CLAVER: An integrated framework of convolutional layer, bidirectional LSTM with attention mechanism based scholarly venue recommendation

Abstract Scholarly venue recommendation is an emerging field due to a rapid surge in the number of scholarly venues concomitant with exponential growth in interdisciplinary research and cross collaboration among researchers. Finding appropriate publication venues is confronted as one of the most challenging aspects in paper publication as a larger proportion of manuscripts face rejection due to a disjunction between the scope of the venue and the field of research pursued by the research article. We present CLAVERG??an integrated framework of Convolutional Layer, bi-directional LSTM with an Attention mechanism-based scholarly VEnue Recommender system. The system is the first of its kind to integrate multiple deep learning-based concepts, that only requiring only the abstract and title of a manuscript to identify academic venues. An extensive and exhaustive set of experiments conducted on the DBLP dataset certify that the postulated model CLAVER performs better than most of the modern techniques as entrenched by standard metrics such as stability, accuracy, MRR, average venue quality, precision@k, nDCG@k and diversity.