Overview on NLP Techniques for Content-based Recommender Systems for Books

Recommender systems are an essential part of today{'}s largest websites. Without them, it would be hard for users to find the right products and content. One of the most popular methods for recommendations is content-based filtering. It relies on analysing product metadata, a great part of which is textual data. Despite their frequent use, there is still no standard procedure for developing and evaluating content-based recommenders. In this paper, we will first examine current approaches for designing, training and evaluating recommender systems based on textual data for books recommendations for GoodReads{'} website. We will give critiques on existing methods and suggest how natural language techniques can be employed for the improvement of content-based recommenders.