Abstract

Reading is a fundamental skill that each person needs to develop during early childhood and continue to enhance into adulthood. While children/teenagers depend on this skill to advance academically and become educated individuals, adults are expected to acquire a certain level of proficiency in reading so that they can engage in social/civic activities and successfully participate in the workforce. A step towards assisting individuals to become lifelong readers is to provide them adequate reading selections which can cultivate their intellectual and emotional growth. Turning to (web) search engines for such reading choices can be overwhelming, given the huge volume of reading materials offered as a result of a search. An alternative is to rely on reading materials suggested by existing recommendation systems, which unfortunately are not capable of simultaneously matching the information needs, preferences, and reading abilities of individual readers. In this dissertation, we present novel recommendation strategies which identify appealing reading materials that the readers can comprehend, which in turn can motivate them to read. In accomplishing this task, we have examined used-defined data, in addition to information retrieved/inferred from reputable and freely-accessible online sources. We have incorporated the concept of “social trust” when making recommendations for advanced readers and suggested fiction books that match the reading ability of individual K-12 readers using our readability-analysis tool for books. Furthermore, we have emulated the readers' advisory service offered at school/public libraries in making recommendations for K-12 readers, which can be applied to advanced readers as well. A major contribution of our work is in the development of unsupervised recommendation strategies for advanced readers which suggest reading materials for both entertainment and learning acquisition purposes. Unlike their counterparts, these recommendation strategies are unaffected by the cold-start or long-tail problems, since they exploit user-defined data (if available) while taking advantage of alternative publicly-available metadata. Our readability-analysis tool is innovative, which can predict the readability-levels of books on-the-fly, even in the absence of excerpts from books, a task that cannot be accomplished by any of the well-known readability tools/strategies. Moreover, our multi-dimensional recommendation strategy is novel, since it simultaneously analyzes the reading abilities of K-12 readers, which books readers enjoy, why the books are appealing to them, and what subject matters the readers favor. Besides assisting K-12 readers, our recommender can be used by parents/teachers/librarians in locating reading materials to be suggested to their (K-12) children/students/patrons. We have validated the performance of each methodology presented in this dissertation using existing benchmark datasets or datasets we created for the evaluation purpose (which is another contribution we make to the research community). We have also compared the performance of our proposed methodologies with their corresponding baselines and state-of-the-art counterparts, which further verifies the correctness of the proposed methodologies.

Degree

PhD

College and Department

Physical and Mathematical Sciences; Computer Science

Rights

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