A Top-N-Balanced Sequential Recommendation Based on Recurrent Network

A Recurrent Neural Network Based Recommendation System

6 Recommendation systems play an extremely important role in e-commerce; 7 by recommending products that suit the taste of the consumers, e-commerce 8 companies can generate large profits. The most commonly used 9 recommender systems typically produce a list of recommendations through 10 collaborative or content-based filtering; neither of those approaches take 11 into account the content of th...

Suggest * Top-n Recommendation Engine

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...

A Hierarchical Contextual Attention-based GRU Network for Sequential Recommendation

Sequential recommendation is one of fundamental tasks for Web applications. Previous methods are mostly based on Markov chains with a strong Markov assumption. Recently, recurrent neural networks (RNNs) are getting more and more popular and has demonstrated its effectiveness in many tasks. The last hidden state is usually applied as the sequence’s representation to make recommendation. Benefit ...

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-...