Seeking recommendation for
books is a common task for people due to the large amount of books and their
various themes and topics. Developing an online recommender system for websites
that holds large number of book reviews and users’ interaction will benefit a
lot. In this project, we used different recommendation models, including a baseline
model, latent Dirichlet distribution (LDA), Matrix Factorization (MF), and Collaborative
Topic Regression(CTR), to recommend books to users of Douban, an Chinese online networking community providing books,
movies, and music. We crawl and study a subset of data from Douban, and trained different parameters
and used the optimized
parameters to compare results. Results showed that CTR
model provides more effective recommender system than other models.
The final product is in the following link:
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