establish a graph-based ontology to maintain a user profile to describe the user’s personal reading interest. Each node in the graph is a concept, which represents the user’s potential interest on this concept. I also use the dwell time to measure concept-level interest, which can be inferred from document-level user dwell times. The graph is generated based on the Wikipedia. According to the estimated concept-level user interest, my algorithm can estimate a user’s potential dwell time over a new document, based on which personalized webpage re-ranking can be carried out. I compare the rankings produced by my algorithm with rankings generated by popular commercial search engines and a recently proposed personalized ranking algorithm. The results clearly show the superiority of my method. I also use my personalized recommendation framework in other applications. A good example is personalized document summarization. The same knowledge graph is employed to estimate the weight of every word in a document; combining with a traditional document summarization algorithm which focused on text mining, I could generate a personalized summary which emphasize the user’s interest in the document. To deal with images and videos, I present a new image search and ranking algorithm for retrieving unannotated images by collaboratively mining online search results, which consists of online images and text search results. The online image search results are leveraged as reference examples to perform content-based image search over unannotated images. The online text search results are used to estimate individual reference images’ relevance to the search query as not all the online image search results are closely related to the query. Overall, the key contribution of my method lies in its ability to deal with unreliable online image search results through jointly mining visual and textual aspects of online search results. Through such collaborative mining, my algorithm infers the relevance of an online search result image to a text query. Once I estimate a query relevance score for each online image search result, I can selectively use query specific online search result images as reference examples for retrieving and ranking unannotated images. To explore the performance of my algorithm, I tested it both on a standard public image datasets and several modestly sized personal photo collections. I also compared the performance of my method with that of two peer methods. The results are very positive, which indicate that my algorithm is superior to existing content-based image search algorithms for retrieving and ranking unannotated images. Overall, the main advantage of my algorithm comes from its collaborative mining over online search results both in the visual and the textual domains. / published_or_final_version / Computer Science / Doctoral / Doctor of Philosophy