@conference {10.1145/3308560.3316601,
	title = {From Free-Text User Reviews to Product Recommendation Using Paragraph Vectors and Matrix Factorization},
	booktitle = {Companion Proceedings of The 2019 World Wide Web Conference},
	series = {WWW {\textquoteright}19},
	year = {2019},
	pages = {335{\textendash}343},
	publisher = {Association for Computing Machinery},
	organization = {Association for Computing Machinery},
	address = {New York, NY, USA},
	abstract = {Recent theoretical and practical advances have led to the emergence of review-based recommender systems, where user preference data is encoded in at least two dimensions; the traditional rating scores in a predefined discrete scale and the user-generated reviews in the form of free-text. The main contribution of this work is the presentation of a new technique of incorporating those reviews into collaborative filtering matrix factorization algorithms. The text of each review, of arbitrary length, is mapped to a continuous feature space of fixed length, using neural language models and more specifically, the Paragraph Vector model. Subsequently, the resulting feature vectors (the neural embeddings) are used in combination with the rating scores in a hybrid probabilistic matrix factorization algorithm, based on maximum a-posteriori estimation. The proposed methodology is then compared to three other similar approaches on six datasets in order to assess its performance. The obtained results demonstrate that the new formulation outperforms the other systems on a set of two metrics, thereby indicating the robustness of the idea.},
	keywords = {Collaborative Filtering, Free-text reviews, Matrix Factorization, Maximum A-posteriori Estimation, Neural Language Processing, Paragraph Vectors, Recommender Systems, Word2Vec},
	isbn = {9781450366755},
	doi = {10.1145/3308560.3316601},
	url = {https://doi.org/10.1145/3308560.3316601},
	author = {Alexandridis, Georgios and Tagaris, Thanos and Siolas, Giorgos and Stafylopatis, Andreas}
}
