Enriching Non-negative Matrix Factorization with Contextual Embeddings for Recommender Systems

Recommender Systems (RS) are used to give customized recommendations about specific items to users in a variety of applications including social web sites applications, media recommendation and commerce sites etc. Collaborative Filtering (CF) along with the Content Based Filtering (CBF) are two widely used methods which are being efficiently applied in RS implementation. CF suffers with sparseness problem where user-to-item ratings are amply sparse. On the other hand, CBF performance rely on methods of feature extraction for efficient use of items' description. The sparseness of user-to-item ratings and features extraction impede the performance of RS. Quality of rating prediction and item recommendation further degrades due to the negative values present in users/items latent factors. This paper proposes a novel RS that is built upon the semantics based items' content embedding model, enriched with contextual features extracted through Convolutional Neural Network (CNN). Non-negative Matrix Factorization (NMF), supplied with improvised embedding is used as CF technique. Embedding model captures the item details, thus resolving the sparsity, whereas, NMF caters for information loss due to negative values in latent factors. The proposed RS with contextually enriched NMF (Contx-NMF) simultaneously overcomes both the issues of sparseness and loss due to negative values, thus enhancing the rating prediction accuracy. The proposed model is evaluated on three public datasets (MovieLens 1M, MovieLens 10M)and Amazon Instant Video (AIV). The results demonstrate significant improvement in performance of Contx-NMF over state of the art RS models for sparse user-to-item ratings. (C) 2019 Elsevier B.V. All rights reserved.