Thesis (Ph. D.)--University of Chicago, Dept. of Computer Science, August 2001. / Includes bibliographical references. Also available on the Internet.

Thesis (Ph. D.)--University of Texas at Austin, 2002. / Vita. Includes bibliographical references. Available also from UMI Company.

Thesis (Ph. D.)--University of Texas at Austin, 2002. / Vita. Includes bibliographical references (leaves 137-145). Available also in a digital version from Dissertation Abstracts.

Thesis (Ph. D.)--University of Washington, 2002. / Vita. Includes bibliographical references (leaves 112-125).

Thesis (Ph. D.)--University of Texas at Austin, 2001. / Vita. Includes bibliographical references (leaves 318-328). Available also in a digital version from Dissertation Abstracts.

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Thesis (D.Tech. in Computer Systems Engineering) -- Tshwane University of Technology, 2012. / The general problem of generating recommendations from a recommender system for users is an arduous one. More arduous is the generation of recommendations for mobile users, because of the limitations of the mobile devices on which the recommendations are to be projected. Mobile devices with integrated support of camera can be used to offer online services to global community whenever and wherever they are located. The mobile user expects to receive a limited number of probable recommendations from a shopping recommender system in few seconds and must be approximately accurate to the mobile user's needs. In order to achieve this objective proposed client-server architecture for image content based shopping recommender system framework over wireless mobile devices was implemented. The image content shopping recommender system performed a query by external image captured by the mobile device's camera. It then generated a set of recommendations that is viewed on the mobile device using the Internet browser. The image content used to improve recommendations generation is the shape extracted using level sets and active contour without edge methods. An algorithm to represent the extracted shape content such that it will be invariant to Euclidean transform, affine transformation and robust to occlusion and clutter was found. The shape invariant content was then used to characterise sales item for effective recommendations generation. Suitable distance measure was used to evaluate the images' similarity for retrieval purpose on the content representation. Experimental results were generated and analyzed to test the efficacy of the shape content representation and matching algorithm. Finally the Image Content in Recommender System for Mobile Users is simulated and evaluated by users.

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