Exploring coclustering for serendipity improvement in content-based recommendation

EasyChair Preprint no. 625

Abstract

Content-based recommender systems are now widely used for item recommendations in several application domains such as entertainment, e-commerce and news. However, one of its major drawbacks is the lack of serendipity in recommendations. A recommendation is considered serendipitous when is both relevant and unexpected. There is a common understanding in literature that the search for serendipitous recommendations should be guided by partial similarities between items. From that intuition, coclustering can be exploited in order to ensure the compromise between accuracy and unexpectedness leading to serendipitous recommendations to the users, since it is a technique capable of finding partial similarity relations between items. In this paper, we propose the use of coclustering for serendipity improvement in content-based recommender systems. Experiments carried out over the MovieLens 2K dataset show that the proposed approach overcome a traditional content-based recommender in terms of serendipity.

Keyphrases: coclustering, Content-based Recommender Systems, Jaccard similarity, nonnegative matrix factorization, Serendipity

@Booklet{EasyChair:625,
  author = {Andrei Martins Silva and Fernando Henrique da Silva Costa and Alexandra Katiuska Ramos Diaz and Sarajane Marques Peres},
  title = {Exploring coclustering for serendipity improvement in content-based recommendation},
  howpublished = {EasyChair Preprint no. 625},
  doi = {10.29007/8g6q},
  year = {EasyChair, 2018}}