Tan Nghia Duong
ABSTRACT
Recent years have witnessed several neural network-based collaborative filtering systems that yield immense success in providing users with personalized suggestions. These systems, however, severely suffer from the data sparsity problem. Similar to other neural network-based collaborative filtering systems, the sparse rating information also affects Autorec at the input layer. This paper represents a generalized hybrid Autorec framework that can accept a variety of content-based representations as input to create a more efficient system. Subsequently, to fully employ the capability of the Autoencoder architecture in compressing information, rating vectors compressed via the conventional Autorec, together with content-based information, creates a robust hybrid input for our framework to resolve the sparsity problem. Empirical experiments demonstrate the state-of-the-art performance of our system compared to other hybrid recommendation systems.
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Index Terms
- A Generalized Autorec Framework Applying Content-based Information for Resolving Data Sparsity Problem
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