Web Page Prediction for Web Personalization: A Review

This paper proposes a survey of Web Page Ranking for web personalization. Web page prefetching has been widely used to reduce the access latency problem of the Internet. However, if most prefetched web pages are not visited by the users in their subsequent accesses, the limited network bandwidth and server resources will not be used efficiently and may worsen the access delay problem. Therefore, it is critical that we have an accurate prediction method during prefetching. The technique like Markov models have been widely used to represent and analyze user2018;s navigational behavior (usage data) in the Web graph, using the transitional probabilities between web pages, as recorded in the web logs. The recorded users2018; navigation is used to extract popular web paths and predict current users2018; next steps

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

Automated functional testing of online search services

Search services are the main interface through which people discover information on the Internet. A fundamental challenge in testing search services is the lack of oracles. The sheer volume of data on the Internet prohibits testers from verifying the results. Furthermore, it is difficult to objectively assess the ranking quality because different assessors can have very different opinions on the relevance of a Web page to a query. This paper presents a novel method for automatically testing search services without the need of a human oracle. The experimental findings reveal that some commonly used search engines, including Google, Yahoo!, and Live Search, are not as reliable as what most users would expect. For example, they may fail to find pages that exist in their own repositories, or rank pages in a way that is logically inconsistent. Suggestions are made for search service providers to improve their service quality. Copyright © 2010 John Wiley & Sons, Ltd. A novel method for automatically testing search services without the need of a human oracle is presented. The experimental findings reveal that some commonly used search engines, including Google, Yahoo!, and Live Search, are not as reliable as what most users would expect. For example, they may fail to find pages that exist in their own repositories, or rank pages in a way that is logically inconsistent. Suggestions are made for search service providers to improve their service quality. Copyright © 2010 John Wiley & Sons, Ltd.link_to_subscribed_fulltex

Semantic Selection of Internet Sources through SWRL Enabled OWL Ontologies

This research examines the problem of Information Overload (IO) and give an overview of various attempts to resolve it. Furthermore, argue that instead of fighting IO, it is advisable to start learning how to live with it. It is unlikely that in modern information age, where users are producer and consumer of information, the amount of data and information generated would decrease. Furthermore, when managing IO, users are confined to the algorithms and policies of commercial Search Engines and Recommender Systems (RSs), which create results that also add to IO. this research calls to initiate a change in thinking: this by giving greater power to users when addressing the relevance and accuracy of internet searches, which helps in IO. However powerful search engines are, they do not process enough semantics in the moment when search queries are formulated. This research proposes a semantic selection of internet sources, through SWRL enabled OWL ontologies. the research focuses on SWT and its Stack because they (a)secure the semantic interpretation of the environments where internet searches take place and (b) guarantee reasoning that results in the selection of suitable internet sources in a particular moment of internet searches. Therefore, it is important to model the behaviour of users through OWL concepts and reason upon them in order to address IO when searching the internet. Thus, user behaviour is itemized through user preferences, perceptions and expectations from internet searches. The proposed approach in this research is a Software Engineering (SE) solution which provides computations based on the semantics of the environment stored in the ontological model