Towards Analogy-based Recommendation
نویسندگان
چکیده
Requests for recommendation can be seen as a form of query for candidate items, ranked by relevance. Users are however oen unable to crisply dene what they are looking for. One of the core concepts of natural communication for describing and explaining complex information needs in an intuitive fashion are analogies: e.g., “What is to Christopher Nolan as is 2001: A Space Odyssey to Stanley Kubrick?”. Analogies allow users to explore the item space by formulating queries in terms of items rather than explicitly specifying the properties that they nd aractive. One of the core challenges which hamper research on analogy-enabled queries is that analogy semantics rely on consensus on human perception, which is not well represented in current benchmark data sets. erefore, 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. e results show that current word embedding approaches are still not not suitable to suciently 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.
منابع مشابه
Towards Analogy-based Recommendation: Benchmarking of Perceived Analogy Semantics
Requests for recommendation can be seen as a form of query for candidate items, ranked by relevance. Users are however oen unable to crisply dene what they are looking for. One of the core concepts of natural communication for describing and explaining complex information needs in an intuitive fashion are analogies: e.g., “What is to Christopher Nolan as is 2001: A Space Odyssey to Stanley Ku...
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