Analysis on Research Paper Publication Recommender System with Authors - Conferences Matrix
DOI:
https://doi.org//10.32628/IJSRSET207249Keywords:
Recommender Systems, Machine Learning, Dimensionality Reduction, Correspondence Analysis, Topic Modelling, Linear Transformation, Author Social Network, Content ModellingAbstract
For years, achievements and discoveries made by researcher are made aware through research papers published in appropriate journals or conferences. Many a time, established s researcher and mainly new user are caught up in the predicament of choosing an appropriate conference to get their work all the time. Every scienti?c conference and journal is inclined towards a particular ?eld of research and there is a extensive group of them for any particular ?eld. Choosing an appropriate venue is needed as it helps in reaching out to the right listener and also to further one’s chance of getting their paper published. In this work, we address the problem of recommending appropriate conferences to the authors to increase their chances of receipt. We present three di?erent approaches for the same involving the use of social network of the authors and the content of the paper in the settings of dimensionality reduction and topic modelling. In all these approaches, we apply Correspondence Analysis (CA) to obtain appropriate relationships between the entities in question, such as conferences and papers. Our models show hopeful results when compared with existing methods such as content-based ?ltering, collaborative ?ltering and hybrid ?ltering.
References
- Marko Balabanovi´c and Yoav Shoham. Fab: content-based, collaborative recommendation. Communications of the ACM, 40(3):66–72, 1997.
- Michael Pazzani and Daniel Billsus. Learning and revising user pro?les: The identi?cation of interesting web sites. Machine learning, 27(3):313–331, 1997.
- Xun Zhou, Jing He, Guangyan Huang, and Yanchun Zhang. A personalized recommendation algorithm based on approximating the singular value decomposition (approsvd). In Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology-Volume 02, pages 458–464. IEEE Computer Society, 2012.
- Robert M Bell, Yehuda Koren, and Chris Volinsky. The bellkor solution to the net?ix prize, 2007.
- G´abor Tak´acs, Istv´an Pil´aszy, Botty´an N´emeth, and Domonkos Tikk. A uni?ed approach of factor models and neighbor based methods for large recommender systems. In Applications of Digital Information and Web Technologies, 2008. ICADIWT 2008. First International Conference on the, pages 186–191. IEEE, 2008.
- Arkadiusz Paterek. Improving regularized singular value decomposition for collaborative ?ltering. In Proceedings of KDD cup and workshop, volume 2007, pages 5–8, 2007.
- ON Osmanli and IH Toroslu. Using tag similarity in svd-based recommendation systems. In Application of Information and Communication Technologies (AICT), 2011 5th International Conference on, pages 1–4. IEEE, 2011.
- Ken Goldberg, Theresa Roeder, Dhruv Gupta, and Chris Perkins. Eigentaste: A constant time collaborative ?ltering algorithm. Information Retrieval, 4(2):133–151, 2001.
- Manolis G Vozalis and Konstantinos G Margaritis. Using svd and demographic data for the enhancement of generalized collaborative ?ltering. Information Sciences, 177 (15):3017–3037, 2007.
- Manolis G Vozalis and Konstantinos G Margaritis. A recommender system using principal component analysis. Current Trends in Informatics, 1:271–283, 2007.
- Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl. Application of dimensionality reduction in recommender system-a case study. Technical report, DTIC Document, 2000.
- Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl. Incremental singular value decomposition algorithms for highly scalable recommender systems. In Fifth International Conference on Computer and Information Science, pages 27–28. Citeseer, 2002.
- Panagiotis Symeonidis. Content-based dimensionality reduction for recommender systems. In Data Analysis, Machine Learning and Applications, pages 619–626. Springer, 2008.
- Markus Zanker, Matthias Fuchs, Wolfram H¨opken, Mario Tuta, and Nina Mu¨ller. Evaluating recommender systems in tourism—a case study from austria. Information and communication technologies in tourism 2008, pages 24–34, 2008.
- Steven Bethard and Dan Jurafsky. Who should i cite: learning literature search models from citation behavior. In Proceedings of the 19th ACM international conference on Information and knowledge management, pages 609–618. ACM, 2010.
- C Lee Giles, Kurt D Bollacker, and Steve Lawrence. Citeseer: An automatic citation indexing system. In Proceedings of the third ACM conference on Digital libraries, pages 89–98. ACM, 1998.
- Kannan Chandrasekaran, Susan Gauch, Praveen Lakkaraju, and Hiep Phuc Luong. Concept-based document recommendations for citeseer authors. In Adaptive Hypermedia and Adaptive Web-Based Systems, pages 83–92. Springer, 2008.
- Xiang Chen, Cheng-Zen Yang, Ting-Kun Lu, and Hojun Jaygarl. Implicit social network model for predicting and tracking the location of faults. In Computer Software and Applications, 2008. COMPSAC’08. 32nd Annual IEEE International, pages 136–143. IEEE, 2008.
Downloads
Published
Issue
Section
License
Copyright (c) IJSRSET
This work is licensed under a Creative Commons Attribution 4.0 International License.