Authoritative Scholarly Paper Recommendation Based on Paper Communities

With the rapid growth of the number of papers, the traditional methods of searching papers by scholar search engine are becoming unacceptable. These methods can´t meet the needs of users and users still need to take a lot of time to filter the search results. To solve the problem, this paper uses the concepts and methods of community partition and introduces a model to recommend authoritative papers based on the specific community. Above all, this model uses Greedy Clique Expansion Algorithm to discover communities. Then, we study the diffusion of influence based on the specific community. At last, our model uses Paper Rank Algorithm to compute the influence of papers and gets a recommendation list. Compared with existing paper recommendation methods, our method narrows the scope of recommended papers, and further improves the recommending speed. Besides, our method improves the quality of recommended papers by ranking papers´ influence.