Web page recommendation

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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

In proposed system, web page recommendation will be done by using multiple website in same domain. The system will take the log files from the websites as the input. The semantic analysis of domain terms in different sites will be give effective recommendation results. The existing system works with static Web pages. For dynamic web page recommendations advanced tools will be generated to identify and collect more appropriate web usage data than web logs. Dynamic web click stream analysis will be conducted in the data preparation stage, in which the web page will be identified as dynamic contents rather than static pages. Synonyms of the domain
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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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Use Of Ontology And Web Usage Mining For Web Page Recommendation

Use Of Ontology And Web Usage Mining For Web Page Recommendation

VI. CONCLUSION AND FUTURE RESEARCH Dominion Ontology Semantic Search matches the semantic content with the user given query. The web search results more suitable to the user query are extracted after the syntactic and semantic evaluation during context analysis in structured dominion ontology. This paper has presented a new method to offer better Web-page recommendations through semantic enhancement by three new knowledge representation models. Two new models have been proposed for representation of domain knowledge of a website. One is an ontology-based model which can be semi-automatically constructed, namely DomainOntoWP, and the other is a semantic network of Web-pages, which can be automatically constructed, namely TermNetWP. A conceptual prediction model is also proposed to integrate the Web usage and domain knowledge to form a weighted semantic network of frequently viewed terms, namely TermNavNet. A number of Web-page recommendation strategies have been proposed to predict next Web-page requests of users through querying the knowledge bases. The experimental results are promising and are indicative of the usefulness of the proposed models.Compared with one of the most advanced Web usage mining method, i.e. PLWAP-Mine, the proposed method can substantially enhance the performance of Web-page recommendation in terms of precision and satisfaction. Moreimportnatly, this method is able to alleviate the ―new-page‖problem mentioned in the introduction because it based on not only the Web usage knowledge, but also the semantics of Web-pages.
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A Web Page Recommendation using Naive Bayes Algorithm in Hybrid Approach

A Web Page Recommendation using Naive Bayes Algorithm in Hybrid Approach

Web page recommendation has been emerging as a most important application area in mining. In order to predict the users’ interests for effective recommendation two methods such as collaborative filtering and content based filtering are considered. Content based filtering is applied by considering information including user’s profile and the users’ past preferences. User preferences and similarity with other users are considered as primary factor in collaborative filtering method. In probabilistic generative the unobserved user preferences are also considered along with ratings and semantic content. To improve the accuracy and to still improve the user satisfaction this paper applies Naïve- Bayes classifier along with content and collaborative based approach. Naive-Bayes classifier is considered to be more efficient as it considers dynamic and adaptive features for accurate classification. The features that are considered in Naive-Bayes classifier are independent to each other. The performance of the proposed algorithm is measured using the precision and recall.
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Web page recommendation model for web personalization

Web page recommendation model for web personalization

Web personalization can be described as any action that can customized the content or structure of a Web site to the user’s taste or preferences has widely been utilized by e- commerce organizations to better serve their customers. The actions can be made by highlighting the hyperlinks, inserting new hyperlinks that seem to be of interest for the current user dynamically, and the creation of new index pages. The common personal- ization systems for the Web can be categorized into three groups, which is manual decision rule system, content-based filtering agents and collaborative filtering systems [4]. Of all, the collaborative filtering system has become the predominant approach in furnishing the e-commerce system with an intelligence to capture user profiles and recommending relevant pages to the users. However, it suffers some limitations, which are mostly related to the scalability and efficiency [7]. Item-based similarity [8] and dimension reduction was proposed by some researchers to overcome the drawback.
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Enhancement in Next Web Page Recommendation with the help of Multi- Attribute Weight Prophecy

Enhancement in Next Web Page Recommendation with the help of Multi- Attribute Weight Prophecy

In Today internet world has increasing number of websites so it’s the big task to get accurate data from large numbers of website. The web data mining is one of the challenging task .While performing the web page prediction pre-processing of the data from a web site. The necessity for predicting the user’s needs in order to enhance the usability and user maintenance of a web site is more than marked now a day’s lacking proper guidance, a visitor often wanders aimlessly without visiting significant pages, loses attention, and leaves the site earlier than expected. When they access the network, a large amount of data is generated and is stored in Web log files which can be used efficiently as many times user frequently searched the same type of Web pages recorded in the log files. These sequence can be considered as a web access pattern, valuable to find the user behavior Through this custom-made information, it’s quite easy to forecast the next set of pages user might visit based on the previously searched patterns, thereby reducing the browsing time of a user.
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Personalized Web Page Recommendation System with Diversified Ranking

Personalized Web Page Recommendation System with Diversified Ranking

The aforementioned three techniques [1, 2, 3] model the variance of topics in groups of documents. They all have difficulties applying the concept of diversity to Web-search, e.g., in , the authors assume that all pages are labeled with the topics they cover, then rank them to roughly improve the number of topics covered by a set of pages. Clearly, in the most applicable case of Web-search, such labeling is not available a priori. For that reason, we do not compare our search engine to experimentally, rather we chose to compare our results to state-of-the-art search engines such as Google’s, for which we speculate that they address the problem of result diversity. In contrast to prior known methods that focus on maximizing diversity, the technology introduced in this work aims at modeling the overall finite knowledge space for a specific query and improving the coverage of this space by a set of documents. We propose a “sack-of-words” model for representing knowledge spaces, introduce a formal notion of coverage over the “sack-of- words,” and derive a simple but systematic algorithm to select documents that maximize coverage, while being relevant to the search topic.
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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 Personalized Search Engine with Secure Data Storage in Cloud

A Personalized Search Engine with Secure Data Storage in Cloud

information to practice philosophy. This web service provides a completely distinctive methodology to give more net page recommendation supported internet usage and domain info, that's supported by three new info illustration models and a bunch of Web-page recommendation ways: 1 ) Associate in the Nursing ontology-based model that provides the domain info of an online website. The event of this model is semi-automated so as that the event efforts from developers are going to low. 2) The second model could also be linguistics network that shows domain info, whose construction is going to be completely automatic. It’s going to be merely incorporated into web-page methodology due to it automatic feature. 3) This model could also be an abstract prediction model, this will be a map network of domain terms supported the off times viewed internet-pages. The recommendation ways build domain uses info and additionally the prediction model through a set of the 3 models to predict consecutive pages with potentialities for a given net user supported his or her current Web-page position state. An honest extent, this methodology is automatic the content erection and eased the new- page downside as provided above. Some models, like ordered modeling, have shown their vital effectiveness on recommendation generation. Some research, have displayed that tree-based algorithms, notably Pre-Order coupled WAP-Tree Mining area unit excellent in supporting Web-page recommendation, compared with various sequence mining algorithms. Moreover, blending of PLWAP Mine and therefore the increasing-order Andre Mark off model can considerably value of mining performance
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Web Page Recommender System for Effective Information Retrieval using hybridization of Trust, ACO and GA

Web Page Recommender System for Effective Information Retrieval using hybridization of Trust, ACO and GA

ABSTRACT: In this paper novel method is proposed for web page recommendation using hybridization of Trust, Ant Colony Optimization (ACO) and Genetic Algorithm (GA) for effective Information retrieval. The proposed approach uses the trusted colonies of web pages in a given cluster domain for rank optimization using GA in order to recommend relevant documents up in ranking for effective information retrieval. The user’s clicks to the recommended web pages is captured online using pheromone update in ACO for optimizing the path of trusted clicked URLs and uses GA for their optimal ranking. The process of recommendation of optimal ranked set of clicked URLs continues till the search is personalized to the information need of the user. Experiment was conducted to confirm the improvement of precision of search results using the proposed method.
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TOWARDS SECURE CLOUD COMPUTING USING DIGITAL SIGNATURE

TOWARDS SECURE CLOUD COMPUTING USING DIGITAL SIGNATURE

An efficient private searching keyword on frequently visited web-page which ensures user authentication and access control in a privacy preserving way using homomorphic encryption technique has been implemented. The features of websites are enhanced according to the user’s interest on web based applications in an optimised web search engine. The Web-page recommendation is developed to offer Web users the top-N most commonly visited Web-pages from the currently visited Webpage. The knowledge bases used in the system, includes the website domain and Web usage knowledge bases, are represented by ontological-style semantic networks which can be implemented consistently in a formal Web Ontology Language. The current system works with static Web-pages. With the advancement in Web technology, pages have been evolving into pages with dynamic structures. To offer more effective Web-page recommendations, it will be highly desirable to develop advanced tools to identify and collect more appropriate Web usage data than Web logs, such as click stream data. Websites have been evolving over time therefore the knowledge bases, i.e. domain and Web usage knowledge bases, need to be updated accordingly. Considering the traditional Web usage data source, which is the Web log file, the system can only take a limited segment of the log file to build the Web usage knowledge base due to the fact that the size of the log file can be huge. The future work can be focused on the discovered Web usage knowledge is up-to date, new methods need to be developed to dynamically update the knowledge bases.
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Recommendation System for Web Mining: A Review

Recommendation System for Web Mining: A Review

The main disadvantage is the result shows only of most viewed products. And frequency based technique give worst result as compare to model based collaborative filtering [19]. A technique for producing personalized web page recommendation. First, from user browsing log, we recognize users who find the interesting pages before others, we called them early adepter [30]. In the previous approaches, the cluster of similar users has been used. In this approach, the log to build a weighted, directed graph. In the graph, node represents user and edge represents that the user on both node access the same page.
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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

Web Page recommendation has become increasingly popular, and is shown as links to related stories, related books, or most viewed pages at websites. Web Recommendation system is a specific type of information filtering system technique that attempts to predict the user next browsing activity then recommend to the user web pages items that are likely to be of interest to the user. A recommender system is a typical software solution used in e-commerce for personalized services. Based on the customer preferences, it helps to find the products they would like to purchase by providing recommendations and is particularly useful in ecommerce sites that offer millions of products for sale [8].
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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

Web-page recommendation plays an important role in intelligent Web systems. Useful knowledge discovery from Web usage data and satisfactory knowledge representation for effective Web-page recommendations are crucial and challenging. This paper proposes a novel method to efficiently provide better Web-page recommendation through semantic enhancement by integrating the domain and Web usage knowledge of a website

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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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Musical Recommendation on Thematic Web Radio

Musical Recommendation on Thematic Web Radio

We have found a macroaverage recall of 43.25%. It is important to state that each user received 20 recommendations. This is an acceptable value as the query construction was made automatically without human intervention. It happened to be lower than it should be if we have used more songs, maybe access to MySpace music, but the problem is the limited songs for singer or band. Other important consideration is that the recommendation ranking was generated with a depreciation degree that was dependent on the promotion year and on the user language, as explained in the previous section. As the time-slice considered corresponds to a small part of the full period stored in the database related with the Thematic Web Radio, not all songs are good recommendations since the preferences changes along the time.
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Study of Recommendation System for Web Portals

Study of Recommendation System for Web Portals

Burke, 2002 defined Recommendation system as “any system that produces individualized recommendations as output or has the effect of guiding the user in a personalized way to interesting or useful objects in a large space of possible options” [1]. Recommendation system is a dynamic process of learning from Customers behavior of traditional information by which machine learns and do various texts mining analysis to recommend product’s rating or score. It is found in many applications to help their customer for judging of products. This system produces a list on the basis of scores or ratings that is done by Recommendation system algorithms by which customer can decide appropriate items.
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Legal Semantic Web- A Recommendation System

Legal Semantic Web- A Recommendation System

2.2.1 Information retrieval. By encoding knowledge with meaning, concepts and the relations, it greatly empowers the users of information retrieval systems. Information about level of specificity of concepts can help the user to find information relevant to his query. Examples of relevant publications and projects are Matthijssen (1999), who introduces an interface between the lay user and a legal database, LOIS (which stands for ‘Lexical Ontologies for legal Information Sharing’, cf. Dini et al. 2005) and BEST (which stands for ‘Batna Establishment using Semantic web Technology’, cf. Van Laarschot et al. 2005). [11]
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A Survey on Recommendation System on Web Services

A Survey on Recommendation System on Web Services

user profiles. The user profile merging is totally based on total distance minimization. So it guarantees that the merged results are accurate that is close to user preferences. In Collaborative filtering recommendation a user with a group of same choices based on preference over all the items and recommends to the user those items which is enjoyed by others in the group [6]. There are many user items subgroups each consist of a same choice users on these items. For this the U-Matrix represents the subgroups. In some systems on the basis of different parameters we can conclude collaborative filtering using Pearson Correlation Coefficient. Collaborative filtering have two forms 1. Prediction 2. Recommendation. To get Qos values they proposed a Qos ranking prediction by observing past services used experiences of consumer [7]. The ranking similarity computation find out the comparison between two user Qos ranking.For the same set of services on two ranking the Kendall rank correlation coefficient is used. It evaluates the degree of similarity.
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