Master of Science in Computer Science (MSCS)
As one of the most popular recommendation algorithms, collaborative filtering (CF) suggests items favored by like-minded based on user ratings. However, CF performs worse for users and items with fewer ratings, which is known as the cold-start problem. On the other hand, the auxiliary information of items such as images and reviews can be helpful for relieving the cold-start issue and improving recommendation accuracy. How to effectively extract features from heterogeneous auxiliary information and integrate them with collaborative filtering remains a big challenge. In this thesis, we propose a tightly-coupled hybrid recommender system named Fusion-MF-Mix via a deep fusion framework, which extracts features automatically from different domains and enables two-way information propagation between feature learning and rating prediction. We conduct extensive experiments to evaluate our model on two large-scale real-world datasets from amazon movie and book recommendation domains. The results show that our model outperforms other baseline models.
Li, Fei, "Hybrid Recommender Systems with Deep Learning" (2017). LSU Master's Theses. 4304.
Available for download on Friday, August 09, 2024