27 research outputs found
A contextual information based scholary paper recommender system using big data platform
Recommender systems for research papers have been increasingly popular. In the past 14 years more than 170 research papers,patents and webpageshave been published in this field. Scientific papers recommender systemsare trying to provide some recommendations to each user which are consistent with the users' personal interests based on performance, personal tastes and users behaviors.Since the volume of papers are growing day after day and the recommender systemshave not the ability for covering these huge volumes ofprocessing papers according to the users' preferences it is necessary to use parallel processing (mapping – reducing programming) for covering and fast processing of these volumes of papers. The suggested system for this research constitutes a profile for each paper which contains context information and the scope of paper. Then, the system will advise some papers to the user according to the user work domain and the papers domain. For implementing the system it has been used hadoop bed and the parallel programming because the volume of data was a part of a big data and the time was also an important factor. The performance of the suggested system was measured by the criteria such as user satisfaction and the accuracy and the results have been satisfactory.Keywords: Recommender systems; big data; Hadoop; contextual informatio
Joint Modeling of Topics, Citations, and Topical Authority in Academic Corpora
Much of scientific progress stems from previously published findings, but
searching through the vast sea of scientific publications is difficult. We
often rely on metrics of scholarly authority to find the prominent authors but
these authority indices do not differentiate authority based on research
topics. We present Latent Topical-Authority Indexing (LTAI) for jointly
modeling the topics, citations, and topical authority in a corpus of academic
papers. Compared to previous models, LTAI differs in two main aspects. First,
it explicitly models the generative process of the citations, rather than
treating the citations as given. Second, it models each author's influence on
citations of a paper based on the topics of the cited papers, as well as the
citing papers. We fit LTAI to four academic corpora: CORA, Arxiv Physics, PNAS,
and Citeseer. We compare the performance of LTAI against various baselines,
starting with the latent Dirichlet allocation, to the more advanced models
including author-link topic model and dynamic author citation topic model. The
results show that LTAI achieves improved accuracy over other similar models
when predicting words, citations and authors of publications.Comment: Accepted by Transactions of the Association for Computational
Linguistics (TACL); to appea
Multi-modal adversarial autoencoders for recommendations of citations and subject labels
We present multi-modal adversarial autoencoders for recommendation and evaluate them on two different tasks: citation recommendation and subject label recommendation. We analyze the effects of adversarial regularization, sparsity, and different input modalities. By conducting 408 experiments, we show that adversarial regularization consistently improves the performance of autoencoders for recommendation. We demonstrate, however, that the two tasks differ in the semantics of item co-occurrence in the sense that item co-occurrence resembles relatedness in case of citations, yet implies diversity in case of subject labels. Our results reveal that supplying the partial item set as input is only helpful, when item co-occurrence resembles relatedness. When facing a new recommendation task it is therefore crucial to consider the semantics of item co-occurrence for the choice of an appropriate model