C-Rex: A Comprehensive System for Recommending In-Text Citations with Explanations

Finding suitable citations for scientific publications can be challenging and time-consuming. To this end, context-aware citation recommendation approaches that recommend publications as candidates for in-text citations have been developed. In this paper, we present C-Rex, a web-based demonstration system available at http://c-rex.org for context-aware citation recommendation based on the Neural Citation Network [5] and millions of publications from the Microsoft Academic Graph. Our system is one of the first online context-aware citation recommendation systems and the first to incorporate not only a deep learning recommendation approach, but also explanation components to help users better understand why papers were recommended. In our offline evaluation, our model performs similarly to the one presented in the original paper and can serve as a basic framework for further implementations. In our online evaluation, we found that the explanations of recommendations increased users’ satisfaction

A paper recommender system based on user’s profile in big data scholarly

Users encounter a huge volume of papers in digital libraries and paper search engines such as IEEE Explore, ACM Digital library, Google scholar and etc. these high number of papers make some difficulties for researchers for finding proper information and items. Recommender systems contain successful tools for knowledge of users and identification of their priorities. These systems present a personalized proposal to users who seek to find a special kind of relevant data or their priorities through the big number of data. Recommendersystem based on personalization uses the user profile and in view of the fact that the user profile encompass information pertaining to the user priorities; so it is a very active district in data recovery. Recommendersystem is an attitude that presented in order to encounter difficulties caused by abundant data and it helps users to attain their goals quickly through huge number of data. In this paper, we have presented an approach that received entire of available information in a paper, and formed a profile for each user by short and long inquiries; this profile is personalized for user and then recommends the closest paperto the  user by the user profile. Findings indicate that suggested approach outperformsthe similar approaches.Keywords: recommender system; bigdata; user profile; content-based recommender system; hadoo

Citation recommendation via proximity full-text citation analysis and supervised topical prior

Currently the many publications are now available electronically and online, which has had a significant effect, while brought several challenges. With the objective to enhance citation recommendation based on innovative text and graph mining algorithms along with full-text citation analysis, we utilized proximity-based citation contexts extracted from a large number of full-text publications, and then used a publication/citation topic distribution to generate a novel citation graph to calculate the publication topical importance. The importance score can be utilized as a new means to enhance the recommendation performance. Experiment with full-text citation data showed that the novel method could significantly (p < 0.001) enhance citation recommendation performance