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In this project we examined how recommender systems work (better or worse) if we take advantage of the review texts along with the review ratings. We aimed to combine latent ratings with latent review topics and analyze the results. Our assumption was that combining the review text with ratings would help the recommender system make better predictions. Hence, we compared different models on Amazon Apps dataset and calculated RMSE.
In today’s world of ubiquitous digital content, the number of choices we have are overwhelming. In an effort to understand how recommender systems can make better predictions, we decided to explore the impact of review text on suggesting relevant items to users and helping them discover the content they are looking for. We hypothesized that using review text would produce better recommendations than traditional ratings-only based approaches to recommendation systems.
