ParVecMF: A Paragraph Vector-based Matrix Factorization Recommender System
This is an incremental improvement for review-based recommender systems, potentially enhancing recommendation accuracy by better leveraging textual data.
The paper tackles the problem of improving recommender systems by incorporating textual user reviews through a paragraph vector model and fusing them with rating scores in a probabilistic matrix factorization algorithm, resulting in preliminary encouraging results compared to a baseline on a reference dataset.
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.