Surfing Behavior Research Articles

A Time Efficient Algorithm for Web Log Analysis

The world of web is very diverse and contains huge amount of information. To obtain relevant information from the www is a challenging task today. To ensure the retrieval of pertinent information, suitable web mining techniques can be applied. From the perspective of a web designer it is important to know about the web surfing behavior of a user which can be accomplished by using the web log that captures the history of user activities. There are a number of algorithms which can be used for analyzing web log, however choosing the appropriate algorithm is important to retrieve the required data in less amount of time. This paper proposes a new time efficient algorithm which is work for web log analysis and is very time efficient to the other web log algorithms.

Reference Scan Algorithm for Path Traversal Patterns

The paper focuses the path of exploring an algorithm which involves mining frequently used user traversals pattern while using the World Wide Web by the user. As the web’s becoming more and more popular, web servers gather the large amount of data from the web in the form of web server logs. The web server log provides important information aiding to analysing the user surfing behavior and thus extract important information. Web Usage Mining analyses the Web access logs to find how the users surf a website so as to bring out interesting patterns which can be used for improving user profile, web site design, business and marketing decision support and web server system. Firstly, the server log is scanned and the maximal forward references are obtained for which effect of backward references is not considered and secondly, the maximal forward references obtained in the first step are used to finally obtain the large references which are frequently accessed patterns by the user.

Do Links Matter? An Investigation of the Impact of Consumer Feedback, Recommendation Networks, and Price Bundling on Sales

Link structures among items within an E-commerce Web site can be regarded as a potential recommendation that helps new consumers quickly locate relevant products. In this paper, combining a modified version of Google's PageRank method with economic analysis of word of mouth, we investigate whether a product's position within a network composed of recommendation links is incrementally informative about its future sales. Based on data from Amazon.com, we document that with consumer word of mouth and other product characteristics controlled, the position of a product within a recommendation network does influence consumers' purchase decisions, and models incorporating link structure have a higher incremental predictive power of future sales than models without. In addition, as time elapses, the relative weights consumers placed on recommendations with price discount and those without are different. Last, we develop a learning mechanism through which we find the optimal damping value of the PageRank IR model in the Amazon context. Our results show that compared to general Internet surfing behavior, consumer consumption on Amazon is less random. We conclude that even though the product position within a recommendation network does influence customers' purchase behaviors, product sales are still mainly driven by their own product characteristics.

Continuous time Markov chain based website navigability measure

Website navigability is acquiring a growing importance in website design and redesign, quality evaluation, and improvement. Existing navigability measures mainly depend on site link structure, so that they only consider the impact of site link structure for navigability and ignore the impact of Web page content. A continuous Markov chain model which depicts the user's surfing behavior can balance these two factors in the evaluation of website navigability, and it needs to estimate the page transition probabilities and user stay time according to user access log. In this way, we can obtain more reliable results for website navigability measure than the existed methods. Experiments show that our method is effective.

Issues of Learning the Browsing Language

The web is pervading all walks of life and its huge increase in information volume has made the web personalization mandatory. Web Personalization may be achieved by web mining especially the web usage mining technique on the surfing behavior. Learning the surfing behavioral pattern has emerged into a promising research area to achieve web personalization. Till recently web usage mining was done on server logs on website visits and was found insufficient. The goal can be further alleviated if the task or mood of surfer is also learnt. Very shortly, a new approach of web usage mining of client or browser logs on website visits to understand the task or mood of surfer have come into view. It will make the prediction of next web pages by a surfer more accurate. Exploration of browsing behavior at browser to understand an intended task or mood of surfer is hereby termed as Browsing Language as epitome of body language of any person. This work discusses the issues of web usage mining the client side behavior logs for single website only. Implementation of such a capability into a browser will develop the literature of browsing language and may also reduce the overhead of server in realizing web personalization.

English

We all regularly use the internet for a variety of reasons. We do like some sites and dislike others. There can be various reasons for liking and disliking sites. Some sites interest us, some sites are visited by us often, some are visited periodically, some simple annoy. Our Internet usage is also pretty much the same everyday barring weekends. A average user logs on to the internet at nearly same times everyday, surfs some sites regularly some new sites at times and perform repeated action on sites, more or less. The browser is used as an intermediatery for developing a system which identifies these usage patterns, learns them and then uses it to enhance and personalize our surfing behavior. The system is smart enough to prefetch the right pages at the right time and display them in the browser for the user, all without any manual intervention.

Ontological analysis of web surf history to maximize the click-through probability of web advertisements

Due to an enormous influx of capital over the past decade, the online advertising industry has become extremely robust and competitive. The difference between success and failure in such a competitive market often rests in the ability to deliver advertisements that are closely in line with a user's interests. In this work, we propose and test a new online advertisement targeting technique which adapts and utilizes several powerful and well tested information retrieval and lexical techniques to develop an estimate of a user's affinity for particular products and services based on an analysis of a user's web surfing behavior. This new online ad targeting technique performs extremely well in our empirical tests.

MNav: A Markov Model-Based Web Site Navigability Measure

Web site success is significantly associated with navigability, an important attribute of usability that denotes the ease with which users find desired information as they move through a Web site. Navigable Web sites allow users to form a mental model of the type and location of information in the Web site and an expectation of where and to what a particular hyperlink will lead. Existing navigability measures are based mainly on the static hyperlink structure of a Web site. Such measures, however, have two main drawbacks: 1) the effect on navigability of a hyperlink structure cannot be well characterized and 2) the effect on navigability of the navigation aids (such as the Back button provided by a browser) is ignored. In this paper, we abstract a dynamic Web surfing behavior as a Markov model which synthesizes typical surfing actions. Based on this model, we propose a novel navigability measure MNav. The experimental results show that MNav can be efficiently computed and it provides an effective and useful measurement of Web site navigability.

Deleterious Mutations Can Surf to High Densities on the Wave Front of an Expanding Population

There is an increasing recognition that evolutionary processes play a key role in determining the dynamics of range expansion. Recent work demonstrates that neutral mutations arising near the edge of a range expansion sometimes surf on the expanding front leading them rather than that leads to reach much greater spatial distribution and frequency than expected in stationary populations. Here, we extend this work and examine the surfing behavior of nonneutral mutations. Using an individual-based coupled-map lattice model, we confirm that, regardless of its fitness effects, the probability of survival of a new mutation depends strongly upon where it arises in relation to the expanding wave front. We demonstrate that the surfing effect can lead to deleterious mutations reaching high densities at an expanding front, even when they have substantial negative effects on fitness. Additionally, we highlight that this surfing phenomenon can occur for mutations that impact reproductive rate (i.e., number of offspring produced) as well as mutations that modify juvenile competitive ability. We suggest that these effects are likely to have important consequences for rates of spread and the evolution of spatially expanding populations.

Effects of Perceived Interactivity on Web Site Preference and Memory: Role of Personal Motivation

The main purpose of this study is to explore theoretically and empirically the effects of consumers' different surfing behaviors in terms of advertising effectiveness in the new media context. This study attempts to answer two primary questions: (1) What effect does interactivity have on attitude and memory? (2) What is the role of individual motivation on clicking behavior on the Web site? In this study, perceived interactivity was also found to influence consumers' attitudes toward the ad and memory for its contents. This finding is consistent with literature about the effects of interaction on attitude and memory. Results of this study showed a positive impact of perceived interactivity on both attitude and memory.

A Bayesian Hierarchical Model for the Evaluation of a Website

Consider a website and the surfers visiting its pages. A typical issue of interest, forexample while monitoring an advertising campaign, concerns whether a specific pagehas been designed successfully, i.e. is able to attract surfers or address them to otherpages within the site. We assume that the surfing behaviour is fully described by thetransition probabilities from one page to another, so that a clickstream (sequence ofconsecutively visited pages) can be viewed as a finite-state-space Markov chain. Wethen implement a variety of hierarchical prior distributions on the multivariate logitsof the transition probabilities and define, for each page, a content effect and a linkeffect. The former measures the attractiveness of the page due to its contents, whilethe latter signals its ability to suggest further interesting links within the site.Moreover, we define an additional effect, representing overall page success, whichincorporates both effects previously described. Using WinBUGS, we provide estimatesand credible intervals for each of the above effects and rank pages accordingly.

Online Analytical Mining of Path Traversal Patterns for Web Measurement

The WWW and its associated distributed information services provide rich world-wide online information services, where objects are linked together to facilitate interactive access. Users seeking information of Internet traverse from one object via links to another. It is important to analyze user access patterns which will help improve Web page design by providing an efficient access between highly correlated objects, and also assist in better marketing decisions by placing advertisements in frequently visited document. We need to study the user surfing behavior by examining the Web access log, browsing frequency of Web pages and computing the average duration time of visit. This paper offers an architecture to store the derived web user access paths in a data warehouse, and facilitates its view maintainability by use of a metadata. The system will update the user access paths pattern with the data warehouse by the data operation functions in the metadata. Whenever a new user access path occurs, the view maintainability is triggered by a constraint class in the metadata. The data warehouse can be analyzed on the frequent pattern tree of user access paths on the Website within a period and duration. The result is an online analytical mining path traversal pattern. Our experimental and performance studies have demonstrated the effectiveness and efficiency of our system with the following contributions: an architecture of online analytical mining (OLAM) using frame model metadata, a methodology (stepwise procedure) of implementing OLAM and the resultant cluster of web pages frequently visited by users for marketing use.

Profiling Web Usage in the Workplace

Employees' nonwork-related Web surfing behavior results in millions of dollars of expenditure for organizations. This paper proposes the use of a behavior-based artificial intelligence system to pr...

Customer profiling in e-commerce: Methodological aspects and challenges

Online profiling is the collection of information about Internet surfing behaviour across many different websites for the purpose of formulating a profile of users' habits and interests. While marketers have long profiled large consumer groups on the basis of demographics, online profiling allows companies to collect information from individuals across a wide range of traits. A company's ability to build and strengthen long-term customer relationships via individualised e-commerce offers will depend on its ability to use customer data to plan, develop and control interactions with its customers. Despite the importance of customer profiles in e-commerce, the methods for producing them have rarely been investigated in marketing research. This paper aims to close this gap by providing an overview of key areas and issues of customer profiling in e-commerce. More recently, this controversial practice has seized the public's attention because ethics and privacy are concerned.

Predicting Selection and Activity in Television Genre Viewing

"Active" television viewing has meant (among other things) selective exposure to types of content, attention to that content, and several different kinds of other activities during viewing itself. This study argues that such meanings are differently predicted by three types of predictors (individuals' gratifications sought from different television genres, their expertise with these genres, and their need for cognition), and also vary by genre. Two different instrumental reasons for viewing (mood and content preference) both predicted selective viewing and thinking while viewing, but only content preference predicted attentive viewing. Casual reasons for viewing were related to less viewing and more channel surfing behavior. Need for cognition was unrelated to variation in genre viewing, but it was related in differing but sensible ways to attention to different genres. These results support the utility of genre in differentiating processes in television viewing and further argue for making a number of distinctions in research: between genres, between gratifications, between gratifications and other predictors, and between selective viewing and during-viewing activities.

Distributions of surfers’ paths through the World Wide Web: Empirical characterizations

Surfing the World Wide Web (WWW) involves traversing hyperlink connections among documents. The ability to predict surfing patterns could solve many problems facing producers and consumers of WWW content. We analyzed WWW server logs for a WWW site, collected over ten days, to compare different path reconstruction methods and to investigate how past surfing behavior predicts future surfing choices. Since log files do not explicitly contain user paths, various methods have evolved to reconstruct user paths. Session times, number of clicks per visit, and Levenshtein Distance analyses were performed to show the impact of various reconstruction methods. Different methods for measuring surfing patterns were also compared. Markov model approximations were used to model the probability of users choosing links conditional on past surfing paths. Information-theoretic (entropy) measurements suggest that information is gained by using longer paths to estimate the conditional probability of link choice given surf path. The improvements diminish, however, as one increases the length of path beyond one. Information-theoretic (total divergence to the average entropy) measurements suggest that the conditional probabilities of link choice given surf path are more stable over time for shorter paths than longer paths. Direct examination of the accuracy of the conditional probability models in predicting test data also suggests that shorter paths yield more stable models and can be estimated reliably with less data than longer paths.

SpeedTracer: A Web usage mining and analysis tool

SpeedTracer, a World Wide Web usage mining and analysis tool, was developed to understand user surfing behavior by exploring the Web server log files with data mining techniques. As the popularity of the Web has exploded, there is a strong desire to understand user surfing behavior. However, it is difficult to perform user-oriented data mining and analysis directly on the server log files because they tend to be ambiguous and incomplete. With innovative algorithms, SpeedTracer first identifies user sessions by reconstructing user traversal paths. It does not require “cookies” or user registration for session identification. User privacy is protected. Once user sessions are identified, data mining algorithms are then applied to discover the most common traversal paths and groups of pages frequently visited together. Important user browsing patterns are manifested through the frequent traversal paths and page groups, helping the understanding of user surfing behavior. Three types of reports are prepared: user-based reports, path-based reports and group-based reports. In this paper, we describe the design of SpeedTracer and demonstrate some of its features with a few sample reports.