OAUC's participation in the CLEF2015 SBS Search Suggestion Track

In this article we describe the OAUC's participation in the CLEF 2015 SBS Search Suggestion track. We are trying to represent appeal elements, used in readers' advisory theory and practice, to see if they can be used in an automatic retrieval and recommendation context. We are starting out with the pace appeal element, used on fiction to representing how quickly a buildup of the story is. The results so far indicate that much tuning is needed when building models that can represent pace

Enhancing Children’s Experience with Recommendation Systems

Recommender Systems (RSs) offer a personalized support in exploring large amounts of information, assisting users in decision making about products matching their taste and preferences. Most of the research todate on recommender systems have focused on traditional users, i.e., adult individuals who are able to offer explicit feedback, write reviews, or purchase items themselves. However, children's patterns of attention and interaction are quite different from those of adults. This paper presents the first results of a research-in-progress that can be suited to bridge the barrier between children and a recom-mender system by providing a child-friendly interaction paradigm. Specifically, a web application is developed that employs real-time object recognition on movie thumbnails or DVD cover-photos in a real-time manner. The tangible object can be manipulated by the user and provide input to the system for the purpose of generating movie recommendations. We plan to extend this work to the scenario where the child could ask for a video content showing a related toy (e.g., a car, a plane, the doll of a character that she likes in a cartoon) and the system could generate the videos that matches these implicit preferences expressed by the chil

International and Interdisciplinary Perspectives on Children & Recommender Systems (KidRec)

Resources for children are abundant, but finding suitable and appropriate resources for children in our information-rich society can be challenging. Due to this abundance of information, systems to find and recommend appropriate information for children are needed. Recommender systems (RS) for children have only recently begun to be researched. This area of research brings together researchers in education, child-development, computer scientists, designers, and more who address several issues including those related to education, algorithms, ethics, privacy, security. In this workshop we will: discuss and identify issues related to RS designed for children including challenges and limitations, discuss possible solutions to the identified challenges and plan for future research, and of critical importance work to build a community that explicitly looks at these critical issues. This workshop has a specific theme of educationally-related recommendations, but welcomes other child-oriented recommender system contributions

Personalized Recommendation of PoIs to People with Autism

The suggestion of Points of Interest to people with Autism Spectrum Disorder (ASD) challenges recommender systems research because these users' perception of places is influenced by idiosyncratic sensory aversions which can mine their experience by causing stress and anxiety. Therefore, managing individual preferences is not enough to provide these people with suitable recommendations. In order to address this issue, we propose a Top-N recommendation model that combines the user's idiosyncratic aversions with her/his preferences in a personalized way to suggest the most compatible and likable Points of Interest for her/him. We are interested in finding a user-specific balance of compatibility and interest within a recommendation model that integrates heterogeneous evaluation criteria to appropriately take these aspects into account. We tested our model on both ASD and "neurotypical" people. The evaluation results show that, on both groups, our model outperforms in accuracy and ranking capability the recommender systems based on item compatibility, on user preferences, or which integrate these two aspects by means of a uniform evaluation model

Automating readers' advisory to make book recommendations for K-12 readers

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 in-dispensable to provide readers with engaging and motivat-ing reading selections. Unfortunately, existing book recom-menders have failed to offer adequate choices for K-12 read-ers, 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 recom-mender 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 reac-tions 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. Con-ducted empirical studies have shown that Rabbit outper-forms a number of (readability-based) book recommenders. Categories and Subject Descriptors Information Systems [Retrieval tasks and goals]: [Rec-ommender systems

Evaluating Recommender Systems for Technology Enhanced Learning: A Quantitative Survey

The increasing number of publications on recommender systems for Technology Enhanced Learning (TEL) evidence a growing interest in their development and deployment. In order to support learning, recommender systems for TEL need to consider specific requirements, which differ from the requirements for recommender systems in other domains like e-commerce. Consequently, these particular requirements motivate the incorporation of specific goals and methods in the evaluation process for TEL recommender systems. In this article, the diverse evaluation methods that have been applied to evaluate TEL recommender systems are investigated. A total of 235 articles are selected from major conferences, workshops, journals, and books where relevant work have been published between 2000 and 2014. These articles are quantitatively analysed and classified according to the following criteria: type of evaluation methodology, subject of evaluation, and effects measured by the evaluation. Results from the survey suggest that there is a growing awareness in the research community of the necessity for more elaborate evaluations. At the same time, there is still substantial potential for further improvements. This survey highlights trends and discusses strengths and shortcomings of the evaluation of TEL recommender systems thus far, thereby aiming to stimulate researchers to contemplate novel evaluation approaches.Laboratorio de Investigación y Formación en Informática Avanzad

Evaluating Recommender Systems for Technology Enhanced Learning: A Quantitative Survey

The increasing number of publications on recommender systems for Technology Enhanced Learning (TEL) evidence a growing interest in their development and deployment. In order to support learning, recommender systems for TEL need to consider specific requirements, which differ from the requirements for recommender systems in other domains like e-commerce. Consequently, these particular requirements motivate the incorporation of specific goals and methods in the evaluation process for TEL recommender systems. In this article, the diverse evaluation methods that have been applied to evaluate TEL recommender systems are investigated. A total of 235 articles are selected from major conferences, workshops, journals, and books where relevant work have been published between 2000 and 2014. These articles are quantitatively analysed and classified according to the following criteria: type of evaluation methodology, subject of evaluation, and effects measured by the evaluation. Results from the survey suggest that there is a growing awareness in the research community of the necessity for more elaborate evaluations. At the same time, there is still substantial potential for further improvements. This survey highlights trends and discusses strengths and shortcomings of the evaluation of TEL recommender systems thus far, thereby aiming to stimulate researchers to contemplate novel evaluation approaches.Laboratorio de Investigación y Formación en Informática Avanzad

Evaluating Recommender Systems for Technology Enhanced Learning: A Quantitative Survey

The increasing number of publications on recommender systems for Technology Enhanced Learning (TEL) evidence a growing interest in their development and deployment. In order to support learning, recommender systems for TEL need to consider specific requirements, which differ from the requirements for recommender systems in other domains like e-commerce. Consequently, these particular requirements motivate the incorporation of specific goals and methods in the evaluation process for TEL recommender systems. In this article, the diverse evaluation methods that have been applied to evaluate TEL recommender systems are investigated. A total of 235 articles are selected from major conferences, workshops, journals, and books where relevant work have been published between 2000 and 2014. These articles are quantitatively analysed and classified according to the following criteria: type of evaluation methodology, subject of evaluation, and effects measured by the evaluation. Results from the survey suggest that there is a growing awareness in the research community of the necessity for more elaborate evaluations. At the same time, there is still substantial potential for further improvements. This survey highlights trends and discusses strengths and shortcomings of the evaluation of TEL recommender systems thus far, thereby aiming to stimulate researchers to contemplate novel evaluation approaches.Laboratorio de Investigación y Formación en Informática Avanzad

Using implicit feedback for recommender systems: characteristics, applications, and challenges

Recommender systems are software tools to tackle the problem of information overload by helping users to find items that are most relevant for them within an often unmanageable set of choices. To create these personalized recommendations for a user, the algorithmic task of a recommender system is usually to quantify the user's interest in each item by predicting a relevance score, e.g., from the user's current situation or personal preferences in the past. Nowadays, recommender systems are used in various domains to recommend items such as products on e-commerce sites, movies and music on media portals, or people in social networks. To assess the user's preferences, recommender systems proposed in past research often utilized explicit feedback, i.e., deliberately given ratings or like/dislike statements for items. In practice, however, in many of today's application domains of recommender systems this kind of information is not existent. Therefore, recommender systems have to rely on implicit feedback that is derived from the users' behavior and interactions with the system. This information can be extracted from navigation or transaction logs. Using implicit feedback leads to new challenges and open questions regarding, for example, the huge amount of signals to process, the ambiguity of the feedback, and the inevitable noise in the data. This thesis by publication explores some of these challenges and questions that have not been covered in previous research. The thesis is divided into two parts. In the first part, the thesis reviews existing works on implicit feedback and recommender systems that exploit these signals, especially in the Social Information Access domain, which utilizes the "community wisdom" of the social web for recommendations. Common application scenarios for implicit feedback are discussed and a categorization scheme that classifies different types of observable user behavior is established. In addition, state-of-the-art algorithmic approaches for implicit feedback are examined that, e.g., interpret implicit signals directly or convert them to explicit ratings to be able to use "classic" recommendation approaches that were designed for explicit feedback. The second part of the thesis comprises some of the author's publications that deal with selected challenges of implicit feedback based recommendations. These contain (i) a specialized learning-to-rank algorithm that can differentiate different levels of interest indicator strength in implicit signals, (ii) contextualized recommendation techniques for the e-commerce domain that adapt product suggestions to customers' current short-term goals as well as their long-term preferences, and (iii) intelligent reminding approaches that aim at the re-discovery of relevant items in a customer's browsing history. Furthermore, the last paper of the thesis provides an in-depth analysis of different biases of various recommendation algorithms. Especially the popularity bias, the tendency to recommend mostly popular items, can be problematic in practical settings and countermeasures to reduce this bias are proposed