CLAVER: An integrated framework of convolutional layer, bidirectional LSTM with attention mechanism based scholarly venue recommendation
| Autor: | Prashanth Kumar, Sukomal Pal, Tribikram Pradhan |
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| Rok vydání: | 2021 |
| Předmět: |
Information Systems and Management
Scope (project management) Computer science 05 social sciences Stability (learning theory) 050301 education 02 engineering and technology Recommender system Data science Field (computer science) Computer Science Applications Theoretical Computer Science Artificial Intelligence Control and Systems Engineering Information and Communications Technology 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Learning to rank Set (psychology) 0503 education Software |
| Zdroj: | Information Sciences. 559:212-235 |
| ISSN: | 0020-0255 |
| Popis: | 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 CLAVER--an integrated framework of Convolutional Layer, bi-directional LSTM with an Attention mechanism-basedscholarly VEnue Recommender system. The system is the first of its kind to integrate multiple deep learning-based concepts, that requires only the abstract and the 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. |
| Databáze: | OpenAIRE |
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