A recommender system is designed to suggest items that are expected to interest a user. In order to filter the items and produce the recommendation, Data Mining techniques are largely employed. Among the most popular recommendation approaches in the literature and in real-world applications (e.g., e-commerce websites) are the so-called content-based recommender systems. Content-based recommender systems suggest to users items that are similar to those they previously evaluated. The early systems used relatively simple retrieval models, such as the Vector Space Model, with the basic TF-IDF weighting. 

Simple (word-based) interest descriptions may fall short both because of semantic ambiguity and because they lack of generality. Recently, content-based recommender systems evolved and started employing external knowledge sources (e.g., ontologies) to improve accuracy and scope of recommendations. 

More recent approaches have been based on deep learning. Other approaches, such as, have employed word embeddings in the recommendation process. Among the best known and high-performance implementations following these lines of research we mention Google's word2vec. 

Given the rapid advances of Semantic Technologies, there is still a large number of options for recommender systems to take advantage of semantics.