The phenomenon of rapidly developing academic venues poses a significant challenge for researchers: how to recognize the ones that are not only in accordance with one's scholarly interests but also of high significance? Often, even a high-quality paper is rejected because of a mismatch between the research area of the paper and the scope of the journal. Recommending appropriate scholarly venues to researchers empowers them to recognize and partake in important academic conferences and assists them in getting published in impactful journals. A venue recommendation system becomes helpful in this scenario, particularly when exploring a new field or when further choices are required. We propose CNAVER: A Content and Network-based Academic VEnue Recommender system. It provides an integrated framework employing a rank-based fusion of paper-paper peer network (PPPN) model and venue-venue peer network (VVPN) model. It only requires the title and abstract of a paper to provide venue recommendations, thus assisting researchers even at the earliest stage of paper writing. It also addresses cold start issues such as the involvement of an inexperienced researcher and a novel venue along with the problems of data sparsity, diversity, and stability. Experiments on the DBLP dataset exhibit that our proposed approach outperforms several state-of-the-art methods in terms of precision, nDCG, MRR, accuracy, F−measuremacro, average venue quality, diversity, and stability.
All Science Journal Classification (ASJC) codes
- Management Information Systems
- Information Systems and Management
- Artificial Intelligence