Word Embeddings for Wine Recommender Systems Using Vocabularies of Experts and Consumers
Year: 2018
Type: article
Abstract: This vision paper proposes an approach to use the most advanced word embeddings techniques to bridge the gap between the discourses of experts and non-experts and more specifically the terminologies used by the two communities. Word embeddings makes it possible to find equivalent terms between experts and non-experts, by approach the similarity between words or by revealing hidden semantic relations. Thus, these controlled vocabularies with these new semantic enrichments are exploited in a hybrid recommendation system incorporating content-based ontology and keyword-based ontology to obtain relevant wines recommendations regardless of the level of expertise of the end user. The major aim is to find a non-expert vocabulary from semantic rules to enrich the knowledge of the ontology and improve the indexing of the items (i.e. wine) and the recommendation process.
Source: HAL
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2889732350