Recommender Systems have received plenty of attention in the past decade. Given the possibility to interact with a catalog of millions of items on e-commerce companies such as Amazon, users can utilize the search functionality to query the needed items, or a system can be used to recommend items that the users may find relevant. Recommender systems are exploited to suggest movies on Netflix, music tracks on Spotify, Novels on Goodreads and hundreds of different items categories from different retailers and e-commerce companies.
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ELSAFTY, Ahmed, 2017. Document Similarity using Dense Vector Representation. pdf 
⇒ Doc2Vec
ALEXANDRIDIS, Georgios, SIOLAS, Georgios and STAFYLOPATIS, Andreas, 2018. ParVecMF: A Paragraph Vector-based Matrix Factorization Recommender System. 10 January 2018. arXiv. arXiv:1706.07513. arxiv .
Review-based recommender systems have gained noticeable ground in recent years. In addition to the rating scores, those systems are enriched with textual evaluations of items by the users. Neural language processing models, on the other hand, have already found application in recommender systems, mainly as a means of encoding user preference data, with the actual textual description of items serving only as side information. In this paper, a novel approach to incorporating the aforementioned models into the recommendation process is presented. Initially, a neural language processing model and more specifically the Paragraph Vector Model is used to encode textual user reviews of variable length into feature vectors of fixed length. Subsequently this information is fused along with the rating scores in a probabilistic matrix factorization algorithm, based on maximum a-posteriori estimation. The resulting system, ParVecMF, is compared to a ratings’ matrix factorization approach on a reference dataset. The obtained preliminary results on a set of two metrics are encouraging and may stimulate further research in this area.
arXiv:1706.07513 [cs]