Relational Collaborative Topic Regression for Recommender Systems
Product Description
Relational Collaborative Topic Regression for Recommender Systems
Abstract— Relational Collaborative Topic Regression for Recommender Systems. Due to its successful application in recommender systems, collaborative filtering (CF) has become a hot research topic in data mining and information retrieval. In traditional CF methods, only the feedback matrix, which contains either explicit feedback < Final Year Projects 2016 > also called ratings > or implicit feedback on the items given by users, is used for training and prediction. Typically, the feedback matrix is sparse, which means that most users interact with few items. Due to this sparsity problem, traditional CF with only feedback information will suffer from unsatisfactory performance. Recently, many researchers have proposed to utilize auxiliary information, such as item content (attributes), to alleviate the data sparsity problem in CF.
Video
View Demo
Including Packages
Our Specialization
Support Service
Statistical Report
satisfied customers
3,589
Freelance projects
983
sales on Site
11,021
developers
175+
Additional Information
| Domains | |
|---|---|
| Programming Language |
Would you like to submit yours?
There are no reviews yet