Similarity Based Ranking of Query Results from Real Web Databases

The information available in the World Wide Web is stored using many real Web databases (e.g. vehicle database). Accessing the information from these real Web databases has become increasingly important for the users to find the desired information. Web users search for the desired information by querying these Web databases, when the number of query results generated is large, it is very difficult for the Web user to select the most relevant information from the large result set generated. Users today, have become more and more demanding in terms of the quality of information that is provided to them while searching the Web databases. The most common solution to solve the problem involves ranking the query results returned by the Web databases. Earlier approaches have used query logs, user profiles and frequencies of database values. The problem in all of these techniques is that ranking is performed in a user and query independent manner. This paper, proposes an automated ranking of query results returned by Web databases by analyzing user, query and workload similarity. The effectiveness of this approach is discussed considering a vehicle Web database as an example

Content Based Web Page Re-Ranking Using Relevancy Algorithm

The World Wide Web is a system of interlinked hypertext documents that are accessed via the internet. It plays a leading role for retrieving user requested information from the web resources. In order to retrieve user requested information, search engine plays a major role for crawling web content on different node and organizing them into result pages so that user can easily select the required information by navigating through the result pages link. This strategy worked well in earlier because, number of resources available for user request is limited. It is feasible to identify the relevant information directly by the user from the search engine results. As the Internet era increases, sharing of resource also increases and this leads to develop an automated technique to rank each web content resource. Different search engine uses different techniques to rank search results for the user query. This leads to business motivation of bringing up their web resource into top ranking position. As the competition and web resource increases, the ranking of web content becomes tedious and dynamic with respect to the user query. In the proposed work a new approach is introduced to rank the relevant pages based on the content and keywords rather than keyword and page ranking provided by search engines. Based on the user query, search engine results are retrieved. Every result is individually analyzed based on keywords and content. User Query is pre-processed to identify the root words. Root word is considered for Dictionary construction and Dictionary is built with synonyms for the user query. Keywords and content words of each resultant web page is preprocessed and compared against the dictionary. If a match is found, then particular weight is awarded for each word. Finally, the total relevancy of the particular link against user request is computed by summarizing all the weights of the keyword and content words. The results are then re-ranked in descending order of their weights and displayed

Personalized Recommendation of Web Pages Using Group Average Agglomerative Hierarchical Clustering (GAAHC)

Entrepreneurs are investing heavily on marketing and promoting business through the websites to enhance their online reputation and draw the attention of the web users. Website structure plays the vital role in attracting the web users. Creating personalized website structure for individual user by restructuring the web site structure is a tedious and endless job. If the users do not find the required information easily in the websites, then users abandon such websites. Hence, personalized recommendation of web pages to the web users increases the user’s interest and the time they spend in the website. Personalization is the process of creating customized participation of users to a website, rather than providing a broad participation. Personalization allows the website to present the users with the unique participation bespoke to their demands and passion. Personalized recommendation is a challenging task, which has drawn the focus of many researchers. Personalization has to trace the behavior of individual users. Usage behavior can be traced by observing the individual navigation patterns using web log file of the specific website. This method requires session identification, clustering sessions into similar clusters and building a model for personalized recommendations using access time length and frequency of access. Most of the existing works on this topic have used K-Means clustering with Euclidean distance. K-Means suffers from choosing the initial random center and sequence of page visits is not considered. The proposed research work uses Group Average Agglomerative Hierarchical Clustering (GAAHC), with Modified Levenshtein

Web page access prediction using hierarchical clustering based on modified levenshtein distance and higher order Markov model

Web Page access prediction is a challenging task in the current scenario, which draws the attention of many researchers. Predictions need to keep track of history data to analyze the usage behavior of the users. Web Usage behavior of a user can be analyzed using the web log file of a specific website. User behavior can be analyzed by observing the navigation patterns. This approach requires user session identification, clustering the sessions into similar clusters and developing a model for prediction using the current and earlier accesses. Most of the previous works in this field have used K-Means clustering technique with Euclidean distance for computation. The drawbacks of K-Means is that deciding on the number of clusters, choosing the initial random center are difficult and the order of page visits are not considered. The proposed research work uses hierarchical clustering technique with modified Levenshtein distance, Page Rank using access time length, frequency and higher order Markov model for prediction. Experimental results prove that the proposed approach for prediction gives better accuracy over the existing techniques

Spectral clustering algorithm based web mining and quadratic support vector machine for learning style prediction in E-learning platform

A learning system, which is composed of a computer and the internet as the major elements, is termed an e-learning platform. It also promotes the education standard with the utilization of modern technology and equipment. Meanwhile, to enhance the standard of education significantly, it is important to predict the learning style of the users with the assistants of feedback and supervision. Nevertheless, it will avert the inherent correlation among e-learning behaviors. Hence, to predict the learning style automatically we propose a novel Spectral Clustering algorithm based Quadratic Support Vector Machine (E-SVM) approach. Our proposed approach employs two phases: (i) Utilizing the Web usage mining approach the learning secrets are extracted from the log files of learners. (ii) The classification of learning styles of learners is effectuated with the proposed approach. Experiments are demonstrated with Python package and analyzed the performance. For simulation, we have utilized real-time dataset and compared the results with the state-of-art approaches. Our approach surpasses all the other approaches