When the System Becomes Your Personal Docent: Curated Book Recommendations

Curation is the act of selecting, organizing, and presenting content most often guided by professional or expert knowledge. While many popular applications have attempted to emulate this process by turning users into curators, we put an accent on a recommendation system which can leverage multiple data sources to accomplish the curation task. We introduce QBook, a recommender that acts as a personal docent by identifying and suggesting books tailored to the various preferences of each individual user. The goal of the designed system is to address several limitations often associated with recommenders in order to provide diverse and personalized book recommendations that can foster trust, effectiveness of the system, and improve the decision making process. QBook considers multiple perspectives, from analyzing user reviews, user historical data, and items\u27 metadata, to considering experts\u27 reviews and constantly evolving users\u27 preferences, to enhance the recommendation process, as well as quality and usability of the suggestions. QBook pairs each generated suggestion with an explanation that (i) showcases why a particular book was recommended and (ii) helps users decide which items, among the ones recommended, will best suit their individual interests. Empirical studies conducted using the Amazon/LibraryThing benchmark corpus demonstrate the correctness of the proposed methodology and QBook\u27s ability to outperform baseline and state-of-the-art methodologies for book recommendations

Genre Prediction to Inform the Recommendation Process

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 counter-part

Anything Fun Going On?: A Simple Wizard to Avoid the Cold-Start Problem for Event Recommenders

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

Finding, Understanding and Learning: Making Information Discovery Tasks Useful for Children and Teachers

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. YouUnderstood.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 YouUnderstood.me and well-known (children-oriented) search engines based on queries generated by K-9 students, showcases the need for this type of environment

“One Size Doesn’t Fit All : Helping Users Find Events from Multiple Perspectives

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\u27 needs, each user is allowed to choose how to access these suggestions in one of four ways: search, categorized browsing, following, and traditional recommendations

From Recommendation to Curation: When the System Becomes Your Personal Docent

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

Exploiting Reviews to Generate Personalized and Justified Recommendations to Guide Users’ Selections

We introduce RUS, 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\u27 reviews and matrix factorization to generate recommendations that include reviewers\u27 opinions related to item characteristics that each individual user frequently mentions. To demonstrate the validity of RUS we use the Amazon dataset

Is Sven Seven? : A Search Intent Module for Children

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\u27s 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\u27s 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\u27s 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\u27s 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

Online Searching and Learning: \u3cem\u3eYUM\u3c/em\u3e and Other Search Tools for Children and Teachers

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