Combining Algorithms for Recommendation System on Twitter

Abstract:

Twitter has rapidly increased in popularity over the past few years. So, we have focused on Twitter as it has a large scale of data which is increasingly difficult to search through. In this paper, we propose recommendations for content on Twitter. We explored four dimensions in designing such as: topic relevance of content sources, the content candidate set for users, social voting and Meta data mapping. We implemented 24 algorithms for analysis of 12,000 records for three domains as follows: entertainment, stock exchange and smart phone in the design space. The best performing algorithm improved the percentage of correct matching interesting content to 23.86%.

• Periodical: Advanced Materials Research (Volumes 403-408)
• Pages: 3688-3692
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.403-408.3688
• Online since: November 2011
• Authors: Nichakorn Pankong, Somchai Prakancharoen
• Keywords: Logistic Regression Analysis, Recommendation System, Support Vector Machine (SVM), Twitter

References

[1] Jianshu Weng, Ee-Peng Lim, Jing Jiang and Qi He, in: TwitterRank: Finding Topic-sensitive Influential Twitterers, WSDM'10, New York City, New York, USA (2010).

DOI: 10.1145/1718487.1718520

[2] Jilin Chen, Rowan Nairn, Les Nelson, Michael Bernstei and Ed H. Chi, in: Short and Tweet: Experiments on Recommending Content from Information Streams, CHI 2010, Atlanta, Georgia, USA (2010).

DOI: 10.1145/1753326.1753503

[3] Choochart Haruechaiyasak, Wittawat Jitkrittum, Chatchawal Sangkeettrakarn and Chaianun Damrongrat, in: Implementing News Article Category Browsing Based on Text Categorization Technique, Web Intelligence/IAT Workshops, USA (2008).

DOI: 10.1109/wiiat.2008.61

[4] Phelan, Owen; McCarthy, Kevin; Smyth, Barry, in: Using Twitter to recommend real-time topical news, Proceedings of the third ACM conference on Recommender systems, (2009).

DOI: 10.1145/1639714.1639794

[5] Nilanjan Banerjee, Angshu Rai and Sameer Madan, in: User Interests in Social Media Sites: An Exploration with Micro-blogs, CHI 2010, Atlanta, Georgia, USA (2010).

[6] Yuan Wang, Jie Zhang and Julita Vassileva, in: A User-Centric Approach for Social Data Integration and Recommendation, Human-Centric Computing (HumanCom), (2010).

DOI: 10.1109/humancom.2010.5563331

[7] Bernard J. Jansen, Mimi Zhang, Kate Sobel and Abdur Chowdury, in: Twitter Power: Tweets as ElectronicWord of Mouth, American Society For Information Science and Technology, (2009).

DOI: 10.1002/asi.21149

[8] Ray, S.K. Singh, S. Dept. of Comput. Sci. & Eng., BIT, Muscat, Oman, in: Blog content based recommendation framework using WordNet and multiple Ontologies, Computer Information Systems and Industrial Management Applications (CISIM), (2010).

DOI: 10.1109/cisim.2010.5643502

[9] Danah boyd, Scott Golder and Gilad Lotan, in: Tweet, Tweet, Retweet: Conversational Aspects of Retweeting on Twitter, HICSS-43. IEEE, Kauai, HI (2010).

DOI: 10.1109/hicss.2010.412

[10] Jia Li,: Logistic Regression, on www. stat. psu. edu/~jiali/course/stat597e/notes2/logit. pdf.

[11] Marti A. Heart, in: Support vector machines, IEEE Intelligent Systems, vol. 13, (1998).

[12] Nathaniel Good, J. Ben Schafer, Joseph ]A. Konstan, Al Borchers, Badrul Sarwar, Jon Herlocker, and John Riedl in: Combining Collaborative Filtering with Personal Agents for Better Recommendation, University of Minnesota, (1999).

[13] Information on http: /www. dtreg. com/svm. htm.