International Journal of Education and Management Engineering (IJEME)
IJEME Vol. 6, No. 3, 8 May 2016
Cover page and Table of Contents: PDF (size: 331KB)
Personalized Search Recommender System: State of Art, Experimental Results and Investigations
Full Text (PDF, 331KB), PP.1-8
Views: 0 Downloads: 0
Author(s)
Index Terms
Personalization, Search process, recommender system, review, Web 2.0, social networking, E-commerce
Abstract
Personalized recommender system has attracted wide range of attention among researchers in recent years. These recommender systems suggest products or services depending upon user's personal interest. There has been a huge demand for development of web search apps for gaining knowledge pertaining to user's choice. A strong knowledge base, type of approach for search and several other factors make it accountable for a good personalized web search engine. This paper presents the state of art, challenges and other issues in this context, thereby providing the need for an improved personalized system. The study carried out in this paper reports the overview of existing technologies for building a personalized recommender systems in social networking platforms. Study reported in this article seems to be promising and provides possibilities of research directions, pros & cons and other alternatives.
Cite This Paper
Janet Rajeswari, Shanmugasundaram Hariharan,"Personalized Search Recommender System: State of Art, Experimental Results and Investigations", International Journal of Education and Management Engineering(IJEME), Vol.6, No.3, pp.1-8, 2016. DOI: 10.5815/ijeme.2016.03.01
Reference
[1]Xi Chen, Zibin Zheng, Xudong Liu, Zicheng Huang and Hailong Sun, "Personalized QoS-Aware Web Service Recommendation and Visualization", IEEE Transactions on Services Computing, vol.6, no. 1, pp. 35-47, First Quarter 2013, doi:10.1109/TSC.2011.35.
[2]Yunhong Xu, Xitong Guo, Jinxing Hao, Jian Mac, Raymond Y.K. Lau and Wei Xu , "Combining social network and semantic concept analysis for personalized academic researcher recommendation", Decision Support Systems 54 (2012) 564–573.
[3]Qing Yang, Junli Sun, Jinqiao Wang and Zhiyong Jin, "Semantic Web-Based Personalized Recommendation System of Courses Knowledge Research", International Conference on Intelligent Computing and Cognitive Informatics (ICICCI), pp. 214 – 217, 2010 .
[4]Yu Bo and Qi Luo, "Personalized Web Information Recommendation Algorithm Based on Support Vector Machine", IEEE International Conference on Intelligent Pervasive Computing,pp. 487 – 490, 2007.
[5]Feng-Hsu Wang, "Personalized recommendation for web-based learning based on ant colony optimization with segmented-goal and meta-control strategies", IEEE International Conference on Fuzzy Systems (FUZZ), pp. 2054-2059, 2011.
[6]Ruimei Lian, "The construction of personalized Web page recommendation system in e-commerce", IEEE International Conference on Computer Science and Service System (CSSS), pp. 2687 – 2690, 2011.
[7]Xujuan Zhou, Yue Xu, Yuefeng Li and Audun Josang, "The state-of-the-art in personalized recommender systems for social networking", Artif Intell Rev (2012) 37:119–132.
[8]Mooney RJ, Roy L (2002) Content-based book recommending using learning for text categorization. In: Proceedings of 5th ACM conference on digital libraries. ACM Press, San Antonio, pp 195–204.
[9]Resnick P, IacovouN, SuchakM, Bergstrom P, Riedl J (1994) Grouplens: an open architecture for collaborative filtering of netnews. In: CSCW. pp 175–186.
[10]Schein AI, Popescul A, Ungar LH, Pennock DM (2002) Methods and metrics for cold-start recommendations. In: SIGIR '02: Proceedings of the 25th annual international ACM SIGIR conference on research and development in information retrieval. ACM, New York, pp 253–260.
[11]Yechun Jiang, Jianxun Liu; Mingdong Tang and Xiaoqing Liu, "An Effective Web Service Recommendation Method Based on Personalized Collaborative Filtering", IEEE International Conference on Web Services (ICWS), Page(s): 211 - 218, 2011.
[12]Liang Zhang Xiumin Liu and Xiujuan Liu, "Personalized Instructing Recommendation System Based on Web Mining", The 9th International Conference for Young Computer Scientists (ICYCS 2008), pp. 2517 - 2521 , 2008.
[13]Zhongyun Ying, Zhurong Zhou; Fengjiao Han and Guofeng Zhu, "Research on personalized web page recommendation algorithm based on user context and collaborative filtering", 4th IEEE International Conference on Software Engineering and Service Science (ICSESS), Page(s): 220 - 224 , 2013.
[14]Wei Chu and Seung-Taek Park, "Personalized Recommendation on Dynamic Content Using Predictive Bilinear Models", International World Wide Web Conference Committee (IW3C2), WWW 2009 MADRID, pp.69-700, 2009.
[15]Rani Qumsiyeh and Yiu-Kai Ng, "Predicting the Ratings of Multimedia Items for Making Personalized Recommendations", Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval, pp. 475-484 , 2012.
[16]Chen Lin, Runquan Xie, Xinjun Guan, Lei Li and Tao Li, "Personalized news recommendation via implicit social experts", Information Sciences, Vol. 254, pp. 1–18, 2014.
[17]Ja-Hwung Su, Bo-Wen Wang, Chin-Yuan Hsiao and Vincent S. Tseng, "Personalized rough-set-based recommendation by integrating multiple contents and collaborative information", Information Sciences 180 (2010) 113–131.
[18]Sun Duo and Zhou Cai Ying, "Personalized E- learning System Based on Intelligent Agent", 2012 International Conference on Applied Physics and Industrial Engineering, Physics Procedia (24), pp. 1899 – 1902, 2012.
[19]Ahmad Hawalah and Maria Fasli, "Utilizing contextual ontological user profiles for personalized recommendations", Expert Systems with Applications, Vo. 41, pp. 4777–4797, 2014.
[20]K. Lang, "Newsweeder: Learning to filter netnews," in Proc. of the 12th Int. Conf. on Machine Learning, 1995, pp. 331–339.
[21]D. Billsus and M. Pazzani, "A hybrid user model for news story classification," in Proc. of the 7th Int. Conf. on User Modeling, 1999.
[22]J. Ahn, P. Brusilovsky, J. Grady, and D. He, "Open user profiles for adaptive news systems: help or harm?" in Proc. of the 16th Int. Conf. on WWW, 2007, pp. 11–20.
[23]Zui Zhang, Hua Lin, Kun Liu, Dianshuang Wu, Guangquan Zhang and Jie Lu, "A hybrid fuzzy-based personalized recommender system for telecom products/services", Information Sciences 235 (2013) 117–129.
[24]Donghee Yoo, "Hybrid query processing for personalized information retrieval on the Semantic Web", Knowledge-Based Systems 27 (2012) 211–218.
[25]Mehrdad Jalali, Norwati Mustapha, Md. Nasir Sulaiman and Ali Mamat, "WebPUM: A Web-based recommendation system to predict user future movements", Expert Systems with Applications 37 (2010) 6201–6212.26.
[26]Chuanbao Wang,Fang Yuan,Ying Yun, "Tag Recommendation Based on Collaborative Filtering and Text Similarity", IJEME, vol.2, no.6, pp.7-14, 2012.