Sentiment analysis based distributed recommendation system

The recommendation system assists in selecting the best product among the millions of products available on various e-commerce sites. An effective recommendation system can save the user's time while looking for a suitable product. People are spending more time on the internet these days, and their interests vary over time; as a result, their preference data is accumulating at a rapid pace. The recommendation algorithms should be scalable enough to process this large-scale data efficiently. It should also preserve the relationship between time and user preferences. Matrix factorization is a useful technique in this case. It is, however, dependent on the users' previous transactions and confronts data sparsity and scalability issues. In this study, we proposed a recommendation model utilizing distributed alternating least square matrix factorization that incorporates product ratings along with user reviews. It utilizes the up-vote technique to justify user reviews and the weighted rating normalization approach to assign a normalized rating to the reviews, which helps in improving recommendations. The proposed method is scalable enough to efficiently process the large amount of data generated due to user reviews and ratings. Extensive experiments were performed to validate the outcomes on the well-known Amazon reviews dataset utilizing the Apache Spark cluster. The proposed methodology outperformed state-of-the-art models in product recommendations, achieving an average precision of 89.1% and an average recall of 84.1%.