Natural Language Processing via LDA Topic Model in Recommendation Systems
This is an incremental review and application of existing LDA methods for recommendation systems, primarily targeting researchers in NLP and recommendation domains.
The paper presents a taxonomy of recommendation systems using LDA topic modeling and applies LDA with Gibbs sampling to evaluate publications from ISWC and WWW conferences, suggesting that LDA-based systems can be effective for smart recommendations in online communities.
Today, Internet is one of the widest available media worldwide. Recommendation systems are increasingly being used in various applications such as movie recommendation, mobile recommendation, article recommendation and etc. Collaborative Filtering (CF) and Content-Based (CB) are Well-known techniques for building recommendation systems. Topic modeling based on LDA, is a powerful technique for semantic mining and perform topic extraction. In the past few years, many articles have been published based on LDA technique for building recommendation systems. In this paper, we present taxonomy of recommendation systems and applications based on LDA. In addition, we utilize LDA and Gibbs sampling algorithms to evaluate ISWC and WWW conference publications in computer science. Our study suggest that the recommendation systems based on LDA could be effective in building smart recommendation system in online communities.