Web usage mining for Web site evaluation
TL;DR: Making a site better fit its users by knowing about the needs of potential customers and the ability to establish personalized services that satisfy these needs is key to winning this competitive race.
Abstract: T he Web has become a borderless marketplace for purchasing and exchanging goods and services. While Web users search for, inspect and occasionally purchase products and services on the Web, companies compete bitterly for each potential customer. The key to winning this competitive race is knowledge about the needs of potential customers and the ability to establish personalized services that satisfy these needs. Making a site better fit its users.
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TL;DR: This work describes the full process for mining e-learning data step by step as well as how to apply the main data mining techniques used, such as statistics, visualization, classification, clustering and association rule mining of Moodle data.
Abstract: Educational data mining is an emerging discipline, concerned with developing methods for exploring the unique types of data that come from the educational context. This work is a survey of the specific application of data mining in learning management systems and a case study tutorial with the Moodle system. Our objective is to introduce it both theoretically and practically to all users interested in this new research area, and in particular to online instructors and e-learning administrators. We describe the full process for mining e-learning data step by step as well as how to apply the main data mining techniques used, such as statistics, visualization, classification, clustering and association rule mining of Moodle data. We have used free data mining tools so that any user can immediately begin to apply data mining without having to purchase a commercial tool or program a specific personalized tool.
1,049 citations
TL;DR: This article introduces the modules that comprise a Web personalization system, emphasizing the Web usage mining module, and presents a review of the most common methods that are used as well as technical issues that occur.
Abstract: Web personalization is the process of customizing a Web site to the needs of specific users, taking advantage of the knowledge acquired from the analysis of the user's navigational behavior (usage data) in correlation with other information collected in the Web context, namely, structure, content, and user profile data. Due to the explosive growth of the Web, the domain of Web personalization has gained great momentum both in the research and commercial areas. In this article we present a survey of the use of Web mining for Web personalization. More specifically, we introduce the modules that comprise a Web personalization system, emphasizing the Web usage mining module. A review of the most common methods that are used as well as technical issues that occur is given, along with a brief overview of the most popular tools and applications available from software vendors. Moreover, the most important research initiatives in the Web usage mining and personalization areas are presented.
941 citations
Cites background or methods from "Web usage mining for Web site evalu..."
...[Spiliopoulou and Faulstich 1998; Spiliopoulou et al. 1999; Spiliopoulou 2000] have designed MINT, another mining language for the implementation of WUM, a sequence mining system for the specification, discovery, and visualization of interesting navigation patterns....
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...Spiliopoulou et al. [Spiliopoulou and Faulstich 1998; Spiliopoulou et al. 1999; Spiliopoulou 2000] have designed MINT, another mining language for the implementation of WUM, a sequence mining system for the speci.cation, discovery, and visualization of interesting navigation patterns....
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...(Suggested) Buchner et al. [Buchner and Mulvenna 1998; Buchner et al. 1999] Server . . . WUM [Spiliopoulou and Faulstich 1998; Spiliopoulou et al. 1999; Spiliopoulou 2000] Server ....
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Posted Content•
TL;DR: An experiment in which self-reported privacy preferences of 171 participants were compared with their actual disclosing behavior during an online shopping episode, suggesting that current approaches to protect online users' privacy may face difficulties to do so effectively.
Abstract: interactive, privacy is a matter of increasing concern. Many surveys have investigated households' privacy attitudes and concerns, revealing a general desire among Internet users to protect their privacy. To complement these questionnaire-based studies, we conducted an experiment in which we compared selfreported privacy preferences of 171 participants with their actual disclosing behavior during an online shopping episode. Our results suggest that current approaches to protect online users' privacy, such as EU data protection regulation or P3P, may face difficulties to do so effectively. This is due to their underlying assumption that people are not only privacy conscious, but will also act accordingly. In our study, most individuals stated that privacy was important to them, with concern centering on the disclosure of different aspects of personal information. However, regardless of their specific privacy concerns, most participants did not live up to their self-reported privacy preferences. As participants were drawn into the sales dialogue with an anthropomorphic 3-D shopping bot, they answered a majority of questions, even if these were highly personal. Moreover, different privacy statements had no effect on the amount of information disclosed; in fact, the mentioning of EU regulation seemed to cause a feeling of 'false security'. The results suggest that people appreciate highly communicative EC environments and forget privacy concerns once they are `inside the Web'.
510 citations
14 Oct 2001
TL;DR: In this article, the authors conducted an experiment in which they compared self-reported privacy preferences of 171 participants with their actual disclosing behavior during an online shopping episode, and they found that regardless of their specific privacy concerns, most participants did not live up to their self reported privacy preferences.
Abstract: As electronic commerce environments become more and more interactive, privacy is a matter of increasing concern. Many surveys have investigated households' privacy attitudes and concerns, revealing a general desire among Internet users to protect their privacy. To complement these questionnaire-based studies, we conducted an experiment in which we compared self-reported privacy preferences of 171 participants with their actual disclosing behavior during an online shopping episode. Our results suggest that current approaches to protect online users' privacy, such as EU data protection regulation or P3P, may face difficulties to do so effectively. This is due to their underlying assumption that people are not only privacy conscious, but will also act accordingly. In our study, most individuals stated that privacy was important to them, with concern centering on the disclosure of different aspects of personal information. However, regardless of their specific privacy concerns, most participants did not live up to their self-reported privacy preferences. As participants were drawn into the sales dialogue with an anthropomorphic 3-D shopping bot, they answered a majority of questions, even if these were highly personal. Moreover, different privacy statements had no effect on the amount of information disclosed; in fact, the mentioning of EU regulation seemed to cause a feeling of 'false security'. The results suggest that people appreciate highly communicative EC environments and forget privacy concerns once they are 'inside the Web'.
396 citations
TL;DR: The empirically investigated the effect of user-based design and Web site usability on user satisfaction across four types of commercial Web sites, finding that trading sites are the lowest rated and online shopping and customer self-service sites should serve as models for Web site developers.
314 citations
References
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IBM1
TL;DR: Three algorithms are presented to solve the problem of mining sequential patterns over databases of customer transactions, and empirically evaluating their performance using synthetic data shows that two of them have comparable performance.
Abstract: We are given a large database of customer transactions, where each transaction consists of customer-id, transaction time, and the items bought in the transaction. We introduce the problem of mining sequential patterns over such databases. We present three algorithms to solve this problem, and empirically evaluate their performance using synthetic data. Two of the proposed algorithms, AprioriSome and AprioriAll, have comparable performance, albeit AprioriSome performs a little better when the minimum number of customers that must support a sequential pattern is low. Scale-up experiments show that both AprioriSome and AprioriAll scale linearly with the number of customer transactions. They also have excellent scale-up properties with respect to the number of transactions per customer and the number of items in a transaction. >
5,663 citations
"Web usage mining for Web site evalu..." refers background in this paper
...pioneering work of Agrawal and Srikant [1],...
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...The discovery of typical usage patterns seems to be exactly what sequence miners [1, 8] are built for....
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...In the pioneering work of Agrawal and Srikant [1], sequence mining is defined as follows: Given is a collection of transactions ordered in time, where each transaction contains a set of items....
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...Agrawal, R. and Srikant, R. Mining sequential patterns....
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...This is similar to the definition of a frequent sequence in [1, 8]....
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Book•
10 Jun 1997
TL;DR: One of the first practical guides to mining business data, Data Mining Techniques describes techniques for detecting customer behavior patterns useful in formulating marketing, sales, and customer support strategies.
Abstract: From the Publisher:
Data Mining Techniques thoroughly acquaints you with the new generation of data mining tools and techniques and shows you how to use them to make better business decisions. One of the first practical guides to mining business data, it describes techniques for detecting customer behavior patterns useful in formulating marketing, sales, and customer support strategies. While database analysts will find more than enough technical information to satisfy their curiosity, technically savvy business and marketing managers will find the coverage eminently accessible. Here's your chance to learn all about how leading companies across North America are using data mining to beat the competition; how each tool works, and how to pick the right one for the job; seven powerful techniques - cluster detection, memory-based reasoning, market basket analysis, genetic algorithms, link analysis, decision trees, and neural nets, and how to prepare data sources for data mining, and how to evaluate and use the results you get. Data Mining Techniques shows you how to quickly and easily tap the gold mine of business solutions lying dormant in your information systems.
1,823 citations
"Web usage mining for Web site evalu..." refers background in this paper
...Concept hierarchies are also useful in data mining, especially for market basket analysis [3]: The analyst groups individual products into more general concepts, with the effect of also grouping purchases of the products together....
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TL;DR: This paper presents several data preparation techniques in order to identify unique users and user sessions and Transactions identified by the proposed methods are used to discover association rules from real world data using the WEBMINER system.
Abstract: The World Wide Web (WWW) continues to grow at an astounding rate in both the sheer volume of traffic and the size and complexity of Web sites. The complexity of tasks such as Web site design, Web server design, and of simply navigating through a Web site have increased along with this growth. An important input to these design tasks is the analysis of how a Web site is being used. Usage analysis includes straightforward statistics, such as page access frequency, as well as more sophisticated forms of analysis, such as finding the common traversal paths through a Web site. Web Usage Mining is the application of data mining techniques to usage logs of large Web data repositories in order to produce results that can be used in the design tasks mentioned above. However, there are several preprocessing tasks that must be performed prior to applying data mining algorithms to the data collected from server logs. This paper presents several data preparation techniques in order to identify unique users and user sessions. Also, a method to divide user sessions into semantically meaningful transactions is defined and successfully tested against two other methods. Transactions identified by the proposed methods are used to discover association rules from real world data using the WEBMINER system [15].
1,616 citations
Journal Article•
493 citations
"Web usage mining for Web site evalu..." refers methods in this paper
...This methodology is pursued by Eighmey in his field study of multiple commercial Web sites [7]....
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Proceedings Article•
02 Aug 1996TL;DR: A general and flexible framework of specifying classes of generalized episodes that are recurrent combinations of events satisfying certain conditions is presented, which can be instantiated to a wide variety of applications by selecting suitable primitive conditions.
Abstract: Sequences of events are an important special form of data that arises in several contexts, including telecommunications, user interface studies, and epidemiology. We present a general and flexible framework of specifying classes of generalized episodes. These are recurrent combinations of events satisfying certain conditions. The framework can be instantiated to a wide variety of applications by selecting suitable primitive conditions. We present algorithms for discovering frequently occurring episodes and episode rules. The algorithms are based on the use of minimal occurrences of episodes; this makes it possible to evaluate confidences of a wide variety of rules using only a single analysis pass. We present empirical results on the behavior of the algorithms on events stemming from a WWW log.
391 citations
"Web usage mining for Web site evalu..." refers background in this paper
...The discovery of typical usage patterns seems to be exactly what sequence miners [1, 8] are built for....
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...This is similar to the definition of a frequent sequence in [1, 8]....
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