Top PDF Ontology Generation from Session Data for Web Personalization

Ontology Generation from Session Data for Web Personalization

Ontology Generation from Session Data for Web Personalization

-------------------------------------------------------------------ABSTRACT----------------------------------------------------------------- With an increasing continuous growth of information in WWW it is very difficult for the users to access the interested web pages from the website. Because day by day the information in the web is growing in an increasing manner so without any help system the user may spend more time to get the interested information from the website. To overcome the above problem, in this paper we propose a Model which create a User Interested Page Ontology (UIPO), it will be created by assigning weights and ranking the user interest by count the number of occurrence of each item which was collected from the web logs within a session for all users. The main feature of this model is, it generates UIPO dynamically from that it personalize the interested pages to the web users in their next access The proposed model is very useful for understanding the behavior of the users and also improving the web site design too. The performance of the new model in a session is also discussed in this paper.
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A Survey on Web Personalization of Web Usage Mining

A Survey on Web Personalization of Web Usage Mining

In web usage mining, association rules are used to discover pages that are visited together quite often. Knowledge of these associations can be used either in marketing and business or as guidelines to web designers for (re)structuring web sites. Transactions for mining association rules differ from those in market basket analysis as they cannot be represented as easily as in MBA (items bought together). Association rules are mined from user sessions containing remote host, user id, and a set of URL’s. As a result of mining for association rules we can get, for example, the rule: X, Y → Z (c=85%, s=1%). This means that visitors who viewed pages X and Y also viewed page Z in 85 % (confidence) of cases, and that this combination makes up 1% of all transactions in preprocessed logs. In [Cooley et al., 1999] a distinction is made between association rules based on a type of pages appearing in association rules. They identify Auxiliary- Content Transactions and Content-only transactions. The second one is far more meaningful as association rules are found only among pages that contain data important to visitors.
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Modern way for Web Personalization through Web Mining

Modern way for Web Personalization through Web Mining

Nowadays, the data is rapidly growing in various domains on the web. That is why, these days, the need for identifying and retrieving the data is based on exactly the needs of users in order to improve the usability of a website. To do this, web personalization is defined as any action that always adopts the contents or services provided by a web site to single user or a group of users based on their navigational behaviour, stored in the log file. This data is included with the content, structure as well as user’s interests. The web personalization gives the output in the dynamic generation of suggestions to create web pages according to the needs of users, highlighted existing hyperlinks that are required by the users. Most of the earlier research efforts in web personalization deal with Web Usage Mining(WUM). Here, we present pure usage based personalization where insufficient data is available in order to extract pattern. When, the content of the web site is changed and new pages are added but that isn’t included in the logs yet. These days, the user’s aim is to find the content concerning a required content. Thus the underlined content semantics should be a dominant factor in the web personalization process. There have been many research studies that integrate the contents of web site in order to improve the process of web personalization. Most of the contents are characterized on the web by extracting features from the web pages. Keywords as features are used to retrieve similar content based on the interest of the user.
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Web Mining for Web Personalization

Web Mining for Web Personalization

A way of uniquely identifying a visitor through a session is by using cookies. W3C [WCA] defines cookie as “the data sent by a Web server to a Web client, stored locally by the client and sent back to the server on subsequent requests.” In other words, a cookie is simply an HTTP header that consists of a text-only string, which is inserted into the memory of a browser. It is used to uniquely identify a user during Web interactions within a site and contains data parameters that allow the remote HTML server to keep a record of the user identity, and what actions he takes at the remote Web site. The contents of a cookie file depend on the Web site that is being visited. In general, information about the visitor’s identification is stored, along with password information. Additional information such as credit card details, if one is used during a transaction, as well as details concerning the visitor’s activities at the Web site, for example, which pages were visited, which purchases were made, or which advertisements were selected, can also be included. Often, cookies point back to more detailed customer information stored at the Web server. Another way of uniquely identifying users through a Web transaction is by using identd, an identification protocol specified in RFC 1413 [RFC] that provides a means to determine the identity of a user of a particular TCP connection. Given a TCP port number pair, it returns a character string, which identifies the owner of that connection (the client) on the Web server’s system. Finally, a user can be identified making the assumption that each IP corresponds to one user. In some cases, IP addresses are resolved into domain names that are registered to a person or a company, thus more specific information is gathered. As already mentioned, user profiling information can be explicitly obtained by using online registration forms requesting information about the visitor, such as name, age, sex, likes, and dislikes. Such information is stored in a database, and each time the user logs on the site, it is retrieved and updated according to the visitor’s browsing and purchasing behavior.
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An Implementation of Web Personalization using Web Mining Techniques

An Implementation of Web Personalization using Web Mining Techniques

Association rule mining has been applied to e-learning systems for traditionally association analysis (finding correlations between items in a dataset), including, e.g., the following tasks: building recommender agents for on-line learning activities or shortcuts [5], automatically guiding the learner’s activities and intelligently generate and recommend learning materials [6], identifying attributes characterizing patterns of performance disparity between various groups of students [7], discovering interesting relationships from student’s usage information in order to provide feedback to course author [8], finding out the relationships between each pattern of learner’s behavior [9], finding students’ mistakes that are often occurring together [10], guiding the search for best fitting transfer model of student learning [11], optimizing the content of an e-learning portal by determining the content of most interest to the user [12], extracting useful patterns to help educators and web masters evaluating and interpreting on-line course activities [5], and personalizing e-learning based on aggregate usage profiles and a domain ontology [13].
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User profile Ontology for the Personalization approach

User profile Ontology for the Personalization approach

For this, the works are oriented to design of a new generation of personalization systems based on context, aimed at delivering information relevant and appropriate to the context of the user who issued the request. In [19], a contextual personalization system is defined as follows: "Combine search technologies and knowledge about the query and user context into a single framework in order to provide the most appropriate answer for a user’s information needs". The works of [20] placed the notion of context and situation, without distinction, where the context describes the intentions of the user on the one hand, and research environment on the other. There are many definitions of context discussed in the literature that differ primarily by elements of the context. A multi-dimensional definition of context [21] adds to the situation notion the characteristics related of a part in the temporal aspect of information needs and the type of research asked the other. Although the authors do not converge to the same definition of context, however there are common dimensions descriptive such as cognitive environment, the need for information, etc. Into a contextual personalization system, stress is laid on using a user's model previously constructed called "profile" [22]. The first systems are designed based on collaborative filtering. These systems such as Grouplens [23] exploit the collaborative profile linked to a group of users sharing common interests and persistent and returns the user to the information meeting the criteria of the profile of the group to which it belongs. On the other, personal agents of data personalization are then developed as the system Letizia [24] which is a personal assistant to using the web, able to propose the information without explicit request by the user. Other systems [13] [15] explore different techniques for learning of user profile that is subsequently used in one of the phase of the personalization process. We focused in this paper to present an extension of approaches to implicit construction of user profile previously developed in the literature with the object of build a profile. Our approach is based on the using of ontology. This profile will be exploited in a personalization process of neurological responses.
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A Personalization Recommendation Method Based on Deep Web Data Query

A Personalization Recommendation Method Based on Deep Web Data Query

Users’ interests are various and ever-changing, it would be too simply to describe them as interested or uninterested. It can neither effectively describe users’ multiple interest features, nor timely tracking users’ interests changing, especially some interests update frequently and change shortly. Considering the factors mentioned above, in the period from users submit information needs to log off, do integrated description about one’s interests. Thereinto, that includes the process of the user interest model make dynamic update as demand adjusts, in order to achieve the purpose of reflecting user interest information needs timely and accurately. The paper constructs fine-grained management of structured data. The user interest model concludes and summarizes from bottom to up based on the domain ontology, the forming process is simple and the description is accurate. At the same time it can describe the users' interest differently, reduce the interference between different categories, help frequent short-term theme interest changes, and improve the precision of the model updating.
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Web Personalization Using Web Mining

Web Personalization Using Web Mining

Besides the above stated problem a recent research has shown that only 13% of search engines show personalization characteristics. Hence web personalization [1] is one of the promising approaches to tackle this problem by adapting the content and structure of websites to the needs of the users by taking advantage of the knowledge acquired from the analysis of the users’ access behaviors. One research area that has recently contributed greatly to this problem is web mining. Web mining aims to discover useful information or knowledge from the Web hyperlink structure, page content and usage log. There are roughly three knowledge discovery domains that pertain to web mining: Web Content Mining, Web Structure Mining, and Web Usage Mining. Web content mining is the process of extracting knowledge from the content of documents or their descriptions. Web document text mining, resource discovery based on concepts indexing or agent based technology may also fall in this category. Web structure mining is the process of inferring knowledge from the World Wide Web organization and links between references and referents in the Web. Finally, web usage mining, also known as Web Log Mining, is the process of extracting interesting patterns in web access logs. A key part of the personalization process is the generation of user models. Commonly used user models are still rather simplistic, representing the user as a vector of ratings or using a set of keywords. Even where more multi- dimensional information has been available, such as when collecting implicit measures of interest, the data has traditionally been mapped onto a single dimension; in the form of ratings .In particular profiles commonly used today lack in their ability to model user context and dynamics. Users rate different items for different reasons and under different contexts. The user interests and needs change with time. Identifying these changes and adapting to them is a key goal of personalization. We suggest that the personalization process be taken to a new level, a level where the user does not to be actively involved with the personalization process. All that the user needs to do is to have an active profile file and when the user logs onto a web site, the browser checks for that profile file as it checks for the cookies. The profile file describes the user’s interest and the levels at which the user wants a particular personalizable feature. Since the profile file is in a standardized format, the web sites would be able to provide the content according to the profile file. This would enhance the user’s personalization process without their active involvement.
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An Accomplishment of Web Personalization
                      Using Web Mining Techniqu

An Accomplishment of Web Personalization Using Web Mining Techniqu

This paper has attempted to cover most of the activities of the rapidly growing area of Web usage mining. The proposed frame work “Online Miner “seems to work well for developing prediction models to analyze the web traffic volume. However ,Web usage mining raises some hard scientific questions that must answered before robust tools can be developed. Web usage patterns and data mining will be the basis for a great deal in future research.. Future research will also incorporate data mining algorithms to improve knowledge discovery. The development and application of Web mining techniques in the context of Web content, usage, and structure data will lead to tangible improvements in many Web applications, from search engines and Web agents to Web analytics and personalization. Future efforts, investigating architectures and algorithms that can exploit and enable a more effective integration and mining of content, usage, and structure data from different sources promise to lead to the next generation of intelligent Web applications.
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TermGenie – a web-application for pattern-based ontology class generation

TermGenie – a web-application for pattern-based ontology class generation

TermGenie uses Java servlets mainly as abstraction layer, but we make use of the built-in session handling mech- anism. The session is used to store the relevant tokens for the authentication of users. For the authentica- tion, TermGenie currently relies on Persona [14] as a lightweight service. Persona is a 3rd-party (non-profit and open source) protocol and service, which uses an e-mail address as primary identifier. It provides a conve- nient JavaScript client library and easy server-side calls for token verification. Once a TermGenie session has been authenticated, the authorization module uses the e-mail address as primary identifier to check whether the user has the appropriate permissions for the requested opera- tion. TermGenie has different sets of permissions depend- ing on the tasks. For example, the submission of classes requires a different set of permissions than the TermGenie management console for administrators.
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Hermes: an Ontology Based News Personalization Portal

Hermes: an Ontology Based News Personalization Portal

In order to provide a structured framework for searching news messages on the Internet, we propose a solution (called Hermes) that is based on various Semantic Web technologies. By using these tech- nologies, clear semantics can be assigned to the various news messages, thereby providing a basis for a structured overview of the news. The technologies that we used include the data description languages RDF (Resource Description Framework) [2, 3], OWL (Web Ontology Language) [4], and the correspond- ing query language SPARQL (SPARQL Protocol And Query Language) [5]. Our approach is based on a conceptual model for storing concepts, news items, and relations between these concepts and news items; a model that came into being after an extensive researching phase. In this paper, we present our findings regarding this research and we show an implementation of our solution, with special focus on the NASDAQ domain.
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Web Log Analyzer for Semantic Web Mining

Web Log Analyzer for Semantic Web Mining

usage mining is data filtering and pre-processing. In that phase, Web log data should be cleaned or enhanced, and user, session and page view identification should be performed. Web personalization is a domain that has been recently gaining great momentum not only in the research area, where many research teams have addressed this problem from different perspectives, but also in the industrial area, where there exists a variety of tools and applications addressing one or more modules of the personalization process. Enterprises expect that by exploiting the information hidden in their Web server logs they could discover the interactions between their Web site visitors and the products offered through their Web site. Using such information, they can optimize their site in order to increase sales and ensure customer retention. Apart from Web usage mining, user profiling techniques are also employed in order to form a complete customer profile. Lately, there is an effort to incorporate Web content in the recommendation process, in order to enhance the
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Website Personalization based on Link Analysis,          Navigational Patterns and Web Content

Website Personalization based on Link Analysis, Navigational Patterns and Web Content

A very important use of web mining is website personalization. The system used for web site personalization is called as recommendation system uses many web mining techniques based on Big Data Analytics, Artificial Intelligence and Information Retrieval. Website personalization can be defined as adopting the requirements or preferences of users for managing the contents of the website. This dynamic website content generation can be achieved with two methodologies. One easy approach is to present a menu for the user, so he/she can manually select the preference and change the contents. In web site personalization, the part of collecting and analysing information about web and user is called as web mining which deals with web structure mining, web content mining and web usage mining.
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Ontology Based Data Mining Approach on Web Documents

Ontology Based Data Mining Approach on Web Documents

Nowadays Word Wide Web is a main resource which everyone uses to extract required information. The information on the Web is huge and unstructured. Search engines are examined to facilitate in formation extraction. Key phrases which include one or more key words are descriptors that help reader to recognize topics and main concepts included in the documents. Informally we can say that key phrases indicate major idea of document. Key phrases are used in many text-based applications such as search engines. They are also useful in text clustering and text summarization.[7]
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Search Results From the Web Databases Using          Ontology-Assisted Data Extraction

Search Results From the Web Databases Using Ontology-Assisted Data Extraction

Here alignment of the resultant data is a vital pace in obtaining correct explanation. Many of the presented routine data alignment methods are based on not a single feature, but also on more. Frequently used characteristic is HTML tag paths. In this, the sub trees related to two different data units in dissimilar SRRs but which have the same concept i.e., having the same tag structure. On the other hand, it can’t say that this assumption is not always accurate as the tag tree is very responsive to even slight dissimilarities, which may be occurred by the need to highlight certain data units or erroneous coding.
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Personalization and Clustering of Similar Web Pages

Personalization and Clustering of Similar Web Pages

Conventional document retrieval systems return long lists of ranked documents that users are forced to go through to find relevant documents. The majority of today's Web search engines (e.g., Excite, AltaVista) follow this paradigm. Moreover, data representation and data management are also vital problems in the world of online documents as the user’s satisfaction is not confine to the single document rather he is interested in seeing many similar results for the purpose of increased reliability. But, currently, the situation is something different. User gets loads of results that may satisfy his search to the fullest but these results are not in proper form or in other words that there is deficiency in the arrangement of various pages. Due to the properties of the huge, diverse, dynamic and unstructured nature of Web data, Web search [2] has encountered a lot of challenges, such as scalability, multimedia and temporal issues etc. As a result, Web users are always drowning in an “ocean” of information and facing the problem of information overload when interacting with the web.
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Web page recommendation model for web personalization

Web page recommendation model for web personalization

In all experiments, we used recommendation thresholds varying from 0.1 to 1.0 to measure the precision and coverage. The window size is set to 2 as the mean transac- tion length of the data is 3. As been expected, the high minimum support produces fewer recommendations candidate, which affects the coverage while lower minimum support cause lots of irrelevant recommendations generated so as with the recommen- dation threshold. Thus, the right selection of both values is critical in providing the recommendation engine with appropriate numbers of rules for better recommendation.
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Web Personalization Systems and Web Usage Mining: A Review

Web Personalization Systems and Web Usage Mining: A Review

[A. Vaishnavi, 2012] proposed a technique for developing web personalization system using Modified Fuzzy Possibilistic C Means (MFPCM). The author claims that this approach raises the possibility that URLs presented before a user will be of his interest. [Dimitrios Pierrakos et al., 2012] presented a system that builds and maintains community web directories by employing a web usage mining framework that offers a range of personalization functionalities. It was named as OurDMOZ, which includes adaptive interfaces and web page recommendations. [Bin Xu et al., 2012] extends the traditional clustering collaborative filtering models. They formulate the Multiclass Co-Clustering (MCoC) problem and propose an effective solution to it in order to find meaningful subgroups. They also propose a unified framework that extends the traditional collaborative filtering algorithms by utilizing the subgroups information, for improving their top-N recommendation performance.
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An Implementation of Web Personalization using Web Mining Techniques

An Implementation of Web Personalization using Web Mining Techniques

computing. Depending on the kinds of data to be mined or on the given data mining application, the data mining system may also integrate techniques from spatial data analysis, information retrieval, pattern recognition, image analysis, signal processing, computer graphics, Web technology, economics, business, bioinformatics, or psychology. Because of the diversity of disciplines contributing to data mining, data mining research is expected to generate a large variety of data mining systems. Therefore, it is necessary to provide a clear classification of data mining systems, which may help potential users distinguish between such systems and identify those that best match their needs. Data mining systems can be categorized according to various criteria, as follows:
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An Approach to Cluster Web Pages for          Personalization of Web Search

An Approach to Cluster Web Pages for Personalization of Web Search

We have collected dataset available from www.cs.umass.edu/~ronb[8]. This dataset consists of 1085 Web pages which are collected from Melinda Gervasio’s social network which consists of 12 names of people. According to the person’s occupation, the dataset is labeled. Out of 12 people, two people are unique on the Web, while rest have relatively common names. There are some names that appear extremely ambiguous, e.g. given a query “Tom Mitchell”, 37 different Tom Mitchells were found within the first 100 Google hits. There are web pages of 187 unique people in the dataset, while only 12 of them were relevant. The detailed statistics and overall process is given in [8]. These 12 names are then issued as queries to the Google and first 100 pages were retrieved for each query issued. We did manual filtration of the pages by removing pages in non-textual formats, HTTPD error pages and empty pages. After that we labeled the remaining pages by the occupation of the individuals whose name appeared in the query. TableI shows statistics of the dataset.
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