A hybrid recommendation technique using topic embedding for rating prediction and to handle cold-start problem

Abstract

Recommender systems aim to estimate item ratings and recommend items based on the users’ interests. The traditional recommender systems generally consider user–item rating information for rating prediction, but they suffer from various limitations, such as data sparsity, black-box recommendation, and cold-start problems. As a result, researchers have proposed amalgamating contextual information with rating data to provide effective recommendations. Although user-generated data in the form of reviews are a rich source of contextual information, they are rarely utilized in recommender algorithms. This study presents a hybrid recommendation technique, called RecTE, using rating data and topic embedding, which is an amalgamation of word embedding and topic modeling techniques. The novelty of RecTE lies in predicting item ratings using topic embeddings learned by incorporating local and global contextual information and integrating them with user-based collaborative filtering. RecTE is empirically evaluated over three real-world datasets – YelpNYC, YelpZip and TripAdvisor. This technique performs significantly better in comparison to nine baselines and five state-of-the-art recommendation techniques. On empirical analysis, we found that incorporating topic embedding in RecTE makes it capable of performing significantly better and handle cold-start problems effectively in comparison to the existing recommendation approaches.