DST-HRS: A topic driven hybrid recommender system based on deep semantics

Recommender Systems (RS) provide customized suggestion to users for specific item across the bulk of identical data such as media recommendations, electronic commerce web pages & social networks. RSs are being developed using the methods such as Collaborative Filtering (CF) and Contents Based Filtering (CB). However, CF suffers from sparseness problem wherein user-to-item data is sparse and CB filtering depends on feature extraction methods for item descriptions that require knowledge of content semantics and context of RS. In order to deal with the sparsity problem, various matrix factorization techniques embedded with pre-processed auxiliary information are used. On the other hand, currently employed techniques of feature extraction lack in deep semantics of items textual information as they individually cover either the semantic details or topic information. This paper proposes a hybrid RS model called Deep Semantic based Topic driven Hybrid RS (DST-HRS) that employs item description semantics influenced by its topics information. The proposed model extracts the embeddings by capturing the semantics of textual information and incorporates topic details into it. It further integrates these embeddings into Probabilistic Matrix Factorization (PMF), thus efficiently exploiting the semantics of items textual information such as reviews, synopsis, comments, plots etc to overcome the sparseness issue. The proposed DST-HRS can easily be deployed with lesser computation and time complexity. The model is validated on freely available datasets including Amazon Instant Video (AIV) and Movielens (1M & 10M). The validation exhibited a better performance with respect to sparse ratings of user-to-item as compared to the state-of-the-art.