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  1. 1. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 5, Issue 6, June (2014), pp. 37-42 © IAEME 37 WEB SEARCH PERSONALIZATION: A SURVEY V.Raju* Dr. N.Srinivasan Research Scholar, Professor & Head, Department of MCA, Sathyabama University, Chennai - 119 Sathyabama University, Chennai - 119 ABSTRACT The increasing popularity of the Internet and the rise in the number of its users has steered to build new techniques of web search. Every user has a distinct background and a specific goal when searching for information on the web, but the search engines provide same search results to all its users for the same query irrespective of user context. Search results of the search engines for a query will be based on the usage of the users of the world, not the usage of the user requesting it. Nowadays people are more dependent on the Internet and web search engines for their information needs. Due to the increasing need of identifying and retrieving information from the Web, lot of researches are being conducted by considering the interest, preferences, server usage logs, situation of the user etc. This paper explores different perspective from different authors on web search personalization. Keywords: Information Retrieval, Semantic Web, User Profile, Ontology, Web Personalization, Personalized Search, Personalized Ontology. INTRODUCTION The explosive development of information content available on the Web makes it is more difficult to access relevant information from the web. All search engines present search results for the user query but the search results are not based on the user’s interest and preferences. One possible solution to this problem is web search personalization. To give user friendly results to the user, there is a need to do further processing on the search engine’s search results. The query given by the user may mean different things from different contexts. Most of the search engines will not consider the situation and context of the user when they requesting information from the Web. Without knowing the user context we could not present best suited result to the particular user. * Associate Professor, MCA Department, Dhanalakshmi Srinivasan Engineering College, Perambalur - 621 212 INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & TECHNOLOGY (IJCET) ISSN 0976 – 6367(Print) ISSN 0976 – 6375(Online) Volume 5, Issue 6, June (2014), pp. 37-42 © IAEME: www.iaeme.com/ijcet.asp Journal Impact Factor (2014): 8.5328 (Calculated by GISI) www.jifactor.com IJCET © I A E M E
  2. 2. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 5, Issue 6, June (2014), pp. 37-42 © IAEME 38 The objective of web search personalization is to tailor search results to better fit for the user request based on the user context. This paper presents a study of various web search personalization approaches by various authors. WEB SEARCH PERSONALIZATION APPROACHES S.Senthilkumar et al., [1] recommended a semantic search path analysis using a graph based user profile - personalized page-view graph that gives shortest paths to relevant pages that are missed during search and relevant pages in the search result that are unseen by the user. In this approach semantic based techniques are used to identify shortest paths that lead relevant pages. Link between relevant pages and relevant unvisited pages are created based on the content. Relevant pages that are unvisited by the user are focussed by that links. The personalized page-view graph is constructed automatically without human intervention. Such type of semantic search paths based personalization can produce good results than keyword based searching. A graph based profile is effective for producing better search results. But it is time consuming and highlights user’s interest in terms of the web pages visited by the user and does not highlight the user interest directly. S.Vanitha [2] suggested a new idea of combining the user profile and user clicks to find the more personalized web search. Search histories of the users are learnt from the user profiles, used to improve effectiveness of the web search. The terms are grouped with weight, when a user enters a query through the interface. The user intention is found by comparing the term and weight with categories stored in the database and from these key terms it is possible to find the user’s needs and from the click through data we can find the frequently needed data. The Bayesian classification algorithm is used to generate patterns in the click through module. The results of these two modules are mapped to find the data the user needs. Kavitha D. Satokar et al., [3] presented a web search system, which builds a relevance table. A URL Rank algorithm assigns a score depending on their URL to a given user query that measures the quality and relevance of a selected set of pages. In this system the query posted by the user is parsed and separated using the semantic search algorithm. The search engine identifies related URLs for the query given by the user. Ranking the identified URLs and assigning weights is performed by an URL rank algorithm. The URLs are then ranked and arranged in descending order according to favourites and user profile. The most interested URLs appearing higher in the order. This system gives a list of domains to the user and the user can switch to the different interested domains when he surfing the web for information. AhuSieg et al.,[4] presented an approach which builds ontological user profiles and assigns interest scores to existing concepts in the domain ontology and a spreading activation algorithm for maintaining the interest scores in the user profile according to the user’s behaviour. To provide the most relevant search results to the user, a re-ranking of search results is performed based on the interest scores and the semantic information captured in an ontological user profile. The spreading activation algorithm incrementally updates the interest score of the concepts in the user profile. This algorithm maintains an activation value and interest scores for the specific concept. Charanjeet Dadiyala et al.,[5] recommended an approach to improve the retrieval quality of web search results and refining the results depending on the users need by encoding human search experiences and personalizing the search results using ranking optimization. Re-ranking of search results is performed with the help of personalized ranking criteria. Such criteria are observed and implemented from the user’s search history. The types of user’s search interest can be based on time as a parameter as short-term and long-term interests. To overcome the drawbacks of learning from text documents a taxonomic hierarchy as a tree structure is utilised. Zhongming MA et al., [6] proposed a personalized search based on an interest –to-taxonomy mapping framework and result categorization on the client side instead of server side to avoid
  3. 3. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 5, Issue 6, June (2014), pp. 37-42 © IAEME 39 privacy concerns. The mapping framework automatically maps the known user interests onto a set of categories in a web directory – Open Directory Project. With this mapping framework we can built large amount text classifiers. The query expansion approach can retrieve different set of results by adding extra query terms associated with user interests or search context.The interest category mapping framework will automatically identify an ODP category associated with each of the given user interest. Then the system uses URLs to classify search results into based on the various user interests. Such type of web search personalization can decrease search effort and provide interesting and useful information to the user. Miss. Rupali R. Deshmukh and Prof.R.R.Keole [7] proposed an ontological model to represent user profiles in personalized web search. This model uses the world knowledge and a user’s local instance repository (LIR). World knowledge is the knowledge acquired from experience and education by the user and LIR is a user’s personal collection of information. Personalized ontologies are constructed from the knowledgebase by adopting user feedback. To discover background knowledge and to populate the personalized ontologies the user’s LIRs are used. The LIRs used by the ontology model were controlled and contained less uncertainties than the web data used by the web model. Kazunari Sugiyama et al., [8] compared the novel approaches to give relevant search results satisfies the user needs by considering user preference changes without user’s support. Various experiments are conducted to verify the effectiveness and accuracy of the approaches to choose an approach with best accuracy. Since the accuracy of the recommender may be poor due to users are unwilling to rate items, approaches based on user ratings will not give relevant information which satisfies user’s need. So, directly capturing the changes in the user’s interest and preferences without any user intervention during surfing is important to give more relevant search results to the user. This system monitors the user’s browsing behaviour and updates his profile whenever his browsing page changes. It permits the user to perform a fine-grained search by capturing user’s preference changes without any user intervention. The search results adapt based on his profile to reply the next query. The user profile is constructed automatically based on the browsing history. The approaches compared are based on short term browsing history. For the better search results it can be scaled to meet the exact user’s information need. XuePingPeng [9] focussed on using the clickthrough data and Web page ratings to improve web search results. The clickthrough data is extracted from server logs of the web search engines. They constructed an efficient personalized search model from the associations among clickthrough data and computing web page rating. This approach answers how to create user profiles and how to return the different results when the same query is submitted by different users. Also this system will automatically analyse and quantize user’s behaviour. P.Devisree and P.Revathi[10] proposed an ontology model to represent user background knowledge for personalized web search. By extracting world knowledge, this model constructs user personalized ontologies from the LCSH system and discovering user background knowledge from user LIR. To represent user profiles the model discovers user background knowledge and learns personalized ontologies. The world knowledge base and user’s local instance repository are used in this model for the personalized ontology and user background knowledge. The ontology model was evaluated by an information gathering system that used different sets of background knowledge for information gathering. Zhicheng Dou et al., [11] developed an evaluation framework used to evaluate five personalized search algorithms. The topical interest-based personalized search algorithms implemented were not as stable as the click-based ones under this framework. They are improving search accuracy for somequeries, but they poor performance for more queries. Another important assumption revealed in this work is that personalization does not work equally well undervarious situations. Experimental results showed that personalized Web search yields significant
  4. 4. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 5, Issue 6, June (2014), pp. 37-42 © IAEME 40 improvements over generic Web search for queries with a high click entropy. As personalized search had different effectiveness for different kinds of queries, they argued that queries should not be handled in the same manner with regard to personalization. The proposed click entropy can be used as a simple measurement on whether aquery should be personalized.The system further proposed several features to automatically predict whether a query can benefit from a specific personalization algorithm. Alessandro Micarelli et al., [12] built web search models of short term and long term user needs based on user actions, browsed documents or past queries are playing an increasingly crucial role they form a winning combination, able to satisfy the user better than unpersonalized search engines based on traditional information retrieval techniques. Eirinaki M., and Vazirgiannis M [13] divide the process of Web personalization into of five modules, namely: user profiling, log analysis and Web usage mining, information acquisition, content management, and Web site publishing. User profiling is the process of gathering information specific to each visitor to a Web site either implicitly, using the information hidden in the Web logs or technologies such as cookies, or explicitly, using registration forms, questionnaires, and the like. Such information can be demographic, personal, or even information concerning the user’s navigational behaviour. User profiling seems essential in the process of Web personalization; a legal and more accurate way of acquiring such information is needed. The main component of a Web personalization system is the usage miner. Log analysis and Web usage mining is the procedure where the information stored in the Web server logs is processed by applying statistical and data mining techniques, in order to reveal useful patterns that can be further analyzed. Youssouf EL Allioui and Omar EL Beqqali [14] proposed a generic model of profile that includes all aspects of personalization. The model will be the basis of building ontology able to store all this information, personalize the content and to instantiate the user profile. Zhongming MA, Gautam Pant, and Olivia R. Liu Sheng [15] presented an automatic approach to personalizing Web searches given a set of user interests. The approach is well suited for workplace setting where information about professional interests and skills can be obtained automatically from an employee’s resume or a database using an IE tool or database queries. They presented a variety of mapping methods which we combine into an interest-to-taxonomy mapping framework. The mapping framework automatically maps and resolves a set of user interests with a group of categories in the ODP taxonomy. Their approach then uses data from ODP to build text classifiers to automatically categorize search results according to various user interests. This approach has several advantages, in that it does not (1) collect a user’s browsing or search history, (2) ask a user to provide explicit or implicit feedback about the search results, or (3) require a user to manually specify the mappings between his or her interests and taxonomy categories. Kavita Das and O.P. Vyas [16] developed an architectural model for personalization system using web usage mining for a website with personalization features distributed to server and browser. Web mining activities are distributed at the server and browser sides for finding the personalization features. They also proposed bottom up approach for achieving web personalization from personalized websites. Charanjeet Dadiyala et al [17] provide a general process of search result re-ranking that can be used to re-order search results by using personalized ranking criteria. Such criteria are typically observed, studied, derived and then can be implemented from the user’s search history log or simply from the modelling of user’s search behaviour and interests. Xiaohui Tao, Yuefeng Li, and Ning Zhong proposed an ontology model to evaluate the hypothesis that user background knowledge can be better discovered and represented. This model simulates users’ concept models by using personalized ontologies and attempts to improve web information gathering performance by using ontological user profiles.
  5. 5. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 5, Issue 6, June (2014), pp. 37-42 © IAEME 41 CONCLUSION The continuous growth of information and usage of Web has forced researchers to develop innovative techniques to access useful information from Web. Due to the vast collection of information on the Web, it is difficult to present most relevant search result by the search engines. Most of the search engines provide irrelevant search results in addition to the relevant one for the user query. To reduce the burden of the user, it is necessary to refine the search results as per the user context. In this paper, we presented a survey on web search personalization which reviews various research activities carried out by various authors with their innovative ideas to improve the performance of web search personalization. REFERENCES 1. S.Senthilkumar and T.V.Geetha, “Concept Based Personalized Web Search”, Advances in Semantic Computing, Vol. 2, PP 879-102, 2010. 2. S.Vanitha, “Personalized Web Search Based on user profile and user clicks”, International Journal of Latest Research in Science and Technology, Volume 2, Issue 5, Page No. 78-82, September – October 2013 3. KavithaD.Satokar and Prof.S.Z.Gawali, “Web Search Result Personalization using Web Mining”, International Journal of Computer Applications Volume 2 – No. 5, June 2010. 4. AhuSieg, Bamshad Mobasher and Robin Burke, “Learning Ontology – Based User Profiles: A Semantic Approach to Personalized Web Search, IEEE Computational Intelligence, Vol. 8, No. 1, November 2007. 5. Charanjeet Dadiyala, Prof.Pragati Patil, and Prof.GirishAgrawal, “Personalized Web Search”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 6, June 2013. 6. Zhongming MA, Gautam Pant and Olivia R.Liu Sheng, “Interest-Based Personalized Search”, ACM Transactions on Information Systems, Vol. 25, No. 1, Article 5, February – 2007. 7. Miss. Rupali R. Deshmukh and Prof.R.RKeole, “Web Search Personalization with Ontological User Profiles”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 2, Issue 12, December 2013. 8. Kazunari Sugiyama, Kenji Hatano and Masatoshi Yoshikawa, “Adaptive Web Search Based on User Profile Constructed without any Effort from Users”, WWW '04: Proceedings of the 13th international conference on World Wide Web, page 675--684. NewYork, NY, USA, ACM Press, (2004). 9. XuePingPeng, “Personalized Web Search Using Clickthrough Data and Web Page Rating”. Journal of Computers, Vol. 7, No.10, October 2012. 10. P.Devisree and P.Revathi, “Web Search Personalization with Ontological User Profiles and World Knowledge Base”, International Conference on Computing and Control Engineering (ICCCE 2012), 12 & 13 April, 2012. 11. Zhicheng Dou, Ruihua Song, Ji-Rong Wen, and Xiaojie Yuan, “Evaluating the Effectiveness of Personalized Web Search”, IEEE Transactions on Knowledge and data Engineering, Vol. 21, No. 8, AUGUST 2009. 12. Alessandro Micarelli, Fabio Gasparetti, FilippoSciarrone and SusanGauch, ”Personalized Search on theWorld Wide Web”, The Adaptive Web, LNCS 4321, pp. 195–230, 2007. Springer- 2007 13. Eirinaki M., and Vazirgiannis M., “Web Mining for Web Personalization”, ACM transactions on Internet Technology.
  6. 6. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 5, Issue 6, June (2014), pp. 37-42 © IAEME 42 14. Youssouf EL ALLIOUI and Omar EL BEQQALI, “User profile Ontology for the Personalization approach”, International Journal of Computer Applications (0975 – 8887) Volume 41– No.4, March 2012. 15. ZHONGMING MA, GAUTAM PANT, and OLIVIA R. LIU SHENG, ”Interest-Based Personalized Search”, ACM Transactions on Information Systems, Vol. 25, No. 1, Article 5, Publication date: February 2007. 16. Kavita Das and O.P. Vyas, “A Conceptual Model for Website Personalization and Web Personalization”, International Journal of Research and Reviews in Information Sciences (IJRRIS) Vol. 1, No. 4, December 2011. 17. Charanjeet Dadiyala, Prof. Pragati Patil, Prof. Girish Agrawal, “ Personalized Web Search”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 6, June 2013. 18. Xiaohui Tao, Yuefeng Li, and Ning Zhong “A Personalized Ontology Model for Web Information Gathering”, IEEE Transactions on knowledge and Data Engineering, VOL. 23, NO. 4, APRIL 2011. 19. Shaymaa Mohammed Jawad Kadhim and Dr. Shashank Joshi, “Agent Based Web Service Communicating Different Is’s and Platforms”, International Journal of Computer Engineering & Technology (IJCET), Volume 4, Issue 5, 2013, pp. 9 - 14, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. 20. Houda El Bouhissi, Mimoun Malki and Djamila Berramdane, “Applying Semantic Web Services”, International Journal of Computer Engineering & Technology (IJCET), Volume 4, Issue 2, 2013, pp. 108 - 113, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. 21. Ulka M. Bansode, Prof. Gauri R. Rao and Dr. S. H. Patil, “Detection of Phishing E-Commerce Websites using Visual Cryptography”, International Journal of Computer Engineering & Technology (IJCET), Volume 4, Issue 5, 2013, pp. 165 - 171, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. 22. Bokefode J.D., Ubale S. A., Modani D. G. and Bhandare P.S., “Enhancing the Web Site Structure to Provide Easy Traversal on a Web Site with Minimum Changes to its Current Structure”, International Journal of Computer Engineering & Technology (IJCET), Volume 5, Issue 1, 2014, pp. 38 - 45, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. 23. Tawfiq Khalil and Ching-Seh (Mike) Wu, “Link Patterns in the World Wide Web”, International Journal of Information Technology and Management Information Systems (IJITMIS), Volume 4, Issue 3, 2013, pp. 96 - 113, ISSN Print: 0976 – 6405, ISSN Online: 0976 – 6413. 24. Alamelu Mangai J, Santhosh Kumar V and Sugumaran V, “Recent Research in Web Page Classification – A Review”, International Journal of Computer Engineering & Technology (IJCET), Volume 1, Issue 1, 2010, pp. 112 - 122, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. 25. A. Suganthy, G.S.Sumithra, J.Hindusha, A.Gayathri and S.Girija,, “Semantic Web Services and its Challenges”, International Journal of Computer Engineering & Technology (IJCET), Volume 1, Issue 2, 2010, pp. 26 - 37, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. 26. Vijayalakshmi.K, Dr.SudarsonJena, Dr.D.Vasumathi and Dr.R.RajeswaraRao, “A Framework for Personalization using Query Log and Clickthrough Data”, International Journal of Computer Engineering & Technology (IJCET), Volume 5, Issue 2, 2014, pp. 117 - 129, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. 27. L. Chandra Sekaran and Dr. S. Balasubramanian, “Website Based Patent Information Searching Mechanism”, International Journal of Computer Engineering & Technology (IJCET), Volume 1, Issue 2, 2010, pp. 180 - 191, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375.