The Partial Evaluation Approach to Information Personalization

Information personalization refers to the automatic adjustment of information content, structure, and presentation tailored to an individual user. By reducing information overload and customizing information access, personalization systems have emerged as an important segment of the Internet economy. This paper presents a systematic modeling methodology - PIPE (`Personalization is Partial Evaluation') - for personalization. Personalization systems are designed and implemented in PIPE by modeling an information-seeking interaction in a programmatic representation. The representation supports the description of information-seeking activities as partial information and their subsequent realization by partial evaluation, a technique for specializing programs. We describe the modeling methodology at a conceptual level and outline representational choices. We present two application case studies that use PIPE for personalizing web sites and describe how PIPE suggests a novel evaluation criterion for information system designs. Finally, we mention several fundamental implications of adopting the PIPE model for personalization and when it is (and is not) applicable.Comment: Comprehensive overview of the PIPE model for personalizatio

Website Personalization Based on Demographic Data

This study focuses on websites personalization based on user's demographic data. The main demographic data that used in this study are age, gender, race and occupation. These data is obtained through user profiling technique conducted during the study. Analysis of the data gathered is done to find the relationship between the user's demographic data and their preferences for a website design. These data will be used as a guideline in order to develop a website that will fulfill the visitor's need. The topic chose was Obesity. HCI issues are considered as one of the important factors in this study which are effectiveness and satisfaction. The methodologies used are website personalization process, incremental model, combination of these two methods and Cascading Style Sheet (CSS) which discussed detail in Chapter 3. After that, we will be discussing the effectiveness and evaluation of the personalization website that have been built. Last but not least, there will be conclusion that present the result of evaluation of the websites made by the respondents

Knowledge Discovery from Web Logs - A Survey

Web usage mining is obtaining the interesting and constructive knowledge and implicit information from activities related to the WWW. Web servers trace and gather information about user interactions every time the user requests for particular resources. Evaluating the Web access logs would assist in predicting the user behavior and also assists in formulating the web structure. Based on the applications point of view, information extracted from the Web usage patterns possibly directly applied to competently manage activities related to e-business, e-services, e-education, on-line communities and so on. On the other hand, since the size and density of the data grows rapidly, the information provided by existing Web log file analysis tools may possibly provide insufficient information and hence more intelligent mining techniques are needed. There are several approaches previously available for web usage mining. The approaches available in the literature have their own merits and demerits. This paper focuses on the study and analysis of various existing web usage mining techniques

Improved Pre-Processing Stages in Web Usage Mining Using Web Log

Enormous growth in the web persists both in number of web sites and number of users. The growth generated large volume of data in during user’s interaction with the web site and recorded in web logs. Web site owners need to understand about their users by accessing these web logs. Web mining perks up to comprehend range of concepts of diverse fields. Web Usage Mining (WUM) is the recent research field that it corresponds to the process of Knowledge Discovery in Databases (KDD). It comprises three main categories: Pre-Processing, Pattern Analysis, Pattern Discovery. WUM extracts behavioral data from web users data and if possible from web site information (structure and content). In this paper, we propose a customized application specific methodology for preprocessing the Web logs and combining WUM with Association Rule Mining

Survey of data mining approaches to user modeling for adaptive hypermedia

The ability of an adaptive hypermedia system to create tailored environments depends mainly on the amount and accuracy of information stored in each user model. Some of the difficulties that user modeling faces are the amount of data available to create user models, the adequacy of the data, the noise within that data, and the necessity of capturing the imprecise nature of human behavior. Data mining and machine learning techniques have the ability to handle large amounts of data and to process uncertainty. These characteristics make these techniques suitable for automatic generation of user models that simulate human decision making. This paper surveys different data mining techniques that can be used to efficiently and accurately capture user behavior. The paper also presents guidelines that show which techniques may be used more efficiently according to the task implemented by the applicatio

Graph Transformation Based Guidance for Web Navigation

With growing information volume and diverse user preferences on the web, the performance of web information retrieval has become a critical issue. Web navigation is dramatically influenced by the organizations of web contents. Hence, useful navigation guidance can considerably accelerate the information retrieval process. In this paper, web navigation is formulated as a Directed Group Steiner Forest (DGSF) problem in line graph representation of the website. A heuristic algorithm is proposed to tackle the DGSF problem and attain the suboptimal solution in polynomial time. Simulations are conducted to compare the mean searching time for the proposed DGSF-based navigation guidance and other approaches. The results suggest that the DGSF-based navigation guidance can significantly reduce the mean searching time, especially when the number of web pages is large while the number of destination pages is moderate. The discussion is also made for extending the model to take into account the websites owner’s interests and other concerns as well

Semantic Web Personalization: A Survey

With millions of pages available on web, it has become difficult to access relevant information. One possible approach to solve this problem is web personalization. Web personalization is defined as any action that customizes the information or services provided by a web site to an individual. When personalization is applied to the semantic web it offers many advantages when compared to the traditional web because semantic web integrates semantics with the unstructured data on web so that intelligent techniques can be applied to get more efficient results. We have presented various approaches that are used for personalization in semantic web in this paper. The core of semantic web is the ontologies which are defined as explicit formalization of a shared understanding of a conceptualization. We exploit the machine understandable feature of semantic web to device strategies that perform effective personalization such that the results returned to the user are more relevant to the goal set by him. In this paper we have presented the classification of personalization techniques used for semantic web. Keywords: semantic web,ontologies,personalization,recommendation,user profile