scholarly journals Ontology Based Web Page Recommendation System

2019 ◽  
Vol 8 (6S4) ◽  
pp. 1373-1376
Keyword(s):  
Recommendation System ◽  
Semantic Information ◽  
Initial Step ◽  
Domain Ontology ◽  
Web Usage Mining ◽  
The Other ◽  
Web Page ◽  
Web Usage ◽  
Content Based Filtering ◽  
Filtering Approach

The emerging web page development requires semantic applications with customized administrations. The proposed methodology presents a customized suggestion framework, which makes utilization of item representations and also client profiles created based on ontology. The domain ontology helps the recommender to improve the personalization: from one perspective, client’s interests are displayed in an increasingly powerful and precise route by applying an area based derivative technique; on the other side, the stemmer algorithm derived content- based filtering approach, gives an evaluation of resemblance among a thing and a client, upgraded by applying a semantic likeliness strategy. Recommender frameworks and web personalize were assumed by Web usage mining as a critical job. The proposed strategy is s successful framework dependent on ontology and web usage mining. Extricating highlights from web reports and building applicable ideas is the initial step of the methodology. At that point manufacture metaphysics for the site exploit the ideas and huge terms separated from reports. As per the semantic similitude of web archives to bunch them into various semantic topics, the distinctive subjects suggest diverse inclinations. The proposed methodology incorporates semantic information into Web Usage Mining and personalization process

Web Usage Mining Approaches for Web Page Recommendation

2013 ◽  
pp. 271-288
Author(s):  
H. Inbarani ◽  
K. Thangavel
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Decision Rule ◽  
Web Usage Mining ◽  
Web Page ◽  
Web Personalization ◽  
Rule Based ◽  
Web Usage ◽  
User Ratings ◽  
Content Based Filtering

The technology behind personalization or Web page recommendation has undergone tremendous changes, and several Web-based personalization systems have been proposed in recent years. The main goal of Web personalization is to dynamically recommend Web pages based on online behavior of users. Although personalization can be accomplished in numerous ways, most Web personalization techniques fall into four major categories: decision rule-based filtering, content-based filtering, and collaborative filtering and Web usage mining. Decision rule-based filtering reviews users to obtain user demographics or static profiles, and then lets Web sites manually specify rules based on them. It delivers the appropriate content to a particular user based on the rules. However, it is not particularly useful because it depends on users knowing in advance the content that interests them. Content-based filtering relies on items being similar to what a user has liked previously. Collaborative filtering, also called social or group filtering, is the most successful personalization technology to date. Most successful recommender systems on the Web typically use explicit user ratings of products or preferences to sort user profile information into peer groups. It then tells users what products they might want to buy by combining their personal preferences with those of like-minded individuals. However, collaborative filtering has limited use for a new product that no one has seen or rated, and content-based filtering to obtain user profiles might miss novel or surprising information. Additionally, traditional Web personalization techniques, including collaborative or content-based filtering, have other problems, such as reliance on subject user ratings and static profiles or the inability to capture richer semantic relationships among Web objects. To overcome these shortcomings, the new Web personalization tool, nonintrusive personalization, attempts to increasingly incorporate Web usage mining techniques. Web usage mining can help improve the scalability, accuracy, and flexibility of recommender systems. Thus, Web usage mining can reduce the need for obtaining subjective user ratings or registration-based personal preferences. This chapter provides a survey of Web usage mining approaches.

Web Usage Mining through Associative Models

2011 ◽  
pp. 1231-1234 ◽  
Author(s):  
Paolo Giudici ◽  
Paola Cerchiello
Keyword(s):  
Web Site ◽  
Web Usage Mining ◽  
Web Pages ◽  
Web Page ◽  
User Session ◽  
Web Usage ◽  
Associative Models ◽  
The Web

The aim of this contribution is to show how the information, concerning the order in which the pages of a Web site are visited, can be profitably used to predict the visit behaviour at the site. Usually every click corresponds to the visualization of a Web page. Thus, a Web clickstream defines the sequence of the Web pages requested by a user. Such a sequence identifies a user session.

scholarly journals Webpage Recommendation System Based on the Social Media Semantic Details of the Website

2021 ◽  
Vol 12 (6) ◽  
pp. 237-243
Author(s):  
Dr. R.Rooba Et.al
Keyword(s):  
Recommendation System ◽  
Semantic Information ◽  
Markov Chain Model ◽  
Similarity Score ◽  
Web Pages ◽  
Chain Model ◽  
Web Page ◽  
The Social ◽  
Recommendation Accuracy ◽  
The Web

The web page recommendation is generated by using the navigational history from web server log files. Semantic Variable Length Markov Chain Model (SVLMC) is a web page recommendation system used to generate recommendation by combining a higher order Markov model with rich semantic data. The problem of state space complexity and time complexity in SVLMC was resolved by Semantic Variable Length confidence pruned Markov Chain Model (SVLCPMC) and Support vector machine based SVLCPMC (SSVLCPMC) meth-ods respectively. The recommendation accuracy was further improved by quickest change detection using Kullback-Leibler Divergence method. In this paper, socio semantic information is included with the similarity score which improves the recommendation accuracy. The social information from the social websites such as twitter is considered for web page recommendation. Initially number of web pages is collected and the similari-ty between web pages is computed by comparing their semantic information. The term frequency and inverse document frequency (tf-idf) is used to produce a composite weight, the most important terms in the web pages are extracted. Then the Pointwise Mutual Information (PMI) between the most important terms and the terms in the twitter dataset are calculated. The PMI metric measures the closeness between the twitter terms and the most important terms in the web pages. Then this measure is added with the similarity score matrix to provide the socio semantic search information for recommendation generation. The experimental results show that the pro-posed method has better performance in terms of prediction accuracy, precision, F1 measure, R measure and coverage.

scholarly journals Movie Recommendation System using Machine Learning

2021 ◽  
Vol 9 (VI) ◽  
pp. 4778-4781
Author(s):  
Ghanashyam Vibhandik
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Cosine Similarity ◽  
Daily Lives ◽  
Movie Content ◽  
Movie Recommendation ◽  
The Many ◽  
The Individual ◽  
Content Based Filtering ◽  
Filtering Approach

Movies are very significant in our lives. It is one of the many forms of entertainment that we encounter in our daily lives. It is up to the individual to decide whatever type of film they choose to see, whether it is a comedy, romantic film, action film, or adventure film. However, the issue is locating acceptable content, as there is a large amount of information created each year. As a result, finding our favourite film is really difficult. The goal of this research is to improve the regular filtering technique's performance and accuracy. A recommendation system can be implemented using a variety of approaches. Content-based filtering and collaborative filtering strategies are employed in this work. The content-based filtering approach analyses the user's history/past behaviour and recommends a list of comparable movies depending on their input. K-NN algorithms and collaborative filtering are also employed in this paper to improve the accuracy of the results. Cosine similarity is utilised in this work to quickly discover comparable information. The correctness of the cosine angle is measured by cosine similarity. People may quickly find their favourite movie content thanks to all of this.

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