Advanced Techniques in Web Intelligence-2: Web User Browsing Behaviour and Preference Analysis


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It is obvious that the explosive increase of data had an impact on the precision of many modeling techniques. Hence, this proposal seems too optimistic to be true, and there are fundamental reasons for discarding this extremely naive proposal. First, automatic machine learning methods improve the accuracy of predictions by training better the models on the available data. The problem is that future sce- narios need to belong to the same kind of data.

For instance, if we try to model the trajectory of the Apollo mission by a machine learning approach, perhaps the model will perform well. But if a new factor like cosmic dust enters into the scenario, such a machine learning model will fail.

Second, the large quantities of data presented here do not com- pare with the large quantities of data that could be extracted from other examples in nature. Of course, fluid theory can model the average motion of particles in a fluid, and this is the best-known way to handle this problem. Third, intractable computational complexity is easy to find in a simple human activity system, like the traveling salesman problem, which be- longs to the class of NP-Hard problems.

Nevertheless, there are several heuristics based on machine learning methods, but they show average performance in some cases and worse performance in others. The Internet has become a regular channel for communication, most of all for business transactions. Commerce over the Internet has grown to higher levels in recent years.

Traditional market surveys serve no purpose in reflecting the actual requirements of customers who have not been precisely defined in the web context. It is well known that web users are ubiquitous. In that sense, a marketing survey compiled in a specific location in the world does not carry clear statistical significance in another. However, online queries should improve this issue by re- questing that each visitor answers as many focused questions as they can [7], but apart from predicting future customer preferences, online surveys can improve the effectiveness of the web site content strategy.

In this sense, web usage patterns can be used for analyzing web user preferences.

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Traditional data mining methods need to be pre-processed and adapted before being employed on web data. Several efforts have been made to improve the quality of the results. Once a repository of web user behaviour Web Warehouse is available [26], specific machine learning algorithms can be applied in order to extract patterns regarding the usage of the web site.

As a result of this process, sev- eral applications can be implemented on adaptive web sites, such as recommender systems and revenue management marketing, among others. New models based on the neurophysiology theory of decision making have been applied to the link selec- tion process. These models have two stages: training and simulation. In the second one, the configured agents are simulated within a web structure for recovering the expected behaviour.

The main difference with the machine learning approach consists in the model being independent of the structure and content of the web site. Furthermore, agents can be confronted with any page and decide which link to follow or leave the web site.


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This important characteristic makes these kind of models appropriate for heavily dynamic web sites. Another important difference is that these models have a strong theoretical basis built upon a physical phenomenon, whose modeling equa- tions came from a phenomenon observation.

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Traditional physics models approaches are more generic, but, for web user behaviour analysis, the proposal is based on a specific state-of-the-art theory of brain decision making. New web user behavior models are related to the brain neural activity levels NAL of certain brain regions with a discrete set of possible choices. It describes the neural activity of different brain regions during the subject resolution of the decision, by means of a stochastic process that evolves until an activity reaches a given thresh- old that fires the decision.

Such a class of stochastic processes applied to decision making has been experimentally studied for nearly forty years [9, 15, 19, 12]. Then the web user faces a set of discrete decisions that corresponds to the selection of a hyperlink or leaving the site as in figure 1. More- over, the performance of the artificial agents is reported to have similar statistical behaviour to humans.

If the web site semantic does not change, the set of visitors remains the same. This principle enables predicting the changes in the access pattern to web pages related to small changes in the web site that preserve the semantic. Other studies on ant colony models [1] relate directly to general purpose clustering techniques.

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Traditionally, the next step consists of applying data mining techniques for identifying and extracting patterns of web user browsing be- haviour. Furthermore, data quality needs to be ensured, since the calibration of web user models is sensitive to the data set. The variety of different sources carries a number of complexities in terms of data pre-processing, for example related to the incompleteness of each source.

As a solu- tion to this, several pre-processing algorithms have been developed [26]. Furthermore, in terms of the web log entries, several problems must be confronted. Instead, it registers every retrieval action, but without a unique identification for each user. The web user browsing behaviour can be described by three kinds of data: the web site structure, the web page content and the web user session.

The first is di- rectly related to the environment. The third describes the click stream that each Web user performs during his visit to the web site.

Advanced Techniques in Web Intelligence-2 : Web User Browsing Behaviour and Preference Analysis

Nevertheless, the analysis of web usage is ob- fuscated by the dynamic characteristic of the content and structure of the web sites. A web recommender system is a typical web intelligence application [24]. Recommendation is based on a web user profile recognition procedure, which filters information for content on a web page.

Such a system is based on detected common patterns in the web user browsing behaviour. In a broad perspective, this book includes topics such as traditional and new web data pre-processing techniques, approaches for modelling the web user inspired from the neuro-science area, web usage mining algorithms and techniques, extraction of relevant user opinions from web 2. Fanelli and Maria A. Torsello, introduces a novel WUM approach based on the use of fuzzy clustering to the discovery of user categories, starting from usage patterns.

References 1. Abraham, A. In: Procs. Encourage customers to share their thoughts and opinions by including a customer satisfaction survey into your email drip. SurveyGizmo suggests these three key principles to follow when designing a survey:. Keep these things in mind as you personalize your customer survey with questions pertaining to your brand and product.

Many marketers make the mistake of using generic demographics like age, profession, and location to develop their buyer personas. One way to dig deeper into customer preferences is to use the Acquisitions tab on Google Analytics to see which social media outlets, industry blogs and professional forums your site traffic comes from. Then, apply this information to your personas so you can find out where and when to reach them more effectively. Additionally, acquiring keyword data is a helpful way to discover the terms and descriptions that certain buyer personas use to describe your services.

To segment customers based on keyword searches, for example, use Google Webmaster Tools to create a list of common keywords that drive people to your site. Then, group the keywords into overarching themes and assign to different personas based on the data you have available. To put this language into action, incorporate these keywords across your website copy, content marketing efforts, and other online interactions.

Speaking the same language as your customers is a subtle way to make your current audience feel more welcomed. From clicking on a link to reading through a web page, every customer action offers valuable insight into customer behavior. To determine how customers interact with your website, you can try a user behavior tracking tool. Tools like Google Analytics and Inspectlet are great tools for gathering insights such as time on page and bounce rate.

Inspectlet can even provide short videos of users on your page in real time. If people had trouble navigating to a certain sales page, for example, adjust the interface to allow for a more user-friendly experience. Creating a plan for future customer engagement is just as important as creating a plan for the present. This puts customer experience teams in the right frame of mind to respond to customers during stressful or challenging situations.

Predictive modeling software mines existing customer data to identify cyclical patterns and trends that can inform decision making. To see how predictive modeling informs customer strategy, imagine you work for a SaaS company that wants to adjust its product roadmap to anticipate customer needs. Understanding your most popular and most visited pages can also inform your content strategy, focusing on topics and formats that will best solve your audiences challenges.

Draw trends across the most commonly-used features to determine why your customers liked them.

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Additionally, looking at market trends and analysis will give you a good idea of what other companies in your space have already accomplished, so you can devise new features that explore these areas. And the same is possible for other variables. Create target groups based on click-, buying- or browsing behaviour.

Advanced Techniques in Web Intelligence-2: Web User Browsing Behaviour and Preference Analysis Advanced Techniques in Web Intelligence-2: Web User Browsing Behaviour and Preference Analysis
Advanced Techniques in Web Intelligence-2: Web User Browsing Behaviour and Preference Analysis Advanced Techniques in Web Intelligence-2: Web User Browsing Behaviour and Preference Analysis
Advanced Techniques in Web Intelligence-2: Web User Browsing Behaviour and Preference Analysis Advanced Techniques in Web Intelligence-2: Web User Browsing Behaviour and Preference Analysis
Advanced Techniques in Web Intelligence-2: Web User Browsing Behaviour and Preference Analysis Advanced Techniques in Web Intelligence-2: Web User Browsing Behaviour and Preference Analysis
Advanced Techniques in Web Intelligence-2: Web User Browsing Behaviour and Preference Analysis Advanced Techniques in Web Intelligence-2: Web User Browsing Behaviour and Preference Analysis
Advanced Techniques in Web Intelligence-2: Web User Browsing Behaviour and Preference Analysis Advanced Techniques in Web Intelligence-2: Web User Browsing Behaviour and Preference Analysis
Advanced Techniques in Web Intelligence-2: Web User Browsing Behaviour and Preference Analysis Advanced Techniques in Web Intelligence-2: Web User Browsing Behaviour and Preference Analysis
Advanced Techniques in Web Intelligence-2: Web User Browsing Behaviour and Preference Analysis Advanced Techniques in Web Intelligence-2: Web User Browsing Behaviour and Preference Analysis

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