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As the number of RSS news feeds continue to increase over the Internet, it becomes necessary to minimize the workload of the user who is otherwise required to scan through huge number of news articles to find related articles of interest, which is a tedious and often an impossible task. In order to solve this problem, we present a novel approach, called InFRSS, which consists of a correlation-based phrase matching (CPM) model and a fuzzy compatibility clustering (FCC) model. CPM can detect RSS news articles containing phrases that are the same as well as semantically alike, and dictate the degrees of similarity of any two articles. FCC identifies and clusters non-redundant, closely related RSS news articles based on their degrees of similarity and a fuzzy compatibility relation. Experimental results show that (i) our CPM model on matching bigrams and trigrams in RSS news articles outperforms other phrase/keyword-matching approaches and (ii) our FCC model generates high quality clusters and outperforms other well-known clustering techniques.

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Children struggle with translating their information needs into effective queries to initiate the search process. In this paper, we explore the degree to which the use of a Vocal Assistant (VA) as an intermediary between a child and a search engine can ease query formulation and foster completion of successful searches. We also examine the potential influence VA can have on the search process when compared to a traditional keyboard-driven approach. This comparison motivates the second contribution of our work, an evaluation framework that covers 4 dimensions: (1) a new search strategy (VA) for (2) a specific user group (children) given (3) a particular task (answering questions) in (4) a defined environment (school). The proposed framework can be adopted by the research community to conduct comprehensive assessments of search systems given new interaction methods, user groups, contexts, and tasks.

The academic performance of students is affected by their reading ability, which explains why reading is one of the most important aspects of school curriculums. Promoting good reading habits among K-12 students is essential, given the enormous influence of reading on students' development as learners and members of society. In doing so, it is indispensable to provide readers with engaging and motivating reading selections. Unfortunately, existing book recommenders have failed to offer adequate choices for K-12 readers, since they either ignore the reading abilities of their users or cannot acquire the much-needed information to make recommendations due to privacy issues. To address these problems, we have developed Rabbit, a book recommender that emulates the readers' advisory service offered at school/public libraries. Rabbit considers the readability levels of its readers and determines the facets, i.e., appeal factors, of books that evoke subconscious, emotional reactions on a reader. The design of Rabbit is unique, since it adopts a multi-dimensional approach to capture the reading abilities, preferences, and interests of its readers, which goes beyond the traditional book content/topical analysis. Conducted empirical studies have shown that Rabbit outperforms a number of (readability-based) book recommenders.