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 &
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ISSN 0976 – 6367(Print)
ISSN 0976 – 6375(Online)
Volume 5, Issue 6, June (2014), pp. 37-42
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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. 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
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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. 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. 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
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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.
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