Collaborative topic regression for online recommender systems: an online and Bayesian approach
نویسندگان
چکیده
منابع مشابه
Online Bayesian Collaborative Topic Regression
Collaborative Topic Regression (CTR) combines ideas of probabilistic matrix factorization (PMF) and topic modeling (e.g., LDA) for recommender systems, which has gained increasing successes in many applications. Despite enjoying many advantages, the existing CTR algorithms have some critical limitations. First of all, they are often designed to work in a batch learning manner, making them unsui...
متن کاملCollaborative Recommender Systems for Online Shops
Recommender systems are often used in electronic shops in order to suggest similar or related products, potentially interesting products for a given customer or a set of products for a marketing campaign. Most recommender systems use the collaborative filtering method in order to provide the personalization information. The collaborative filtering method is a very efficient and convenient way o...
متن کاملAn Online Recommender System
One of the most important class of Data Mining applications is the so called “Web Mining Systems” which analyzes and extracts important and non-trivial knowledge from Web related data. Typical applications of Web Mining are represented by the personalization or recommender systems. These systems are aimed to extract knowledge from the analysis of historical information of a web server in order ...
متن کاملAn Experiment on Recommender Systems for SME Online Shops
Recommender systems are often used in electronic shops in order to suggest similar or related products, potentially interesting products for a given customer or a set of products for a marketing campaign. Most recommender systems use the collaborative filtering method in order to provide the personalization information. The collaborative filtering method is a very efficient and convenient way o...
متن کاملContext-Aware Collaborative Topic Regression with Social Matrix Factorization for Recommender Systems
Online social networking sites have become popular platforms on which users can link with each other and share information, not only basic rating information but also information such as contexts, social relationships, and item contents. However, as far as we know, no existing works systematically combine diverse types of information to build more accurate recommender systems. In this paper, we...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Machine Learning
سال: 2017
ISSN: 0885-6125,1573-0565
DOI: 10.1007/s10994-016-5599-z