A Study of Intelligent Recommendation System based on Naive Bayes Text Classification and Collaborative Filtering

Abstract

Scholarly information has increased tremendously according to the development of IT, especially the Internet. However, simultaneously, people have to spend more time and exert more effort because of information overload. There have been many research efforts in the field of expert systems, data mining, and information retrieval, concerning a system that recommends user-expected information items through presumption. Recently, the hybrid system combining a content-based recommendation system and collaborative filtering or combining recommendation systems in other domains has been developed. In this paper we resolved the problem of the current recommendation system and suggested a new system combining collaborative filtering and Naive Bayes Classification. In this way, we resolved the over-specialization problem through collaborative filtering and lack of assessment information or recommendation of new contents through Naive Bayes Classification. For verification, we applied the new model in NDSL's paper service of KISTI, especially papers from journals about Sitology and Electronics, and witnessed high satisfaction from 4 experimental participants.

A personalized channel recommendation and scheduling system considering both section video clips and full video clips

“…When a new article is obtained, the keywords of the new article are compared with the keywords of the article selected by the user; then the probability that this article is recommended to each user is calculated, and a new article is recommended to the N users who are most likely to be recommended. The algorithm used here is the Naive Bayes model, which is often used in document classification [ 25 ]. In addition, it is difficult to provide a recommendation list for new customers such as ‘User E’ in Fig 3 because there is no purchase history information to understand the user’s taste.…”

The Impact of Neural Embedding Characteristics on Text Mining Tasks: Document Classification Use Case

“…Consequently, the robust systems, like Mining sentiments systems, recommender systems, and retrieval information meaning, have achieved to address this problem. The classification task was officially identified as one of the best solutions for analyzing and extracting useful content from documents and developing the cited yield system [1] [2].…”