International Journal of Information Technology and Computer Science (IJITCS)
IJITCS Vol. 9, No. 8, 8 Aug. 2017
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Suggestive Approaches to Create a Recommender System for GitHub
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Author(s)
Surbhi Sharma
1
Anuj Mahajan
1,*
Index Terms
Recommender Systems, GitHub, Collaborative filtering, Content-based filtering, hybrid filtering, Knowledge-based approach, Utility-based approach
Abstract
Recommender system suggests users with options that may be of use to them or may be of their interest or liking. These days recommender systems are used widely on most systems and especially on those which are connected to World Wide Web, it may be a mobile app, a desktop application, or a website. Most advertisements on these systems are focused on targeting a specific group. Recommender systems provide a solution to such a scenario where the recommendations need to be targeted based on a user profile. Almost all commercial, collaborative or even social networking websites rely on recommender systems. In this paper, we specifically focus on GitHub, a source code hosting site and one of the most popular platforms for online collaborative coding and sharing. GitHub offers an opportunity for researchers to perform analysis by providing REST-based APIs for downloading its data. GitHub hosts a vast amount of user repositories so it is quite difficult for a GitHub user to decide to which repository she should contribute on GitHub. So, our paper aims to review different approaches that can be used for creating a recommender system for GitHub, to provide personalized suggestions to GitHub users to which repositories they should contribute. In this paper, we have discussed collaborative filtering, content-based filtering, and hybrid filtering, knowledge-based and utility-based approaches of a recommender system.
Cite This Paper
Surbhi Sharma, Anuj Mahajanā°, "Suggestive Approaches to Create a Recommender System for GitHub", International Journal of Information Technology and Computer Science(IJITCS), Vol.9, No.8, pp.48-55, 2017. DOI:10.5815/ijitcs.2017.08.06
Reference
[1]Hu, Jiangtang. "The Hitchhiker’s Guide to GitHub: SAS Programming Goes Social."
[2]Bruno, Rodrigues. "Version Control Systems to Facilitate Research Collaboration in Economics." Computational Economics (2015): 1-7.
[3]Gousios, Georgios, and Diomidis Spinellis. "GHTorrent: GitHub's data from a firehose." In Mining software repositories (msr), 2012 9th IEEE working conference on, pp. 12-21. IEEE, 2012.
[4]Aberger, Christopher R. "Recommender: An Analysis of Collaborative Filtering Techniques."
[5]Tapucu, Dilek, Seda Kasap, and Fatih Tekbacak. "Performance comparison of combined collaborative filtering algorithms for recommender systems." In Computer Software and Applications Conference Workshops (COMPSACW), 2012 IEEE 36th Annual, pp. 284-289. IEEE, 2012
[6]Su, Xiaoyuan, and Taghi M. Khoshgoftaar. "A survey of collaborative filtering techniques." Advances in artificial intelligence 2009 (2009): 4.
[7]Gong, SongJie, HongWu Ye, and HengSong Tan. "Combining memory-based and model-based collaborative filtering in a recommender system." In Circuits, Communications and Systems, 2009. PACCS'09. Pacific-Asia Conference on, pp. 690-693. IEEE, 2009
[8]Bogers, Toine, and Antal Van den Bosch. "Collaborative and content-based filtering for item recommendation on social bookmarking websites."Submitted to CIKM 9 (2009).
[9]Sarwar, Badrul, George Karypis, Joseph Konstan, and John Riedl. "Item-based collaborative filtering recommendation algorithms." In Proceedings of the 10th international conference on World Wide Web, pp. 285-295. ACM, 2001.
[10]Polatidis, Nikolaos, and Christos K. Georgiadis. "A multi-level collaborative filtering method that improves recommendations." Expert Systems with Applications 48 (2016): 100-110.
[11]CarlKadie, JohnS Breese DavidHeckerman. "Empirical Analysis of Predictive Algorithms for Collaborative Filtering." Microsoft Research Microsoft Corporation One Microsoft Way Redmond, WA 98052 (1998).
[12]Sarwar, Badrul, George Karypis, Joseph Konstan, and John Riedl. "Analysis of recommendation algorithms for e-commerce." In Proceedings of the 2nd ACM conference on Electronic commerce, pp. 158-167. ACM, 2000.
[13]Ungar, Lyle H., and Dean P. Foster. "Clustering methods for collaborative filtering." In AAAI workshop on recommendation systems, vol. 1, pp. 114-129. 1998.
[14]Lemdani, Roza, Nacéra Bennacer, Géraldine Polaillon, and Yolaine Bourda. "A collaborative and semantic-based approach for recommender systems." In Intelligent Systems Design and Applications (ISDA), 2010 10th International Conference on, pp. 469-476. IEEE, 2010.
[15]Melville, Prem, Raymond J. Mooney, and Ramadass Nagarajan. "Content-boosted collaborative filtering for improved recommendations." In AAAI/IAAI, pp. 187-192. 2002.
[16]De Pessemier, Toon, Kris Vanhecke, Simon Dooms, and Luc Martens. "Content-based recommendation algorithms on the Hadoop MapReduce framework." In 7th International Conference on Web Information Systems and Technologies (WEBIST-2011), pp. 237-240. Ghent University, Department of Information technology, 2011.
[17]Lops, Pasquale, Marco De Gemmis, and Giovanni Semeraro. "Content-based recommender systems: State of the art and trends." In Recommender systems handbook, pp. 73-105. Springer US, 2011.
[18]Pazzani, Michael J., and Daniel Billsus. "Content-based recommendation systems." In The adaptive web, pp. 325-341. Springer Berlin Heidelberg, 2007.
[19]Lewis, David D. "Naive (Bayes) at forty: The independence assumption in information retrieval." In Machine learning: ECML-98, pp. 4-15. Springer Berlin Heidelberg, 1998.
[20]Felfernig, Alexander, Michael Jeran, Gerald Ninaus, Florian Reinfrank, Stefan Reiterer, and Martin Stettinger. "Basic approaches in recommendation systems." In Recommendation Systems in Software Engineering, pp. 15-37. Springer Berlin Heidelberg, 2014.
[21]Burke, Robin. "Hybrid recommender systems: Survey and experiments."User modeling and user-adapted interaction 12, no. 4 (2002): 331-370.
[22]Adomavicius, Gediminas, and Alexander Tuzhilin. "Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions." Knowledge and Data Engineering, IEEE Transactions on 17, no. 6 (2005): 734-749.
[23]Felfernig, Alexander, Gerhard Friedrich, Bartosz Gula, Martin Hitz, Thomas Kruggel, Gerhard Leitner, Rudolf Melcher et al. "Persuasive recommendation: serial position effects in knowledge-based recommender systems." In Persuasive technology, pp. 283-294. Springer Berlin Heidelberg, 2007.
[24]Felfernig, Alexander, and Kostyantyn Shchekotykhin. "Debugging user interface descriptions of knowledge-based recommender applications." InProceedings of the 11th international conference on Intelligent user interfaces, pp. 234-241. ACM, 2006.
[25]Huang, Shiu-Li. "Designing utility-based recommender systems for e-commerce: Evaluation of preference elicitation methods." Electronic Commerce Research and Applications 10, no. 4 (2011): 398-407
[26]Chatziasimidis, Fragkiskos, and Ioannis Stamelos. "Data collection and analysis of GitHub repositories and users." In Information, Intelligence, Systems and Applications (IISA), 2015 6th International Conference on, pp. 1-6. IEEE, 2015.