Graph-Based Diffusion Method for Top-N Recommendation

Suggest * Top-n Recommendation Engine

Content-Based Top-N Recommendation Using Heterogeneous Relations

Top-N recommender systems have been extensively studied. However, the sparsity of user-item activities has not been well resolved. While many hybrid systems were proposed to address the cold-start problem, the profile information has not been sufficiently leveraged. Furthermore, the heterogeneity of profiles between users and items intensifies the challenge. In this paper, we propose a content-...

Improving Top-N Recommendation with Heterogeneous Loss

Personalized top-N recommendation systems have great impact on many real world applications such as E-commerce platforms and social networks. Most existing methods produce personalized topN recommendations by minimizing a specific uniform loss such as pairwise ranking loss or pointwise recovery loss. In this paper, we propose a novel personalized top-N recommendation approach that minimizes a c...

Top-N Books Recommendation using Wikipedia

This paper presents an approach of recommending a ranked list of books to a user. A user profile is defined by a few liked and disliked books. To recommend a book, we calculate semantic relatedness of the given book to the liked and disliked books by using Wikipedia. Based on the obtained scores, we predict ratings of the book. We evaluate our approach on a dataset that consists of 6,181 users,...

B-Rank: A top N Recommendation Algorithm

In this paper I propose B-Rank, an efficient ranking algorithm for recommender systems. B-Rank is based on a random walk model on hypergraphs. Depending on the setup, B-Rank outperforms other state of the art algorithms in terms of precision, recall ∼ (19%− 50%) and inter list diversity ∼ (20%− 60%). B-Rank captures well the difference between popular and niche objects. The proposed algorithm p...