Application of Key Words Recommendation Based on Apriori Algorithm in ThemeOriented Personalized Search
LIU Qi1, BU JiaJun2, CHEN Chun2
1.James D. Watson Institute of Genome Sciences, Zhejiang University, Hangzhou 310008 2.College of Computer Science and Technology, Zhejiang University, Hangzhou 310027
Abstract：The application of collaborative filtering algorithm in key words recommendation is analysed and a themeoriented key words recommendation algorithm in personalized search is proposed based on Apriori algorithm in this paper. The essential of proposed method is mining the frequent itemsets of the historical key words by Apriori algorithm. Experimental result indicates that the algorithm can recommend new key words to user based on the historical key words and make the search results more accurate and individual.
刘琦，卜佳俊，陈纯. 基于Apriori算法的关键词推荐在面向主题的用户个性化搜索中的应用[J]. 模式识别与人工智能, 2006, 19(2): 186-190.
LIU Qi, BU JiaJun, CHEN Chun. Application of Key Words Recommendation Based on Apriori Algorithm in ThemeOriented Personalized Search. , 2006, 19(2): 186-190.
[1] Belkin N J, Croft W B. Information Filtering and Information Retrieval: Two Sides of the Same Coin. Communications of the ACM, 1992, 35(12): 29-38 [2] Foltz P W, Dumais S T. Personalized Information Delivery: an Analysis of Information Filtering Methods. Communications of the ACM, 1992, 35(12): 51-60 [3] Zeng C, Xing C X, Zhou L Z. A Personalized Search Algorithm by Using Content-Based Filtering. Journal of Software, 2003, 14(5): 999-1004 (in Chinese) (曾 春,邢春晓,周立柱.基于内容过滤的个性化搜索算法.软件学报, 2003, 14(5): 999-1004) [4] Shardanand U, Maes P. Social Information Filtering: Algorithms for Automating “Word of Mouth”. In: Proc of the SIGCHI Conference on Human Factors in Computing Systems. Denver, USA, 1995, 210-217 [5] Sarwar B, Karypis G, Konstan J, Reidl J. Item-Based Collaborative Filtering Recommendation Algorithms. In: Proc of the 10th International Conference on World Wide Web. Hongkong, China, 2001, 285-295 [6] Agrawl R, Srikant R. Fast Algorithms for Mining Association Rules. In: Proc of the 20th International Conference on Very Large Data Bases. Santiago, Chile, 1994, 487-499 [7] Zeng C, Xing C X, Zhou L Z. A Survey of Personalization Technology. Journal of Software, 2002, 13(10): 1952-1961 (in Chinese) (曾 春,邢春晓,周立柱.个性化服务技术综述.自动化学报, 2002, 13(10): 1952-1961) [8] Agrawal R, Imielin＇ski T, Swami A. Mining Association Rules between Sets of Items in Large Databases. In: Proc of the ACM SIGMOD International Conference on Management of Data. Washington, USA, 1993, 207-216 [9] Mobasher B, Dai H H, Luo T, et al. Effective Personalization Based on Association Rule Discovery from Web Usage Data. In: Proc of the ACM International Workshop on Web Information and Data Management. Atlanta, USA, 2001, 9-15 [10] Pretschner A, Gauch S. Ontology Based Personalized Search. In: Proc of the 11th IEEE International Conference on Tools with Artificial Intelligence. Washington, USA: IEEE Computer Society, 1999, 391-398 [11] Mostafa J, Mukh-Opadh-Yay S, Palakal M, Lam W. A Multilevel Approach to Intelligent Information Filtering: Model, System, and Evaluation. ACM Trans on Information Systems, 1997, 15(4): 368-399 [12] Alton-Scheidl R, Ekhall J, et al. SELECT: Social and Collaborative Filtering of Web Documents and News. In: Proc of the 5th ERCIM Workshop on User Interfaces for All: User-Tailored Information Environments. Dagstuhl, Germany, 1999, 23-37 [13] Balabanovic＇ M. An Adaptive Web Page Recommendation Service. In: Proc of the 1st International Conference on Autonomous Agents. Marina del Rey, USA, 1997, 378-385 [14] Wu Y H, Chen Y C, Chen A L P. Enabling Personalized Recommendation on the Web Based on User Interests and Behaviors. In: Proc of the 11th International Workshop on Research Issues in Data Engineering on Document Management for Data Intensive Business and Scientific Applications. Heidelberg, Germany, 2001, 17-24
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