As education adopts digital platforms, the vast amount of information from various sources, such as learning management systems and learning object repositories, presents challenges in navigation and elaboration. Traditional interfaces involve a steep learning curve, limited user accessibility, and lack flexibility. Language models alone cannot address these issues as they do not have access to structured information specific to the educational organization. In this paper, we propose EDGE (EDucational knowledge Graph Explorer), a natural language interface that uses knowledge graphs to organize educational information. EDGE translates natural language requests into queries and converts the results back into natural language responses. We show EDGE's versatility using knowledge graphs built from public datasets, providing example interactions of different stakeholders. Demo video: https://u.garr.it/eYq63.

Current recommendation methods based on knowledge graphs rely on entity and relation representations for several steps along the pipeline, with knowledge completion and path reasoning being the most influential. Despite their similarities, the most effective representation methods for these steps differ, leading to inefficiencies, limited representativeness, and reduced interpretability. In this paper, we introduce KGGLM, a decoder-only Transformer model designed for generalizable knowledge representation learning to support recommendation. The model is trained on generic paths sampled from the knowledge graph to capture foundational patterns, and then fine-tuned on paths specific of the downstream step (knowledge completion and path reasoning in our case). Experiments on ML1M and LFM1M show that KGGLM beats twenty-two baselines in effectiveness under both knowledge completion and recommendation. Source code and pre-processed data sets are available at https://github.com/mirkomarras/kgglm.

extended-abstract Share on Demystifying Recommender Systems: A Multi-faceted Examination of Explanation Generation, Impact, and Perception Author: Giacomo Balloccu Department of Mathematics and Informatics, University of Cagliari, Italy Department of Mathematics and Informatics, University of Cagliari, Italy 0000-0002-6857-7709View Profile Authors Info & Claims RecSys '23: Proceedings of the 17th ACM Conference on Recommender SystemsSeptember 2023Pages 1361–1363https://doi.org/10.1145/3604915.3608887Published:14 September 2023Publication History 0citation60DownloadsMetricsTotal Citations0Total Downloads60Last 12 Months60Last 6 weeks60 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

The goal of this tutorial is to present the RecSys community with recent advances on explainable recommender systems with knowledge graphs. We will first introduce conceptual foundations, by surveying the state of the art and describing real-world examples of how knowledge graphs are being integrated into the recommendation pipeline, also for the purpose of providing explanations. This tutorial will continue with a systematic presentation of algorithmic solutions to model, integrate, train, and assess a recommender system with knowledge graphs, with particular attention to the explainability perspective. A practical part will then provide attendees with concrete implementations of recommender systems with knowledge graphs, leveraging open-source tools and public datasets; in this part, tutorial participants will be engaged in the design of explanations accompanying the recommendations and in articulating their impact. We conclude the tutorial by analyzing emerging open issues and future directions. Website: https://explainablerecsys.github.io/recsys2022/.

Existing explainable recommender systems have mainly modeled relationships between recommended and already experienced products, and shaped explanation types accordingly (e.g., movie "x" starred by actress "y" recommended to a user because that user watched other movies with "y" as an actress). However, none of these systems has investigated the extent to which properties of a single explanation (e.g., the recency of interaction with that actress) and of a group of explanations for a recommended list (e.g., the diversity of the explanation types) can influence the perceived explaination quality. In this paper, we conceptualized three novel properties that model the quality of the explanations (linking interaction recency, shared entity popularity, and explanation type diversity) and proposed re-ranking approaches able to optimize for these properties. Experiments on two public data sets showed that our approaches can increase explanation quality according to the proposed properties, fairly across demographic groups, while preserving recommendation utility. The source code and data are available at https://github.com/giacoballoccu/explanation-quality-recsys.