Popular search engines are usually tuned to satisfy the information needs of a general audience. As a result, non-traditional, yet active groups of users, such as children, experience challenges composing queries that can lead them to the retrieval of adequate results. To aid young users in formulating keyword queries that can facilitate their information-seeking process, we introduce ReQuIK, a multi-perspective query suggestion system for children. ReQuIK informs its suggestion process by applying (i) a strategy based on search intent to capture the purpose of a query, (ii) a ranking strategy based on a wide and deep neural network that considers both raw text and traits commonly associated with kid-related queries, (iii) a filtering strategy based on the readability levels of documents potentially retrieved by a query to favor suggestions that trigger the retrieval of documents matching children’s reading skills, and (iv) a content-similarity strategy to ensure diversity among suggestions. For assessing the quality of the system, we conducted initial offline and online experiments based on 591 queries written by 97 children, ages 6 to 13. The results of this assessment verified the correctness of ReQuIK’s recommendation strategy, the fact that it provides suggestions that appeal to children and ReQuIK’s ability to recommend queries that lead to the retrieval of materials with readability levels that correlate with children’s reading skills.

Curation is the act of selecting, organizing, and presenting content. Some applications emulate this process by turning users into curators, while others use recommenders to select items, seldom achieving the focus or selectivity of human curators. We bridge this gap with a recommendation strategy that more closely mimics the objectives of human curators. We consider multiple data sources to enhance the recommendation process, as well as the quality and diversity of the provided suggestions. Further, we pair each suggestion with an explanation that showcases why a book was recommended with the aim of easing the decision making process for the user. Empirical studies using Social Book Search data demonstrate the effectiveness of the proposed methodology.

Information discovery tasks using online search tools are performed on a regular basis by school-age children. However, these tools are not necessarily designed to both explicitly facilitate the retrieval of resources these young users can comprehend and aid low-literacy searchers. This is of particular concern for educational environments, as there is an inherent expectation that these tools facilitate effective learning. In this manuscript we present an initial assessment conducted over (1) children-oriented search tools based on queries generated by K-9 students, analyzing features such as readability and adequacy of retrieved results, and (2) tools used by teachers in their classrooms, analyzing their main purpose and target audience’s age range. Among the examined tools, we include YouUnderstood.Me, an enhanced search environment, which is the result of our ongoing efforts on the development of a search environment tailored to 5-15 year-olds that can foster learning through the retrieval of materials that not only satisfy the information needs of these users but also match their reading abilities. The results of these studies highlight the fact that search results presented to children have average reading levels that do not match the target audience. In addition, tools oriented to teachers do not go beyond showing the progress of their students, and seldomly provide a simple way of retrieving class contents that fit current needs of students. These facts further showcase the need for developing a dual environment oriented to both teachers and students.

We introduce BUS, a recommender that assists users by providing personalized and justified suggestions to facilitate the task of deciding which items, among the recommended ones, are best tailored towards their individual interests. We exploit users' reviews and matrix factorization to generate recommendations that include reviewers' opinions related to item characteristics that each individual user frequently mentions. To demonstrate the validity of RUS we use the Amazon dataset.

The Internet is the biggest data-sharing platform, comprised of an immeasurable quantity of resources covering diverse topics appealing to users of all ages. Children shape tomorrow's society, so it is essential that this audience becomes agile with searching information. Although young users prefer well-known search engines, their lack of skill in formulating adequate queries and the fact that search tools were not designed explicitly with children in mind, can result in poor outcomes. The reasons for this include children's limited vocabulary, which makes it challenging to articulate information needs using short queries, or their tendency to create queries that are too long, which translates to few or irrelevant retrieved results. To enhance web search environments in response to children's behaviors and expectations, in this paper we discuss an initial effort to verify well-known issues, and identify yet to be explored ones, that affect children in formulating (natural language or keyword) queries. We also present a novel search intent module developed in response to these issues, which can seamlessly be integrated with existing search engines favored by children. The proposed module interprets a child's query and creates a shorter and more concise query to submit to a search engine, which can lead to a more successful search session. Initial experiments conducted using a sample of children queries validate the correctness of the proposed search intent module.

In this paper we present a time-based genre prediction strategy that can inform the book recommendation process. To explicitly consider time in predicting genres of interest, we rely on a popular time series forecasting model as well as reading patterns of each individual reader or group of readers (in case of libraries or publishing companies). Based on a conducted initial assessment using the Amazon dataset, we demonstrate our strategy outperforms its baseline counterpart.

In this demo, we showcase a set up wizard designed to bypass the cold start problem that often affects recommendation systems in the event domain. We have developed a mobile application for tourists, RelEVENT, which allows them to quickly and non-intrusively set up preferences and/or interests related to events. This will directly affect the degree to which they can receive personalized recommendations on-the-fly and become aware of events happening around town that might be appealing to them.

We present our ongoing efforts on the development of a search environment tailored to 6-15 year-olds that can foster learning though retrieval of materials that not only satisfy the information needs of users but also match their reading abilities. You Understood.me is an enhanced environment based on a popular search engine specifically designed to help students deal with search for learning tasks, and allow teachers to track their progress. An initial assessment conducted on You Understood.me and well-known (children-oriented) search engines based on queries generated by K-9 students, showcases the need for this type of environment.

In this demo, we showcase a novel mobile application that offers various ways to present recommendations to users. While the majority of the existing applications in the tourism domain either focus on event recommendation or event browsing, our mobile application acknowledges the fact that users have different interests at different times and for different occasions. Consequently, while suggested events are filtered and ranked by proximity and date ranges to ensure they suit users' needs, each user is allowed to choose how to access these suggestions in one of four ways: search, categorized browsing, following, and traditional recommendations.

We introduce HRS, a recommender that exploits user reviews and identifies the features that are most likely appealing to users. HRS incorporates this knowledge into the recommendation process to generate a list of top-k recommendations, each of which is paired with an explanation that (i) showcases why a particular item was recommended and (ii) helps users decide which items, among the ones recommended, are best tailored towards their individual interests. Empirical studies conducted using the Amazon dataset demonstrate the correctness of the proposed methodology.