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publications
MindReader: Recommendation over Knowledge Graph Entities with Explicit User Ratings
Published in CIKM: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, 2020
We introduce the MindReader dataset, providing explicit user ratings for both items and KG entities, collected from over 1,174 users in the movie domain. Our study demonstrates that incorporating non-item ratings enhances recommendation quality in various models, highlighting the potential of using explicit ratings on KG entities for improving both warm and cold-start recommendations.
Recommended citation: A. H. Brams*, A. L. Jakobsen*, T. E. Jendal*, M. Lissandrini, P. Dolog, and K. Hose, “Mindreader: Recommendation over knowledge graph entities with explicit user ratings,” in CIKM, 2020
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GInRec: A Gated Architecture for Inductive Recommendation using Knowledge Graphs
Published in Proceedings of the Fifth Knowledge-aware and Conversational Recommender Systems Workshop (KaRS), 2023
We propose GInRec, a state-of-the-art method using a graph neural network with relation-specific gates and a knowledge graph, to provide better recommendations for new users and items, outperforming existing inductive methods. Our work reevaluates state-of-the-art methods, identifies improved evaluation protocols, and highlights flaws in previous conclusions.
Recommended citation: T. E. Jendal, M. Lissandrini, P. Dolog, and K. Hose, “GInRec: A gated architecture for inductive recommendation using knowledge graphs,” in KaRS, 2023.
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Knowledge graph embeddings: open challenges and opportunities
Published in Transactions on Graph Data and Knowledge, 2023
This study surveys advances and challenges in Knowledge Graph embeddings, highlighting the focus on structural information and recent progress in incorporating semantics, multi-modal, temporal, and multilingual features, while also addressing limitations such as neglecting real-world applications, handling dynamic changes, and data biases.
Recommended citation: R. Biswas, L.A. Kaffee, M. Cochez, S. Dumbrava, T.E. Jendal, et al.. "Knowledge graph embeddings: open challenges and opportunities," in TGDK, 2023
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Hypergraphs with Attention on Reviews for Explainable Recommendation
Published in ECIR: European Conference on Information Retrieval, 2024
We propose a novel review-specific Hypergraph (HG) model to capture high-order connections in review data and introduce a model-agnostic explainability module to clarify recommendations. Our experiments confirm the HG model's effectiveness in providing explanations on real-world datasets.
Recommended citation: T.E. Jendal, TH. Le, H.W. Lauw, M. Lissandrini, P. Dolog, K. Hose, "Hypergraphs with Attention on Reviews for Explainable Recommendation," in ECIR, 2024
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talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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