Improve Search by Optimization or Personalization, A Case Study in Sogou Log

With the development of information technology, especially when the Web is developing and widespread, the information resource grows explosively. The information retrieval(IR) system becomes more and more important as an indispensable way of accessing the information. Generally speaking the search engine returns results for user community rather than each person so there is also considerable room for improvement. But, the user of search engine cares about the personal results for him. And many personalization strategies have been investigated. We present a large-scale research of Sogou Logs and do the following work. 1) Analyze the performance of the search engine, 2) According to current information on the user logs, calculate the optimal performance of the search engine without personalization. 3) Process the current user logs and calculate the optimal performance of the search engine with personalization. 4) Adopt two possible personalized approaches and calculate their performance respectively. 5) Reveal the relationship between the performance of personalization and the results returned by the search engine. All of our experiments are carried out on Sogou Logs.

[1]  Rajeev Motwani,et al.  The PageRank Citation Ranking : Bringing Order to the Web , 1999, WWW 1999.

[2]  Paolo Ferragina,et al.  A personalized search engine based on Web‐snippet hierarchical clustering , 2008, Softw. Pract. Exp..

[3]  Ji-Rong Wen,et al.  WWW 2007 / Track: Search Session: Personalization A Largescale Evaluation and Analysis of Personalized Search Strategies ABSTRACT , 2022 .

[4]  Huan Liu,et al.  CubeSVD: a novel approach to personalized Web search , 2005, WWW '05.

[5]  Taher H. Haveliwala Topic-sensitive PageRank , 2002, IEEE Trans. Knowl. Data Eng..

[6]  W. Bruce Croft,et al.  Lexical ambiguity and information retrieval , 1992, TOIS.

[7]  W. Bruce Croft,et al.  Quantifying query ambiguity , 2002 .

[8]  Susan T. Dumais,et al.  Personalizing Search via Automated Analysis of Interests and Activities , 2005, SIGIR.

[9]  Alexander Pretschner,et al.  Ontology based personalized search , 1999, Proceedings 11th International Conference on Tools with Artificial Intelligence.

[10]  Wolfgang Nejdl,et al.  Using ODP metadata to personalize search , 2005, SIGIR '05.

[11]  Susan T. Dumais,et al.  Beyond the Commons: Investigating the Value of Personalizing Web Search , 2005 .

[12]  Feng Qiu,et al.  Automatic identification of user interest for personalized search , 2006, WWW '06.

[13]  Jennifer Widom,et al.  Scaling personalized web search , 2003, WWW '03.

[14]  Masatoshi Yoshikawa,et al.  Adaptive web search based on user profile constructed without any effort from users , 2004, WWW '04.

[15]  Francisco Tanudjaja,et al.  Persona: a contextualized and personalized web search , 2002, Proceedings of the 35th Annual Hawaii International Conference on System Sciences.

[16]  ChengXiang Zhai,et al.  Mining long-term search history to improve search accuracy , 2006, KDD '06.

[17]  Amanda Spink,et al.  Real life, real users, and real needs: a study and analysis of user queries on the web , 2000, Inf. Process. Manag..

[18]  Hinrich Schütze,et al.  Personalized search , 2002, CACM.

[19]  Mary Beth Rosson,et al.  Paradox of the active user , 1987 .

[20]  Zhenyu Liu,et al.  Automatic identification of user goals in Web search , 2005, WWW '05.

[21]  Monika Henzinger,et al.  Analysis of a very large web search engine query log , 1999, SIGF.

[22]  ChengXiang Zhai,et al.  Implicit user modeling for personalized search , 2005, CIKM '05.