Abstract
Requests for recommendation can be seen as a form of query for candidate items, ranked by relevance. Users are however o‰en
unable to crisply de€ne 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 aŠractive. 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. 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.
unable to crisply de€ne 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 aŠractive. 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. 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.
| Original language | English |
|---|---|
| Title of host publication | ComplexRec 2017 Recommendation in Complex Scenarios |
| Subtitle of host publication | Proceedings of the RecSys 2017 Workshop on Recommendation in Complex Scenarios |
| Editors | T. Bogers, M. Koolen, B. Mobasher, A. Said, A. Tuzhilin |
| Publisher | CEUR-WS |
| Pages | 9-13 |
| Number of pages | 5 |
| Publication status | Published - 2017 |
| Event | RecSys 2017 Workshop on Recommendation in Complex Scenarios: co-located with 11th ACM Conference on Recommender Systems (RecSys 2017) - Como, Italy Duration: 31 Aug 2017 → 31 Aug 2017 |
Publication series
| Name | Ceur Workshop Proceedings |
|---|---|
| Publisher | CEUR |
| Volume | 1892 |
| ISSN (Electronic) | 1613-0073 |
Workshop
| Workshop | RecSys 2017 Workshop on Recommendation in Complex Scenarios |
|---|---|
| Country/Territory | Italy |
| City | Como |
| Period | 31/08/17 → 31/08/17 |
Keywords
- Analogy-Enabled Recommendation
- Relational Similarity
- Analogy Benchmarking