Segmentation of visiting patterns on web sites using a sequence alignment method

Hay, Birgit; Wets, Geert; Vanhoof, Koen

doi:10.1016/s0969-6989(03)00006-7

Cited by 29 publications

(7 citation statements)

References 13 publications

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“…It was proved that completing the paths is very important, however, it depends to a great degree on the correct identification of individual sessions of the portal visitors. There exist a large number of models for the identification of users sessions [15][16][17][18][19]. There exists also a method, which expressly identifies these sessions.…”

Section: Discussionmentioning

confidence: 99%

Data preprocessing evaluation for web log mining: reconstruction of activities of a web visitor

Munk

Kapusta

Švec

2010

Procedia Computer Science

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Presumptions of each data analysis are data themselves, regardless of the analysis focus (visit rate analysis, optimization of portal, personalization of portal, etc.). Results of selected analysis highly depend on the quality of analyzed data. In case of portal usage analysis, these data can be obtained by monitoring web server log file. We are able to create data matrices and web map based on these data which will serve for searching for behaviour patterns of users. Data preparation from the log file represents the most time-consuming phase of whole analysis. We realized an experiment so that we can find out to which criteria are necessary to realize this time-consuming data preparation. We aimed at specifying the inevitable steps that are required for obtaining valid data from the log file. Specially, we focused on the reconstruction of activities of the web visitor. This advanced technique of data preprocessing belongs to time consuming one. In the article we tried to assess the impact of reconstruction of activities of a web visitor on the quantity and quality of the extracted rules which represent the web users' behaviour patterns.

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Section: Discussionmentioning

confidence: 99%

Data preprocessing evaluation for web log mining: reconstruction of activities of a web visitor

Munk

Kapusta

Švec

2010

Procedia Computer Science

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“…The ultimate goal is to be able to successfully predict the next user action or information need based on the user's previous surfing behavior (Hay, Wets and Vanhoof, 2003).…”

Section: Implications For Researchmentioning

confidence: 99%

Informing destination recommender systems design and evaluation through quantitative research

Gretzel

Hwang

Fesenmaier

2012

International Journal of Culture, Tourism and Hospitality Research

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Informing destination recommender systems design and evaluation through quantitative research AbstractPurpose -Destination recommender systems need to become truly human-centric in their design and functionality. This requires a profound understanding of human interactions with technology as well as human behavior related to information search and decision-making in the context of travel and tourism. This paper seeks to review relevant theories that can support the development and evaluation of destination recommender systems and to discuss how quantitative research can inform such theory building and testing. Design/methodology/approach -Based on a review of information search and decision-making literatures, a framework for the development of destination recommender systems is proposed and the implications for the design and evaluation of human-centric recommender systems are discussed. Findings -A variety of factors that influence the information search and processing strategies that influence interactions with a destination recommender system are identified. This reveals a great need for data-driven models to inform recommender system processes. Originality/value -The proposed framework provides a basis for future research and development in the area of destination recommender systems. The paper concludes that the success of a specific destination recommender system will depend largely on its ability to anticipate and respond creatively to transformations in the personal and situational needs of its users. Such system intelligence needs to be based on empirical data analyzed with sophisticated quantitative methods. The importance of recommender systems in tourism marketing is also discussed. Approach: Based upon a review of information search and decision-making literatures, a framework for the development of destination recommender systems is proposed and the implications for the design and evaluation of human-centric recommender systems are discussed.Findings: A variety of factors that influence the information search and processing strategies that influence interactions with a destination recommender system are identified. This reveals a great need for data-driven models to inform recommender system processes.Originality/Value: The proposed framework provides a basis for future research and development in the area of destination recommender systems. The paper concludes that the success of a specific destination recommender system will depend largely on its ability to anticipate and creatively respond to transformations in the personal and situational needs of its users. Such system intelligence needs to be based on empirical data analyzed with sophisticated quantitative methods. The importance of recommender systems in tourism marketing is also discussed.

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“…A similar approach is the sequence alignment method, which quantifies the number and types of transformations required to transform one profile into another (e.g. [6]). Once we have calculated the pairwise similarities among all profiles, we can apply one of the numerous clustering approaches, e.g.…”

Section: Profile Similaritymentioning

confidence: 99%

Web Personalisation Through Incremental Individual Profiling and Support-based User Segmentation

Oh¹,

Isahara²

2007

IEEE/WIC/ACM International Conference on Web Intelligence (WI'07)

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Online personalised "my*" services are gaining popularity due to a growing customer need for information filtering and customisation. However, current systems mostly rely on some general usage and customer interaction in selecting components from prespecified blocks of content. The demand is great for high-quality unsupervised services on the customer side and for enabling techniques on the vendor side. Furthermore, individual profiles and, thus, personalised content should reflect changing individual behaviour. How do we efficiently build and maintain up-to-date personalised services for a large number of individuals? A compact and efficient, incrementally updatable representation of individual profiles is crucial. In addition, methods are required for efficient comparison of such profiles.Here we propose a methodology for building up-to-date personalised services. Individual profiles are represented as space-efficient prefix trees that are inherently easy to update incrementally. To measure the similarity of profiles, and also for the purpose of segmentation, we define a support-based metric that exploits the advantages of the tree-based structure. We evaluate our method on anonymised web data of 10,000 customers of an investment bank collected over 1.5 years.

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Segmentation of visiting patterns on web sites using a sequence alignment method

Cited by 29 publications

References 13 publications

Data preprocessing evaluation for web log mining: reconstruction of activities of a web visitor

Data preprocessing evaluation for web log mining: reconstruction of activities of a web visitor

Informing destination recommender systems design and evaluation through quantitative research

Web Personalisation Through Incremental Individual Profiling and Support-based User Segmentation

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