Stylometric relevance-feedback towards a hybrid book recommendation algorithm

Abstract

Reading is an important activity for individuals. Content-based recommendation systems are, typically, used to recommend scientific papers or news, where search is driven by topic. Literary reading or reading for leisure differs from scientific reading, because users search books not only for their topic but also by author or writing style. Choosing a new book to read can be tricky and recommendation systems can make it easy by selecting books that the user will like. In this paper we study recommendation through writing style and the influence of negative examples in user preferences. Our experiments were conducted in a hybrid set-up that combines a collaborative filtering algorithm with stylometric relevance feedback. Using the LitRec data set, we demonstrate that writing style influences book selection; that book content, characterized with writing style, can be used to improve collaborative filtering results; and that negative examples do not improve final predictions.