Digital Research Paper Recommendation System Appling Feature Based Method
Maejo University, Thailand
ABSTRACT
The result of internet growing is the vast information. Recommendation and Search Engine Optimization Methods are used for helping users to find their interested information. In this research, we developed the digital research paper website for assisting searchers to search the interested paper. The recommendation system was used to suggest the related papers for the searcher. The feature-based method was applied in the system. The search engine optimization method was applied to increase the ranking website on the main search engines such as Google, Yahoo, and Bing. The F-measure and Google analytic were used to evaluate the performance of the recommendation and search engine optimization process. The website was developed by using HTML and PHP languages, HTML Access, and MySQL. In the dataset, 246 papers were stored and each paper had 20 representative features. In the experimental, the results were collected between Mach and April 2015. The results showed that the recommendation and search engine optimization methods effectively assisted searchers to search for their interested papers.
KEYWORDS
Recommendation System, Data Mining, Web-Service, Search Engine Optimize