We present the preliminary exploration we conducted to identify traits that can influence children's preferences in books. Findings offer insights for the design of recommender algorithms that would look beyond patterns inferred from traditional user-system interactions (e.g., ratings) for recommendation purposes, since when it comes to children such data is rarely, if at all, available.

Games play a significant role in modern society, since they affect people of all ages and all walks of life, whether it be socially or mentally, and have direct impacts on adults with autism. Autism spectrum disorders (ASD) are a collection of neurodevelopmental disorders characterized by qualitative impairments in social relatedness and interaction, as well as difficulties in acquiring and using communication and language abilities. Adults with ASD often find it difficult to express and recognize emotions which makes it hard for them to interact with others socially. We have designed new interactive and collaborative games for autistic adults and developed a novel strategy to recommend games to them. Using modern computer vision and graphics techniques, we (i) track the player's speech rate, facial features, eye contact, audio communication, and emotional states, and (ii) foster their collaboration. These games are personalized and recommended to a user based on games interested to the user, besides the complexity of games at different levels according to the deficient level of the emotional understanding and social skills to which the user belongs. The objective of developing and recommending short-head (i.e., familiar) and long-tail (i.e., unfamiliar) games for adults with ASD is to enhance their social interacting skills with peers so that they can live a better life.

An online exchange system is a web service that allows communities to trade items without the burden of manually selecting them, which saves users' time and effort. Even though online book-exchange systems have been developed, their services can further be improved by reducing the workload imposed on their users. To accomplish this task, we propose a recommendation-based book exchange system, called EasyEx, which identifies potential exchanges for a user solely based on a list of items the user is willing to part with. EasyEx is a novel and unique book-exchange system because unlike existing online exchange systems, it does not require a user to create and maintain a wish list, which is a list of items the user would like to receive as part of the exchange. Instead, EasyEx directly suggests items to users to increase serendipity and as a result expose them to items which may be unfamiliar, but appealing, to them. In identifying books to be exchanged, EasyEx employs known recommendation strategies, that is, personalized mean and matrix factorization, to predict book ratings, which are treated as the degrees of appeal to a user on recommended books. Furthermore, EasyEx incorporates OptaPlanner, which solves constraint satisfaction problems efficiently, as part of the recommendation-based exchange process to create exchange cycles. Experimental results have verified that EasyEx offers users recommended books that satisfy the users' interests and contributes to the item-exchange mechanism with a new design methodology.

Existing popular web search engines have been widely used for retrieving information of interests by their users and offer query suggestions (QS) to assist them in exploring the wealth of information online. These search tools, however, are designed without any specific group of users in mind and thus are not tailored towards the specific needs of children, which can diminish their usability and design objectives when they are employed by children. Given the increasing use of the Web for educational and entertainment purposes by children, there is an urgent need to help them search the Web effectively. In this paper, we present a QS module, denoted CQS, which assists children in finding appropriate query keywords to capture their information needs by (i) analyzing content written for/by children, (ii) examining phrases and other metadata extracted from reputable (children's) websites, and (iii) using a supervised learning approach to rank suggestions that are appealing to children. CQS offers suggestions with vocabulary that can be comprehended by children and with topics of interest to them. We conducted a number of empirical studies using keyword queries initiated by children, besides gathering feedback on the usefulness of CQS-generated suggestions through crowdsourcing. The performance evaluation of CQS revealed the effectiveness of the methodology of CQS. In addition, it demonstrated that CQS-generated suggestions were preferred over suggestions provided by Bing and Yahoo! and at least as comparable to queries suggested by Google.

The readability levels of books identify suitable reading materials. Unfortunately, the majority of published books are assigned a readability level range, which is not useful to readers who look for books at a particular grade level. Existing readability formulas/analysis tools require at least an excerpt of a book to estimate its readability level, which is a severe constraint, since copyright laws prohibit book contents from being made publicly accessible. To alleviate the constraint, we have developed TRoLL which relies on publicly accessible online book metadata, in addition to using a book's snippet, if it is available, to predict its readability level. Based on a multi-dimensional regression analysis, TRoLL determines the grade level of any book instantly, even without a sample of its text, and considers its topical suitability, which is unique. Furthermore, TRoLL is a significant contribution to the educational community, since its computed book readability levels can enrich K-12 readers' book selections and aid parents, teachers, and librarians in locating reading materials suitable for their K-12 readers, which can be a time-consuming and frustrating task that does not always yield a quality outcome. Conducted empirical studies have verified the prediction accuracy of TRoLL and demonstrated its superiority over well-known readability formulas/analysis tools.

Query suggestions (QS) tailored specifically for children are slowly gaining research attention in response to the growth in Internet use by children. Even though QS offered by popular search engines adequately meet the information needs of the general public, they do not achieve equivalent effectiveness from a child's perspective. This is because children's search behaviors, interests, cognitive levels, and ability to read and understand complex content are different from adults. Given the ubiquitous nature of the Web, its importance in today's society, and its increasing use in education, it is an urgent need to help children search the Web effectively. In this paper, we present a QS module, denoted CQS, which assists children in finding appropriate query keywords to capture their information needs by (i) analyzing content written for/by children, (ii) examining phrases and other metadata extracted from reputable (children's) websites, and (iii) using a supervised learning approach to rank suggestions that are appealing to children. CQS offers suggestions with vocabulary that can be comprehended by children and with topics of interest to them. Empirical studies conducted using keyword queries initiated by children, in addition to feedback gathered through crowdsourcing, have verified not only the effectiveness of CQS, but also the fact that children favor CQS-generated suggestions over the suggestions provided by Google, Yahoo!, and Bing.

We recognize that emergent literacy forms a foundation upon which children will gage their future reading.¹ It is imperative to motivate young readers to read by offering them appealing books to read so that they can enjoy reading and gradually establish a reading habit during their formative years that can aid in promoting their good reading habits. However, with the huge volume of existing and newly-published books, it is a challenge for parents/educators (young readers, respectively) to find the right ones that match children's interests and their readability levels. In response to the needs, we have developed K3Rec, a recommender which applies a multi-dimensional approach to suggest books that simultaneously match the interests/preferences and reading abilities of emergent (i.e., K-3) readers. K3Rec considers the grade levels, contents, illustrations, and topics, besides using special properties, such as length and writing style, to distinguish K-3 books from other books targeting more mature readers. K3Rec is novel, since it adopts an unsupervised strategy to suggest books for K-3 readers which does not rely on the existence of personal social media data, such as personal tags and ratings, that are seldom, if ever, created by emergent readers. Furthermore, unlike existing book recommenders, K3Rec explicitly analyzes book illustrations, which is of special significance for emergent readers, since illustrations assist these readers in understanding the contents of books. K3Rec focuses on a niche group of readers that has not been explicitly targeted by existing book recommenders. Empirical studies conducted using data from BiblioNasium.com and Amazon's Mechanical Turk have verified the effectiveness of K3Rec in making book recommendations for emergent readers.

We introduce EasyEx, a recommendation-based book exchange system which identifies potential exchanges for a user solely based on the user's item list. EasyEx is novel, since it effectively identifies books that are likely appealing to users, and optimizes book-exchange cycles to increase serendipity, instead of simply swapping books on users' wish lists as performed by existing item-exchange algorithms. Experimental results have verified that EasyEx offers users recommended books that satisfy their interests and contributes to the itemexchange mechanism with a new design methodology.

Existing scholarly publication recommenders were designed to aid researchers, as well as ordinary users, in discovering pertinent literature in diverse academic fields. These recommenders, however, often (i) depend on the availability of users' historical data in the form of ratings or access patterns, (ii) generate recommendations pertaining to users' (articles included in their) profiles, as oppose to their current research interests, or (iii) fail to analyze valuable user-generated data at social sites that can enhance their performance. To address these design issues, we propose PReSA, a personalized recommender on scholarly articles. PReSA recommends articles bookmarked by the connections of a user U on a social bookmarking site that are not only similar in content to a target publication P currently of interest to U but are also popular among U's connections.PReSA (i) relies on the content-similarity measure to identify potential academic publications to be recommended and (ii) uses only information readily available on popular social bookmarking sites to make recommendations. Empirical studies conducted using data from CiteULike have verified the efficiency and effectiveness of (the recommendation and ranking strategies of) PReSA, which outperforms a number of existing (scholarly publication) recommenders.

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.

K-12 teachers, especially the ones who teach reading and literacy, are expected to guide their students to read in order to learn. Teachers can promote good reading habits among K-12 readers by offering books that match their interests. Unfortunately, finding the right book for each individual or group of students is not an easy task due to the huge volume of books available these days that cover a diversity of topics at varied reading levels. To address this problem, we have developed BReT, a book recommender for K-12 teachers. BReT adopts a multi-dimensional strategy to suggest books that simultaneously match the interests, preferences, and reading abilities of K-12 students based on the content, topics, literary elements, and grade levels specified by a teacher. BReT is novel, since it recommends books to K-12 teachers tailored to their individual students or groups of students, either for pleasure reading or fulfilling their current instructional activities. Unlike existing book-searching tools currently being used by teachers, which adopt a "one-size-fits-all" strategy, BReT offers personalized suggestions. Conducted empirical studies using Mechanical Turk have verified the effectiveness of BReT in making book recommendations.

It is imperative to motivate children to read by offering them appealing books so that they can gradually establish a reading habit during their formative years. However, with the huge volume of existing and newly-published books, it is a challenge to find the right ones that match children's interests and readability levels. In response to the needs, we have developed K3Rec, an unsupervised recommender, which suggests books that match the interests/preferences and reading abilities of emergent (i.e., K-3) readers.

Taking advantage of the popularity of the web, online marketplaces such as Ebay (.com), advertisements (ads for short) websites such as Craigslist(.org), and commercial websites such as Carmax(.com) (allow users to) post ads on a variety of products and services. Instead of browsing through numerous websites to locate ads of interest, web users would benefit from the existence of a single, fully integrated database (DB) with ads in multiple domains, such as Cars-for-Sale and Job-Postings, populated from various online sources so that ads of interest could be retrieved at a centralized site. Since existing ads websites impose their own structures and formats for storing and accessing ads, generating a uniform, integrated ads repository is not a trivial task. The challenges include (i) identifying ads domains, (ii) dealing with the diversity in structures of ads in various ads domains, and (iii) analyzing data with different meanings in each ads domain. To handle these problems, we introduce ADEx, a tool that relies on various machine learning approaches to automate the process of extracting (un-/semi-/fully- structured) data from online ads to create ads records archived in an underlying DB through domain classification, keyword tagging, and identification of valid attribute values. Experimental results generated using a dataset of 18,000 online ads originated from Craigslist, Ebay, and KSL(.com) show that ADEx is superior in performance compared with existing text classification, keyword labeling, and data extraction approaches. Further evaluations verify that ADEx either outperforms or performs at least as good as current state-of-the-art information extractors in mapping data from unstructured or (semi-)structured sources into DB records.

Commercial web search engines include a querysuggestion module so that given a user's keyword query, alternativesuggestions are offered and served as a guide to assistthe user in formulating queries which capture his/her intendedinformation need in a quick and simple manner. Majorityof these modules, however, perform an in-depth analysis oflarge query logs and thus (i) their suggestions are mostlybased on queries frequently posted by users and (ii) theirdesign methodologies cannot be applied to make suggestions oncustomized search applications for enterprises for which theirrespective query logs are not large enough or non-existent. To address these design issues, we have developed PQS, aprobabilistic query suggestion module. Unlike its counterparts, PQS is not constrained by the existence of query logs, sinceit solely relies on the availability of user-generated contentfreely accessible online, such as the Wikipedia.org documentcollection, and applies simple, yet effective, probabilistic-andinformation retrieval-based models, i.e., the Multinomial, BigramLanguage, and Vector Space Models, to provide usefuland diverse query suggestions. Empirical studies conductedusing a set of test queries and the feedbacks provided byMechanical Turk appraisers have verified that PQS makesmore useful suggestions than Yahoo! and is almost as goodas Google and Bing based on the relatively small difference inperformance measures achieved by Google and Bing over PQS.

Finding books that children/teenagers are interested in these days is a non-trivial task due to the diversity of topics covered in huge volumes of books with varied readability levels. Even though K-12 readers can turn to book recommenders to look for books, the recommended books may not satisfy their personal needs, since they could be beyond/below their readability levels or fail to match their topics of interest. To address these problems, we introduce BReK12, a book recommender that makes personalized suggestions tailored to each K-12 user U based on books available on a social bookmarking site that (i) are similar in content to the ones that are known to be of interest to U, (ii) have been bookmarked by users with reading patterns similar to U's, and (iii) can be comprehended by U. BReK12 is an asset to its users, since it suggests books that are appealing to its users and at grade levels that they can cope with, which can increase their reading selection choices and motivate them to read. We have also developed ReLAT, the readability analysis tool employed by BReK12 to determine the grade level of books. ReLAT is novel, compared with existing readability formulas, since it can predict the grade level of a book even if an excerpt of the book is not available. We have conducted empirical studies which have verified the accuracy of ReLAT in predicting the grade level of a book and the effectiveness of BReK12 over existing baseline recommendation systems.

People are gregarious by nature, which explains why group activities, from colleagues sharing a meal to friends attending a book club event together, are the social norm. Online group recommenders identify items of interest, such as restaurants, movies, and books, that satisfy the collective needs of a group (rather than the interests of individual group members). With a number of new movies being released every week, online recommenders play a significant role in suggesting movies for family members or groups of friends/people to watch, either at home or at movie theaters. Making group recommendations relevant to the joint interests of a group, however, is not a trivial task due to the diversity in preferences among group members. To address this issue, we introduce GroupReM which makes movie recommendations appealing (to a certain degree) to members of a group by (i) employing a merging strategy to explore individual group members' interests in movies and create a profile that reflects the preferences of the group on movies, (ii) using wordcorrelation factors to find movies similar in content, and (iii) considering the popularity of movies at a movie website. Unlike existing group recommenders based on collaborative filtering (CF) which consider ratings of movies to perform the recommendation task, GroupReM primarily employs (personal) tags for capturing the contents of movies considered for recommendation and group members' interests. The design of GroupReM, which is simple and domain-independent, can easily be extended to make group recommendations on items other than movies. Empirical studies conducted using more than 3000 groups of different users in the MovieLens dataset, which are various in terms of numbers and preferences in movies, show that GroupReM is highly effective and efficient in recommending movies appealing to a group. Experimental results also verify that GroupReM outperforms popular CF-based recommenders in making group recommendations.

RSS news articles that are either partially or completely duplicated in content are easily found on the Internet these days, which require Web users to sort through the articles to identify non-redundant information. This manual-filtering process is time-consuming and tedious. In this paper, we present a new filtering and clustering approach, called FICUS, which starts with identifying and eliminating redundant RSS news articles using a fuzzy set information retrieval approach and then clusters the remaining non-redundant RSS news articles according to their degrees of resemblance. FICUS uses a tree hierarchy to organize clusters of RSS news articles. The contents of the respective clusters are captured by the representative keywords from RSS news articles in the clusters so that searching and retrieval of similar RSS news articles is fast and efficient. FICUS is simple, since it uses the pre-defined word-correlation factors to determine related (words in) RSS news articles and filter redundant ones, and is supported by well-known and yet simple mathematical models, such as the standard deviation, vector space model, and probability theory, to generate clusters of non-redundant RSS news articles. Experiments performed on (test sets of) RSS news articles on various topics, which were downloaded from different online sources, verify the accuracy of FICUS on eliminating redundant RSS news articles, clustering similar RSS news articles together, and segregating different RSS news articles in terms of their contents. In addition, further empirical studies show that FICUS outperforms well-known approaches adopted for clustering RSS news articles.

Learning to read efficiently and effectively is emphasized at the elementary and high school levels. Finding books that children/youth are interested in reading, however, is a nontrivial task due to the diversity of topics and different readability levels covered in the huge volume of books available these days. Ideally, K-12 students can turn to book recommenders which suggest books that match their interests. However, since the preferences and reading levels of these students vary from one grade to another, books suggested by existing recommenders, which ignore the literacy skills and the personal interests of their users, may be unsuitable for the targeted audience. In this paper, we present additional design issues that should be applied in developing a book recommender based on BReK12, our previously-proposed book recommender for K-12 users, to further enhance the quality of its recommendations. BReK12, which performs content and readability analysis to identify books potentially appealing to its users, is extended to incorporate (i) a multi-criteria analysis that studies its users' complex and diverse interests and (ii) an enhanced readability-detection tool that determines precisely the readability levels of books which match the literary skills of its users.

Ideally, students in K-12 grade levels can turn to book recommenders to locate books that match their interests. Existing book recommenders, however, fail to take into account the readability levels of their users, and hence their recommendations may be unsuitable for the users. To address this issue, we introduce BReK12, a recommender that targets K-12 users and prioritizes the reading level of its users in suggesting books of interest. Empirical studies conducted using the Bookcrossing dataset show that BReK12 outperforms a number of existing recommenders (developed for general users) in identifying books appealing to K-12 users.

Taking advantage of the Web, many advertisements (ads for short) websites, which aspire to increase client's transactions and thus profits, offer searching tools which allow users to (i) post keyword queries to capture their information needs or (ii) invoke form-based interfaces to create queries by selecting search options, such as a price range, filled-in entries, check boxes, or drop-down menus. These search mechanisms, however, are inadequate, since they cannot be used to specify a natural-language query with rich syntactic and semantic content, which can only be handled by a question answering (QA) system. Furthermore, existing ads websites are incapable of evaluating arbitrary Boolean queries or retrieving partiallymatched answers that might be of interest to the user whenever a user's search yields only a few or no results at all. In solving these problems, we present a QA system for ads, called CQAds, which (i) allows users to post a natural-language question Q for retrieving relevant ads, if they exist, (ii) identifies ads as answers that partially-match the requested information expressed in Q, if insufficient or no answers to Q can be retrieved, which are ordered using a similarity-ranking approach, and (iii) analyzes incomplete or ambiguous questions to perform the "best guess" in retrieving answers that "best match" the selection criteria specified in Q. CQAds is also equipped with a Boolean model to evaluate Boolean operators that are either explicitly or implicitly specified in Q, i.e., with or without Boolean operators specified by the users, respectively. CQAds is easy to use, scalable to all ads domains, and more powerful than search tools provided by existing ads websites, since its query-processing strategy retrieves relevant ads of higher quality and quantity. We have verified the accuracy of CQAds in retrieving ads on eight ads domains and compared its ranking strategy with other well-known ranking approaches.