Research On Similar Subject Academic Paper Recommendation System

With the popularization of the Internet and WEB2.0,especially,after the establishment of CNKI,Web of Science,VIP database and other academic paper library,there has been an explosion in the number of academic papers in every research field.When researchers retrieve academic paper via keywords,they will always be confusion in the retrieval results,because it will always mix some unmatched retrieval results of other subject papers.They may still have to takes a lot of effort to "pick out" the target papers from the retrieval results that really matches the search keyword.After all,it is difficult to identify whether the papers focus on the same subject or not simply from the individual keywordsTo help researchers find out the papers collections with similar subject from millions of academic papers,a Comprehensive Three-level Text Similarity(CTTS)calculation method based on semantic extension is proposed,the CTTS can dig out the potential semantic similarity between two paragraphs at three different semantic levels,and then select the papers collections with similar subject.However,the significance of the selected papers is not the same.Users may prefer to see papers published in authoritative journals by authoritative people in the research field rather than those published in obscure journals by those authors without any influence.So,an academic paper recommendation model is proposed which is calculated according to author,affiliation,download number and citation number of the paper.The simulation data show that this model can be used to measure the referential value of academic papers,which is called as recommendation degree.When researchers upload a paper,the system will automatically crawls papers in CNKI according to the paper provided by users,and produces a papers collection with similar topics.After that,the system will automatically calculate the recommendation degree of each academic paper for recommend high-quality papers to researchers.The simulation data demonstrate that the model achieves the expected goal.In addition,the system can not only recommend similar subject papers according to the paper provided by users,but also extract keywords and generate abstracts according to the papers provided by users.In the followup work,user profile technology can be added to further enhance the ability of the academic paper recommendation system.