AutoSVD++: An Efficient Hybrid Collaborative Filtering Model via Contractive Auto-encoders
This work addresses sparsity in recommendation systems for users seeking personalized products, but it is incremental as it builds on existing hybrid collaborative filtering approaches.
The paper tackles the challenge of increasing sparsity in user-item matrices for collaborative filtering by proposing AutoSVD++, a hybrid model that integrates contractive auto-encoders into matrix factorization to jointly model content information and implicit feedback. Experiments on three large-scale datasets show it outperforms compared methods for item recommendation.
Collaborative filtering (CF) has been successfully used to provide users with personalized products and services. However, dealing with the increasing sparseness of user-item matrix still remains a challenge. To tackle such issue, hybrid CF such as combining with content based filtering and leveraging side information of users and items has been extensively studied to enhance performance. However, most of these approaches depend on hand-crafted feature engineering, which are usually noise-prone and biased by different feature extraction and selection schemes. In this paper, we propose a new hybrid model by generalizing contractive auto-encoder paradigm into matrix factorization framework with good scalability and computational efficiency, which jointly model content information as representations of effectiveness and compactness, and leverage implicit user feedback to make accurate recommendations. Extensive experiments conducted over three large scale real datasets indicate the proposed approach outperforms the compared methods for item recommendation.