Here, There, and Everywhere: Building a Scaffolding for Children’s Learning Through Recommendations

Reading and literacy are on the decline among children. This is compounded by the fact that children have trouble with the discovery of resources that are appropriate, diverse, and appealing. With technology becoming an evermore presence in children’s lives, tools that can minimize choice overload and ease access to online resources become a must. A powerful but underutilized tool in regards to children that could assist in this situation is a recommender system (RS). We posit that RS could be used to impact children’s learning, using them to not only suggest what children might like but what they need in regards to learning. At the same time, if scoped inappropriately, outcomes from RS could be used to alter children’s outlook. The goal instead is to strive for RS that offer suggestions based off children’s evolving knowledge, preferences, reading level, etc., so that with the proper intervention from an expert-in-the-loop (e.g., parents/teachers) could impact not only children’s educational performance, but help them to reach the goal of learning to learn

Listener Modeling and Context-aware Music Recommendation Based on Country Archetypes

Music preferences are strongly shaped by the cultural and socio-economic background of the listener, which is reflected, to a considerable extent, in country-specific music listening profiles. Previous work has already identified several country-specific differences in the popularity distribution of music artists listened to. In particular, what constitutes the "music mainstream" strongly varies between countries. To complement and extend these results, the article at hand delivers the following major contributions: First, using state-of-the-art unsupervised learning techniques, we identify and thoroughly investigate (1) country profiles of music preferences on the fine-grained level of music tracks (in contrast to earlier work that relied on music preferences on the artist level) and (2) country archetypes that subsume countries sharing similar patterns of listening preferences. Second, we formulate four user models that leverage the user's country information on music preferences. Among others, we propose a user modeling approach to describe a music listener as a vector of similarities over the identified country clusters or archetypes. Third, we propose a context-aware music recommendation system that leverages implicit user feedback, where context is defined via the four user models. More precisely, it is a multi-layer generative model based on a variational autoencoder, in which contextual features can influence recommendations through a gating mechanism. Fourth, we thoroughly evaluate the proposed recommendation system and user models on a real-world corpus of more than one billion listening records of users around the world (out of which we use 369 million in our experiments) and show its merits vis-a-vis state-of-the-art algorithms that do not exploit this type of context information.Comment: 30 pages, 3 tables, 12 figure

Report from Dagstuhl Seminar 23031: Frontiers of Information Access Experimentation for Research and Education

This report documents the program and the outcomes of Dagstuhl Seminar 23031 ``Frontiers of Information Access Experimentation for Research and Education'', which brought together 37 participants from 12 countries. The seminar addressed technology-enhanced information access (information retrieval, recommender systems, natural language processing) and specifically focused on developing more responsible experimental practices leading to more valid results, both for research as well as for scientific education. The seminar brought together experts from various sub-fields of information access, namely IR, RS, NLP, information science, and human-computer interaction to create a joint understanding of the problems and challenges presented by next generation information access systems, from both the research and the experimentation point of views, to discuss existing solutions and impediments, and to propose next steps to be pursued in the area in order to improve not also our research methods and findings but also the education of the new generation of researchers and developers. The seminar featured a series of long and short talks delivered by participants, who helped in setting a common ground and in letting emerge topics of interest to be explored as the main output of the seminar. This led to the definition of five groups which investigated challenges, opportunities, and next steps in the following areas: reality check, i.e. conducting real-world studies, human-machine-collaborative relevance judgment frameworks, overcoming methodological challenges in information retrieval and recommender systems through awareness and education, results-blind reviewing, and guidance for authors.Comment: Dagstuhl Seminar 23031, report