Print Email Facebook Twitter Towards Analogy-based Recommendation Title Towards Analogy-based Recommendation: Benchmarking of Perceived Analogy Semantics Author Lofi, C. (TU Delft Web Information Systems) Tintarev, N. (TU Delft Web Information Systems) Contributor Bogers, T. (editor) Koolen, M. (editor) Mobasher, B. (editor) Said, A. (editor) Tuzhilin, A. (editor) Date 2017 Abstract Requests for recommendation can be seen as a form of query for candidate items, ranked by relevance. Users are however o‰enunable to crisply de€ne what they are looking for. One of the core concepts of natural communication for describing and explainingcomplex information needs in an intuitive fashion are analogies: e.g., “What is to Christopher Nolan as is 2001: A Space Odyssey toStanley Kubrick?”. Analogies allow users to explore the item space by formulating queries in terms of items rather than explicitlyspecifying the properties that they €nd aŠractive. One of the core challenges which hamper research on analogy-enabled queries isthat analogy semantics rely on consensus on human perception, which is not well represented in current benchmark data sets. Œerefore, in this paper we introduce a new benchmark dataset focusing on the human aspects for analogy semantics. Furthermore, we evaluate a popular technique for analogy semantics (word2vec neuronal embeddings) using our dataset. Œe results show that current word embedding approaches are still not not suitable to su�ciently deal with deeper analogy semantics. We discuss future directions including hybrid algorithms also incorporating structural or crowd-based approaches, and the potential for analogy-based explanations. Subject Analogy-Enabled RecommendationRelational SimilarityAnalogy Benchmarking To reference this document use: http://resolver.tudelft.nl/uuid:5581c974-167b-4929-8f84-03b8ee56a7a3 Publisher CEUR-WS Source ComplexRec 2017 Recommendation in Complex Scenarios: Proceedings of the RecSys 2017 Workshop on Recommendation in Complex Scenarios Event RecSys 2017 Workshop on Recommendation in Complex Scenarios, 2017-08-31, Como, Italy Series Ceur Workshop Proceedings, 1613-0073, 1892 Part of collection Institutional Repository Document type conference paper Rights © 2017 C. Lofi, N. Tintarev Files PDF paper2.pdf 536.96 KB Close viewer /islandora/object/uuid:5581c974-167b-4929-8f84-03b8ee56a7a3/datastream/OBJ/view