Top PDF Web Page Recommendation using Domain Knowledge and Web Usage Knowledge

Web Page Recommendation using Domain Knowledge and Web Usage Knowledge

Web Page Recommendation using Domain Knowledge and Web Usage Knowledge

Finally this paper provide better webpage recommendation using methods which are the first model is the ontological model that can be semi-automatically constructed namely DomainOntoWP for domain term extraction. Next means second is semantic network analysis model, namely TermNetWP for extracting relationship between domain terms and web pages and the third model is nothing but conceptual prediction model is also proposed to namely TermNavNet for extracting web usage knowledge. To improve performance Key information extraction
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A Survey on Domain Relevant Web-Page Recommendation Using Ontology and Web Usage Knowledge

A Survey on Domain Relevant Web-Page Recommendation Using Ontology and Web Usage Knowledge

ABSTRACT: The World Wide Web provides different kind of web recommendations which are made available to users every day that includes image, audio, video, query suggestion and web page. Web-page recommendations play an important role in intelligent web system. Web Usage mining is the process of retrieving useful knowledge such as sequential patterns from web usage log. The predicted navigational patterns from the user’s web usage log are used to provide personalized web-page recommendations. This paper proposes better web-page recommendation by integrating web usage and domain knowledge via the three new knowledge representation models and a set of web-page recommendation strategies. First model is an ontology-based model which is used to represent the domain knowledge of a website or captures the domain of interest of particular user. The second model is the semantic network, which is a kind of knowledge map which represents domain terms, web-pages and relationship between them. The third model is the Conceptual Prediction Model (CPM), which is the combination of domain knowledge and web-usage knowledge .The recommendation strategies make use of the domain knowledge and prediction model to predict and recommend the next web-pages. This web-page recommendation system provides higher performance than existing Web Usage Mining (WUM) method.
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A Survey on Domain Relevant Web-Page Recommendation Using Ontology and Web Usage Knowledge

A Survey on Domain Relevant Web-Page Recommendation Using Ontology and Web Usage Knowledge

ABSTRACT: The World Wide Web provides different kind of web recommendations which are made available to users every day that includes image, audio, video, query suggestion and web page. Web-page recommendations play an important role in intelligent web system. Web Usage mining is the process of retrieving useful knowledge such as sequential patterns from web usage log. The predicted navigational patterns from the user’s web usage log are used to provide personalized web-page recommendations. This paper uses better web-page recommendation by integrating web usage and domain knowledge via the three new knowledge representation models and a set of web-page recommendation strategies. First model is an ontology-based model which is used to represent the domain knowledge of a website or captures the domain of interest of particular user. The second model is the semantic network, which is a kind of knowledge map which represents domain terms, web-pages and relationship between them. The third model is the Conceptual Prediction Model (CPM), which is the combination of domain knowledge and web-usage knowledge. The recommendation strategies make use of the domain knowledge and prediction model to predict and recommend the next web-pages.
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Efficient Knowledge Representation Integrated with Web Usage Mining for Webpage Recommendation

Efficient Knowledge Representation Integrated with Web Usage Mining for Webpage Recommendation

With the substantial growth of information on the internet, webpage recommendation has become crucial part of the websites today. In the existing systems that incorporate web usage mining, the recommendation results are limited within the frequent web access patterns generated from input web log for example, e-commerce websites suggest the user with product relevant to web usage history of that user. To enhance the performance of webpage recommendation beyond frequent web access sequence, the proposed system introduces clustering of documents containing webpage information which generates the domain knowledge. The use of domain knowledge along with web usage knowledge generated by discovering frequent web access sequences using CM-SPADE algorithm, provides semantic enhancement in the existing webpage recommendation system. The most relevant searches can be obtained by preferring the frequently accessed webpage contained within same cluster of the current web access of the user. With this approach, the accuracy of recommended webpages can be improved. The experimental results show that the proposed system has increased the performance of webpage recommendation in terms of time required to get recommendation results.
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A Paper on Multisite Framework for Web page Recommendation Using Incremental Mining

A Paper on Multisite Framework for Web page Recommendation Using Incremental Mining

Prediction model will develop for generating weighted semantic knowledge of frequently viewed terms pattern. Synonyms of domain term will be used for enhancing domain knowledge discovery. Also to update knowledge bases dynamically incremental mining method will be used. This model will use frequent view term pattern for giving the weight to frequently viewed terms pattern. Weight is probability of the transition between two adjacent terms based on frequently viewed term patterns. Also synonyms of users domain term will be find out and weight is applied to it. Weighted semantic knowledge of frequently viewed terms will use for semantic enhanced web page recommendation.
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TOWARDS SECURE CLOUD COMPUTING USING DIGITAL SIGNATURE

TOWARDS SECURE CLOUD COMPUTING USING DIGITAL SIGNATURE

With the rapid growth of internet technologies, the web has become the world's largest repository of knowledge. So, it is challenging task of the webmasters to organize the contents of the particular websites to gather the needs of the users. Optimised search engine is used for effectively searching keyword queries which are frequently visited by the user based on their interest. The most frequently used keyword queries are encrypted using homomorphic encryption which are semantically secured through searching mechanism. This paper presents a new framework for a semantic-enhanced Web Page Recommendation (WPR), and a suite of enabling techniques which include semantic network models of domain knowledge and Web usage knowledge, querying techniques, and Web-page recommendation strategies.Time based ranking technique is used to calculate the time taken by each user for visiting the recommended web-pages and also to keep record of the number of users. The privacy of the user is thus enhanced by using the clustered web-pages on the search engine. The proposed technique also provides a set of clustered web- pages which are effectively filtered through private searching keyword query and also provides better web- page features in order to satisfy the user’s requirements.
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Use Of Ontology And Web Usage Mining For Web Page Recommendation

Use Of Ontology And Web Usage Mining For Web Page Recommendation

The Web usage knowledge can be discovered from Web usage data through unsupervised learning processes, such as sequential pattern mining techniques, but without the semantics of Web-pages, the discovered knowledge are limited in supporting Web-page recommendation, such as no alleviation to the ―new page‖ problem. Domain ontology is really useful to enhance a Web-page recommendation process by adding semantics to Web-pages, but how to build effective domain ontology for Web-page recommendations is always a big challenge. The study presented in this chapter builds domain ontology of Web-pages of a website that can be used to interpret the semantics of Web-pages. This chapter proposes a domain ontology model that represents the domain concepts, Web-pages, and the relations among them for a given website to support semantic-enhanced Web-page recommendation and also presents a novel method to build such domain ontology for a website.
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An Intelligent Web System by Integrating Domain and Web Usage Knowledge

An Intelligent Web System by Integrating Domain and Web Usage Knowledge

Given a set of frequently viewed term patterns, namely FVTP, the WebNav is generated by populating the CPM schema with FVTP. The CPM schema is designed using the formal ontology web language, RDF. An algorithm is also accomplished to perform this task. The transition probabilities is upgradable based on the first-order or second-order probability formula depending on the applied CPM’s order. Thus, 1st or 2ndorder WebNav is obtained by using the 1st or 2nd-order CPM, respectively. For a given current Web-page or a combination of the current and previous Web-pages, the next Web-pages is recommended differently depending on which knowledge representation model and the order of CPM are used as mentioned earlier. These recommendation methods make utilization of the domain knowledge and the prediction model through two of the three models to forecast the enclosing pages with probabilities for a given Web client depended on his or her current Web-page navigation state. All things considered in this new system is fully automated. The knowledge base implementation has improved the new-page issue as specified previously. This technique yields better performance contrasted with the current Web usage based Web-page recommendation frameworks.
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Recommendations on Web Page Using Domain Knowledge and Web Usage Mining For Personalization
Yagnasri Ashwini & K Kavitha

Recommendations on Web Page Using Domain Knowledge and Web Usage Mining For Personalization Yagnasri Ashwini & K Kavitha

The recommendations. Recent studies have advised that domain information of the online application within the kind of metaphysics will play a vital role in providing smarter and additional comprehensive recommender sys- tems. Hence Associate in nursing increasing effort is re- quired in process the online pages and objects in terms of linguistics data within the kind of ontology’s. the mix of internet Usage Mining and linguistics internet has created a brand new and quick rising analysis space - linguistics internet Mining. The linguistics internet is predicated on a vision of Tim Berners-Lee, the creator of the web. The linguistics internet enriches the web by machine method ready data that supports the user in accomplishing his tasks additional simply [6]. The vision of a linguistics in- ternet has recently drawn significant attention each from tutorial and industrial circles. the concept behind mistreat- ment the linguistics internet for generating customized in- ternet expertise is to boost the results of internet mining by exploiting the new linguistics structures [2]. As a con- sequence of the higher than concerns there’s Associate in Nursing increasing effort in process websites and objects in terms of linguistics data by mistreatment metaphysics. This paper presents a completely unique methodology to produce higher internet page recommendation supported Web usage and domain information , that is supported by 3 new information illustration models and a group of Web-page recommendation methods. The primary model is Associate in Nursing ontology-based model that repre- sents the domain information of an internet site. the devel- opment of this model is semi-automated in order that the event efforts from developers will be reduced. The sec- ond model may be a linguistics network that represents domain information, whose construction will be totally automatic. This model will be simply incorporated into a Web-page recommendation method owing to this totally automatic feature.
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Web Page Prediction System Based on Web Logs and Web Domain Using Cluster Technique

Web Page Prediction System Based on Web Logs and Web Domain Using Cluster Technique

ABSTRACT: In intelligent web system web recommendation plays important role. In web mining, for web recommendation system the knowledge discovery and representation of information is an important and crucial task. Here in this paper new method is introduce to efficiently provide better Web-page recommendation generations through semantic-enhancement by integrating the domain and Web usage knowledge of a website. By the help of knowledge discovery user profile is created to block suspicious user that are harmful for websites or server. This model uses semantic web network to represent relations between domain, Web-pages & websites. Other model, the conceptual model, is proposed to auto generate a semantic web network of the semantic Web usage knowledge, which is the integrated with domain knowledge and Web usage knowledge.
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Web Log Analyzer for Semantic Web Mining

Web Log Analyzer for Semantic Web Mining

Web mining is the application of data mining techniques to extract knowledge from web data, i.e. web content, web structure and web usage data. Web personalization is the process of customizing the content and structure of a website for specifically needs. It involves application of data mining techniques on the contents of WWW but is not limited to it. Web site personalization can be defined as the process of customizing the content and structure of a Web site to the specific and individual needs of each user taking advantage of the user’s navigational behavior. Websites collect technical information about your computer, such as the size of your screen or type of the browser you use. This information helps web designer’s format websites in a way that is websites also collect information related to your activity on the web, such as your internet protocol (IP) address. The time you clicked on a link, how much time you spent on a particular web page before moving onto the next one, and the web page you were reading prior to clicking the link. This information, when aggregated with similar information about other users, is extremely valuable to advertisers.
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Web page recommendation model for web personalization

Web page recommendation model for web personalization

Abstract. Web usage mining has gained more popularity among researchers in discovering the users browsing behavior mining the web server log that records all the users’ transactions activities. In this paper, we developed a usage model for predictions based on association rule. Similarity between items contained in the active user profile will be calculated upon the matched rules and finally the top-N most similar items are then recommended to the user. We used the time spent on each page for weighting the pages instead of binary. Two evaluation metrics were applied to evaluate the accuracy of the recommendations, namely precision and coverage.
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WEB CONTENT MINING METHODS AND APPLICATIONS FOR INFORMATION EXTRACTION: A SURVEY

WEB CONTENT MINING METHODS AND APPLICATIONS FOR INFORMATION EXTRACTION: A SURVEY

The process of discovering structures information from the web documents are called as web structure mining. This mining can be performed either document level or hyperlink level. The hyperlinks provide clear navigation and point to the pages. This is used to retrieve the useful information in the form of structure. Hyperlink analysis can be done based on knowledge models, scope and properties of analysis and types of algorithms. The methods that are done in the web usage mining are Data cleaning, Transaction identification, Data integration, Transformation, Pattern Discovery, Pattern Analysis. This kind of mining emphasizes on the data which describes the structure of the content. It is classified into two types namely intra-page structure and inter-page structure. Intra-page structure means the existence of links within a page. No separate page will be opened in this case. Inter-page structure involves the connection of one page with the other page [3][2].
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A Model For Analysis Most Visited Web Page For Web Usage Mining

A Model For Analysis Most Visited Web Page For Web Usage Mining

Abstract: Weblog analysis takes raw data from access log and performs study on this data for extracting statistical information. These info incorporates a variety of data for the website activity such as average no. of hits, total no. of user visits, failed & successful cached hits, average time of view, average path length over a website and analytical information such as page was not found errors and server errors, server information which includes exit and entry pages, single access pages and top visited pages, requester information like which type of search engines is used, keywords and top referring sites and so on. In general, the website administrator uses this kind of knowledge to make better the system act, helping in the manipulation process of site, then also forgiving marketing decisions support. Most of the advanced Web mining systems practice this kind of informat ion to take out more difficult or complex interpretation those take learning, using data mining procedures like association rules, clustering, and classification etc.
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Web Personalization Using Web Usage Mining

Web Personalization Using Web Usage Mining

The above Fig. 1 shows the types and sources of Web mining. Web Content Mining is the process of extracting useful information from the contents of Web documents. Content data corresponds to the collection of facts a Web page was designed to convey to the users. It may consist of text, images, audio, video, or structured records such as lists and tables [5]. Research in web content mining encompasses resource discovery from the web, document categorization and clustering, and information extraction from web pages [6]. Web structure mining studies the web‟s hyperlink structure. It usually involves analysis of the in-links and outlinks of a web page, and it has been used for search engine result ranking. [6]. Web Structure Mining can be regarded as the process of discovering structure information from the Web. This type of mining can be performed either at the(intra-page) document level or at the (inter-page) hyperlink level [5]. Web structure mining is the process of inferring knowledge from the World Wide Web organization and links between references and referents in the Web [7].Web Usage Mining is the application of data mining techniques to discover interesting usage patterns from Web data, in order to understand and better serve the needs of Web based applications. It also called as Web log mining. Some of the typical usage data collected at a Web site includes IP addresses, page references, and access time of the users. [5]
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Knowledge based Recommendation System in Semantic Web   A Survey

Knowledge based Recommendation System in Semantic Web A Survey

Rule-based reasoning approaches can be used for personalization of Semantic Web by writing rules for implementing personalization logic. Rules represent knowledge with conditions in some domain of logic, such as, first order logic. A rule is defined as ‘If-then’ clause containing logical functions and operations, which can be expressed in a rule language. If-clause specifies the condition or premises and then- clause specifies the conclusion or action to be taken. If conditions are true in if-clause, then the conclusion or action will be carried out in the then-clause. Reasoning based approaches for personalization in Semantic Web were first proposed by Antoniou et al. [24]. They categorized approaches into Monotonic, Non-monotonic, Evolution updates and events, and reasoning about actions. Monotonic reasoning is static. The truth of statement does not change when new information is added and this type of reasoning is performed by DL reasoner which is based on Open World Assumption which allows easy integration of new information and the existing information truth value is not affected with the addition of new information.
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An Implementation of Web Recommendation System using Web Usage Mining Technique

An Implementation of Web Recommendation System using Web Usage Mining Technique

system itself for recommender systems to be successful; they need to achieve a certain level of accuracy in their recommendations that is acceptable to the users. The continuous growth in the size and use of the World Wide Web imposes new methods of design and development of online information services. Most Web structures are large and complicated and users often miss the goal of their inquiry, or receive ambiguous results when they try to navigate through them. Therefore, the requirement for predicting user needs in order to improve the usability and user retention of a Web site can be addressed by personalizing it. The huge and ever increasing amount, complexity and heterogeneity of available digital information overwhelm the human processing capabilities in a wide array of information seeking and e- commerce tasks. To cope with information overload recommender systems have been introduced to filter those items –Web pages, images, videos, audio– that are of low relevance or utility for the user, and present only a small selection better suiting the user’s tastes, interests, and priorities. Often these suggestions are presented while the user is browsing an information service, and without requiring her to launch explicit search queries, as is usually done in information retrieval systems [3] [4] [5].
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Review on Secure Advanced Web Search Personalization Using Domain Knowledge

Review on Secure Advanced Web Search Personalization Using Domain Knowledge

The web search engine is good source for ordinary people to looking for useful information on the web. However, users experiences are sometimes bad when search engines return results that do not match with its needs . Such irrelevance is largely due to the enormous variety of users’ contexts and backgrounds, as well as the ambiguity of texts. Personalized web search (PWS) is a general category of search techniques aiming at providing better search results, which are tailored for individual user needs. As the cost, client data must be gathered and dissected to make sense of the client goal behind the requested query. The answers for PWS can for the most part be categorized into two sorts, to be specifically click log-based methods and profile-based ones. The click log based methods are clear—they just force inclination to clicked pages in the client's query history. In spite of the fact that this procedure has been exhibited to perform reliably and impressively well, it can just deal with repeated queries from the same client, which is a strong limitation confining its applicability. Interestingly, profile-based methods enhance the search experience with confused client interest models created from client profiling strategies. Profile-based methods can be possibly compelling for a wide range of questions, however are accounted for to be insecure under a few circumstances. Although there are pros and cons for both types of PWS techniques, In fact, privacy concerns have become the major barrier for wide proliferation of PWS services.
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An Efficient Web Recommendation System using Collaborative Filtering and Pattern Discovery Algorithms

An Efficient Web Recommendation System using Collaborative Filtering and Pattern Discovery Algorithms

Web usage mining techniques [23] is used by the researchers for determining the interest of “similar” Users. The complete process for recommendation broadly consists of two components: offline component and online component. The offline component involves Data Preprocessing, Pattern Discovery and Pattern Analysis. The outcome of the offline component is the derivation of aggregate usage profiles using web usage mining techniques. The online component is responsible for matching the current user’s profile to the aggregate usage profiles. The scope of this paper is to match an online user’s navigational activity with the aggregate usage profiles obtained through mining tasks and provide suitable page recommendations which may be of interest to the user. Recommendation [3] is done the authors by combining collaborative and content based method. They have used web content mining as the source and Fuzzy C-Means and Ant colony clustering techniques are applied to the web contents as the offline process. A hybrid recommendation systems checked the page matching with the previous similar users and done the suitable recommendation. [24] have done the recommendation based on collaborative filtering technique only for the trustworthy customers. Entropy based similarity measure is used to identify the similarity between the users. Authors [25] had done research on semantic web personalization. In this paper, web contents are modified based on the user’s searching and navigational behavior. [26] have presented a web page recommendation algorithm using weighted sequential patterns and markov model. [27] proposed a technique that incorporates web recommendation and personalization of websites based on the user interest. They have taken web logs for their source. They have used the data structures such as Web-Interest Matrix, User-Interest Matrix, Class-Interest Matrix and Frequent-Path Matrix to keep track of user interest and change the website based on the impact of the users.
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Web Usage Data based Web Page Recommender System

Web Usage Data based Web Page Recommender System

In [7], researchers have proposed web usage min ing Using Art ific ial Ant Colony Clustering and Genetic Progra mming .Using ant clustering algorith m we can discover web usage patterns and using linear genetic progra mming approach we can analyze the visitors trend(s). In [8], researchers have provided survey of recent developments in web usage mining. In [10], A Survey of Accuracy Evaluation Metrics of Reco mmendation tasks have been carried out. Based upon users surfing behaviour we get u ser informat ion learned fro m user‟s web logs data to construct accurate comprehensive individual user profiles [11]. In [12], authors have given a generic framework that delivers “Contextual recommendations” that are based on the combination of previously gathered user feedback data (i.e. ratings and clickstrea m history),context data, and ontology based content categorization scheme . A web reco mmendation approach which is based on learning fro m web logs and recommends user a list of pages which are relevant to him by co mparing with user‟s historic pattern could be obtained fro m [13]. A co mbined approach of content-based model and me mo ry-based collaborative filtering is used in order to remove drawbacks of e xisting system and used feed forward back propagation neural network for tra ining data [14]. An intelligent approach that explores the idea of applying a semantic recommender system in process plant design is discussed in [15]. In [16], authors have shown experiments based on Markov Logic Network, through which, one can do web page reco mmendation with very high accuracy. Tag Based Reco mmender System for Soc ial Bookmarking sites is discussed in [17] and user‟s preference transition applied for Hotel Reco mmendation System is discussed in [18].
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