Recurrent Translation-Based Network for Top-N Sparse Sequential Recommendation

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

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 ...

Recurrent Neural Network based Translation Quality Estimation

This paper describes the recurrent neural network based model for translation quality estimation. Recurrent neural network based quality estimation model consists of two parts. The first part using two bidirectional recurrent neural networks generates the quality information about whether each word in translation is properly translated. The second part using another recurrent neural network pre...

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-...