1. WEB PERSONALIZATION: A GENERAL SURVEY
J. JOSIYA ANCY
Research Scholar, Department of Information Technology,
Vellore Institute of Technology,
Vellore District, Tamilnadu.
ABSTRACT:
Retrieving the relevant information from the web becomes difficult now-a-days because
of the large amount of data’s available in various formats. Now days the user rely on web, but it
is mandatory for users to go through the long list and to choose their needed document, which is
very time consuming process. The approach that is to satisfy the requirements of user is to
personalize the data available on Web, is called Web Personalization. Web personalization is
solution to the information overload problem, it provides the users need without having to ask or
search for it explicitly. This approach helps to improve the efficiency of Information Retrieval
(IR) systems. This paper conducts a survey of personalization, its approaches and techniques.
Keywords- Web Personalization, Information retrieval, User Profile, Personalized Search
I. INTRODUCTION:
WEB PERSONALIZATION:
The web is used for accessing a variety of information stored in various locations in
various formats in the whole world. The content in web is rapidly increasing and need for
identifying, accessing and retrieving the content based on the needs of the users is required.
An ultimate need nowadays is that predicting the user requirements in order to improve
the usability of the web site. Personalized search is the solution for this problem since different
search results based on preferences of users are provided.
In brief, web pages are personalized based on the characteristics of an individual user
based on interests, social category, context etc… The Web Personalization is defined as any
action that adapts information or services provided by a web site to an individual user or a set of
users. Personalization implies that the changes are based on implicit data such as items
purchased or pages viewed.
Personalization is an overloaded term: there are many mechanisms and approaches bothe
automated and marketing rules controlled, whereby content can be focused to an audience in a
one to one manner.
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2. WEB PERSONALIZATION ARCHITECTURE:
Fig 1.1: Web personalization process
The above web personalization process uses the web site’s structure, Web logs created by
observing the user’s navigational behavior and User profiles created according to the user’s
preferences along with the Web site’s content to analyze and extract the information needed for
the user to find the pattern expected by the user. This analysis creates a recommendation model
which is presented to the user. The recommendation process relies on the existing user
transactions or rating data, thus items or pages added to a site recently cannot be recommended.
II. WEB PERSONALIZATION APPROACHES
Web mining is a mining of web data on the World Wide Web. Web mining does the
process on personalizing these web data. The web data may be of the following:
1. Content of the web pages(actual web content)
2. Inter page structure
3. Usage data includes how the web pages are accessed by users
4. User profile includes information collected about users(cookies/Session data)
With personalization the content of the web pages are modified to better fit for user needs.
This may involve actually creating web pages, that are unique per user or using the desires of a
user to determine what web documents to retrieve. Personalization can be done to a group of
specific interested customers, based on the user visits to a websites. Personalization also includes
techniques such as use of cookies, use of databases, and machine learning strategies.
Personalization can be viewed as a type of Clustering, Classification, or even Prediction.
USER PROFILES FOR PERSONALIZED INFORMATION ACCESS:
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3. In the modern Web, as the amount of information available causes information
overloading, the demand for personalized approaches for information access increases.
Personalized systems address the overload problem by building, managing, and representing
information customized for individual users. There are a wide variety of applications to which
personalization can be applied and a wide variety of different devices available on which to
deliver the personalized information.
Most personalization systems are based on some type of user profile, a data instance of a
user model that is applied to adaptive interactive systems. User profiles may include
demographic information, e.g., name, age, country, education level, etc, and may also represent
the interests or preferences of either a group of users or a single person. Personalization of Web
portals, for example, may focus on individual users, for example, displaying news about
specifically chosen topics or the market summary of specifically selected stocks, or a groups of
users for whom distinctive characteristics where identified, for example, displaying targeted
advertising on e-commerce sites. In order to construct an individual user’s profile, information
may be collected explicitly, through direct user intervention, or implicitly, through agents that
monitor user activity.
Fig. 2.1. Overview of user-profile-based personalization
As shown in Figure 2.1, the user profiling process generally consists of three main
phases. First, an information collection process is used to gather raw information about the user.
Depending on the information collection process selected, different types of user data can be
extracted. The second phase focuses on user profile construction from the user data. The final
phase, in which a technology or application exploits information in the user profile in order to
provide personalized services.
TECHNIQUES USING USER PROFILE:
From an architectural and algorithmic point of view personalization systems fall into
three basic categories: Rule-based systems, content-filtering systems, and collaborative filtering
systems.
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4. Rule-Based Personalization Systems. Rule-based filtering systems rely on manually or
automatically generated decision rules that are used to recommend items to users. Many existing
e-commerce Web sites that employ personalization or recommendation technologies use manual
rule-based systems.
The primary drawbacks of rule-based filtering techniques, in addition to the usual
knowledge engineering bottleneck problem, emanate from the methods used for the generation
of user profiles. The input is usually the subjective description of users or their interests by the
users themselves, and thus is prone to bias. Furthermore, the profiles are often static, and thus the
system performance degrades over time as the profiles age.
Content-Based Filtering Systems. In Content-based filtering systems, user profiles
represent the content descriptions of items in which that user has previously expressed interest.
The content descriptions of items are represented by a set of features or attributes that
characterize that item. The recommendation generation task in such systems usually involves the
comparison of extracted features from unseen or unrated items with content descriptions in the
user profile. Items that are considered sufficiently similar to the user profile are recommended to
the user.
Collaborative Filtering Systems. Collaborative filtering has tried to address some of the
shortcomings of other approaches mentioned above. Particularly, in the context of e-commerce,
recommender systems based on collaborative filtering have achieved notable successes. These
techniques generally involve matching the ratings of a current user for objects (e.g., movies or
products) with those of similar users (nearest neighbors) in order to produce recommendations
for objects not yet rated or seen by an active user.
III. WEB PERSONALIZATION AND RELATED WORKS
Lot of research had been conducted in Personalized Ontology. Generally, personalization
methodologies are divided into two complementary processes which are (1) the user information
collection, used to describe the user interests and (2) the inference of the gathered data to predict
the closest content to the user expectation. In the first case, user profiles can be used to enrich
queries and to sort results at the user interface level. Or, in other techniques, they are used to
infer relationships like the social-based filtering and the collaborative filtering. For the second
process, extraction of information on users’ navigations from system log files can be used. Some
information retrieval techniques are based on user contextual information extraction.
Dai and Mobasher [5] proposed a web personalization framework that characterizes the
usage profiles of a collaborative filtering system using ontologies. These profiles are transformed
to “domain-level” aggregate profiles by representing each page with a set of related ontology
objects. In this work, the mapping of content features to ontology terms is assumed to be
performed either manually, or using supervised learning methods. The defined ontology includes
classes and their instances therefore the aggregation is performed by grouping together different
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5. instances that belong to the same class. The recommendations generated by the proposed
collaborative system are in turn derived by binary matching the current user visit expressed as
ontology instances to the derived domain-level aggregate profiles, and no semantic relations
beyond hyperonymy/hyponymy are employed.
Acharyya and Ghosh [7] also propose a general personalization framework based on the
conceptual modeling of the users’ navigational behavior. The proposed methodology involves
mapping each visited page to a topic or concept, imposing a tree hierarchy (taxonomy) on these
topics, and then estimating the parameters of a semi- Markov process defined on this tree based
on the observed user paths. In this Markov modelsbased work, the semantic characterization of
the context is performed manually. Moreover, no semantic similarity measure is exploited for
enhancing the prediction process, except for generalizations/specializations of the ontology
terms.
Middleton et.al. [8] explore the use of ontologies in the user profiling process within
collaborative filtering systems. This work focuses on recommending academic research papers to
academic staff of a University. The authors represent the acquired user profiles using terms of a
research paper ontology (is-a hierarchy). Research papers are also classified using ontological
classes. In this hybrid recommender system which is based on collaborative and content-based
recommendation techniques, the content is characterized with ontology terms, using document
classifiers (therefore a manual labeling of the training set is needed) and the ontology is again
used for making generalizations/specializations of the user profiles.
Kearney and Anand [9] use an ontology to calculate the impact of different ontology
concepts on the users navigational behavior (selection of items). In this work, they suggest that
these impact values can be used to more accurately determine distance between different users as
well as between user preferences and other items on the web site, two basic operations carried
out in content and collaborative filtering based recommendations. The similarity measure they
employ is very similar to the Wu & Palmer similarity measure presented here. This work focuses
on the way these ontological profiles are created, rather than evaluating their impact in the
recommendation process, which remains opens for future work.
IV. CONCLUSION
In this paper we surveyed the research in the area of personalization in web search. The study
reveals a great diversity in the methods used for personalized search. Although the World Wide
Web is the largest source of electronic information, it lacks with effective methods for retrieving,
filtering, and displaying the information that is exactly needed by each user. Hence the task of
retrieving the only required information keeps becoming more and more difficult and time
consuming.
To reduce information overload and create customer loyalty, Web Personalization, a
significant tool that provides the users with important competitive advantages is required. A
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