A frequent goal of a researcher is to publish his/her work in appropriate conferences and journals. With a large number of options for venues in the microdomains of every research discipline, the issue of selecting suitable locations for publishing cannot be underestimated. Further, the venues diversify themselves in the form of workshops, symposiums, and challenges. Several publishers such as IEEE and Springer have recognized the need to address this issue and have developed journal recommenders. In this thesis, our goal is to design and develop a similar recommendation system for the ACM dataset. We view this recommendation problem from a classification perspective. With the success of deep learning classifiers in recent times and their pervasiveness in several domains, we modeled several 1D Convolutional neural network classifiers for the different venues. When given some submission information like title, keywords, abstract, etc. about a paper, the recommender uses these developed classifier predictions to recommend suitable venues to the user. The dataset used for the project is the ACM Digital Library metadata that includes textual information for research papers and journals submitted at various conferences and journals over the past 60 years. We developed the recommender based on two approaches: 1) A binary CNN classifier per venue (single classifiers), and 2) Group CNN classifiers for venue groups (group classifiers). Our system has achieved a MAP of 0.55 and 0.51 for single and group classifiers. We also show that our system has a high recall rate. / Master of Science / A frequent goal of a researcher is to publish his/her research work in the form of papers and journals at recognized publication conferences and journals. Conferences limit the number of pages in a submission, whereas journals tend to be flexible with the length. In general, academic conferences are held annually, while journals have a submission cut off date on a monthly/trimonthly or so basis. These conferences and journals are publication venues. With a large number of options for venues in the microdomains of every research discipline, the issue of selecting suitable locations for publishing is a complicated task. Further, the venues diversify themselves in the form of workshops, symposiums, and challenges. Submitting a work to the wrong venue often leads to a rejection. Every author who is about to publish faces this question of ``Where can I publish my work so that it gets accepted?". This thesis is an attempt to address this question through a recommendation system. Recommendation systems help us in the decision making process. A well-known example is the ``Customers who bought this also bought item y'' message we find in eCommerce websites. These systems help users navigate a product catalog better to address their needs. The goal of this thesis is to develop one such recommendation system that can help researchers to choose venues. When an author is about to publish, they structure their paper/journal in the form of a research title, brief abstract, relevant keywords in the paper, and a detailed explanation of the research carried out. Our system can take any of these as input and suggest appropriate venues based on the submission content. The dataset used for the project is the ACM Digital Library metadata. We developed the recommender using deep learning techniques. Our system can be helpful for finding a single best venue, or a group of suitable venues.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/106915 |
| Date | 17 June 2020 |
| Creators | Kodur Kumar, Harinni |
| Contributors | Computer Science, Fox, Edward A., Kavanaugh, Andrea L., Karpatne, Anuj |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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