Personalizing Web Directories with the Aid of Web Usage Data

Pierrakos, Dimitrios; Paliouras, George

doi:10.1109/tkde.2009.173

Cited by 23 publications

(11 citation statements)

References 30 publications

(41 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Poongothai & Sathiyabama, 2012;Das & Turkoglu, 2009;Algur et al, 2010;Wang, Sanin & Szczerbicki, 2012), effectiveness (e.g. Wang, Lu & Zhang, 2007;Zhang & Xu, 2009;Somprasertsri & Lalitrojwong, 2010;Pierrakos & Paliouras, 2010), scalability (e.g. Yang, Liu & Feng, 2012;Li, 2009;Rettinger et al, 2012;Richardson & Domingos, 2002;Deshpande & Karypis, 2004), sparsity (e.g.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Webometrics benefitting from web mining? An investigation of methods and applications of two research fields

Lorentzen

2014

Scientometrics

View full text Add to dashboard Cite

Webometrics and web mining are two fields where research is focused on quantitative analyses of the web. This literature review outlines definitions of the fields, and then focuses on their methods and applications. It also discusses the potential of closer contact and collaboration between them. A key difference between the fields is that webometrics has focused on exploratory studies, whereas web mining has been dominated by studies focusing on development of methods and algorithms. Differences in type of data can also be seen, with webometrics more focused on analyses of the structure of the web and web mining more focused on web content and usage, even though both fields have been embracing the possibilities of user generated content. It is concluded that research problems where big data is needed can benefit from collaboration between webometricians, with their tradition of exploratory studies, and web miners, with their tradition of developing methods and algorithms.

show abstract

Section: Discussionmentioning

confidence: 99%

“…One exception to the single site norm was presented by Pierrakos and Paliouras (2010) who proposed a framework for the personalization of web directories that corresponded to user navigation throughout the web, not just one site. The usage data came from access log files of Internet service providers' cache proxy servers.…”

Section: Web Miningmentioning

confidence: 99%

Webometrics benefitting from web mining? An investigation of methods and applications of two research fields

Lorentzen

2014

Scientometrics

View full text Add to dashboard Cite

show abstract

“…This information can be used to enrich the user profiles, reduce the dimensionality of the data and even modify the manner in which the similarity between users and items is being estimated. Taxonomies can extend beyond individual Web sites to the whole Web (Pierrakos and Paliouras 2010), while ontologies can provide non-taxonomic relations that are particularly interesting for personalization (Tao et al 2007). …”

Section: Semantic Item Relationsmentioning

confidence: 99%

Discovery of Web user communities and their role in personalization

Παλιούρας

2012

User Model User-Adap Inter

View full text Add to dashboard Cite

One of the major innovations in personalization in the last 20 years was the injection of social knowledge into the model of the user. The user is not considered an isolated individual any more, but a member of one or more communities. User communities have been facilitated by the striking advancements of electronic communications and in particular the penetration of the Web into people's everyday routine. Communities arise in a number of different ways. Social networking tools typically allow users to proactively connect to each other. Alternatively, data mining tools discover communities of connected Web sites or communities of Web users. In this article, we focus on the latter type of community, which is commonly mined from logs of users' activity on the Web. We recall how this process has been used to model the users' interests and personalize Web applications. Collaborative filtering and recommendation are the most widely used forms of community-driven personalization. However, we examine a range of other interesting alternatives that are worth investigating further. This effort leads us naturally to the recent developments on the Web and particularly the advent of the social Web. We explain how this development draws together the different viewpoints on Web communities and introduces new opportunities for community-based personalization. In particular, we propose the concept of active user community and show how this relates to recent efforts on mining social networks and social media.

show abstract

“…Identifying Interest Clusters: User browsing behaviour has been used as a source for identifying clusters of users with similar interests (Pierrakos and Paliouras, 2010). Such clusters play the role of community models, and can be used for personalisation by narrowing down the search space and choosing the category most related to the user's interests.…”

Section: Relevant Techniques For Virtual Communitiesmentioning

confidence: 99%

Adaptive notifications to support knowledge sharing in close-knit virtual communities

Kleanthous

Dimitrova

2012

User Model User-Adap Inter

View full text Add to dashboard Cite

Social web-groups where people with common interests and goals communicate, share resources, and construct knowledge, are becoming a major part of today's organisational practice. Research has shown that appropriate support for effective knowledge sharing tailored to the needs of the community is paramount. This brings a new challenge to user modelling and adaptation, which requires new techniques for gaining sufficient understanding of a virtual community and identifying areas where the community may need support. The research presented here addresses this challenge presenting a novel computational approach for community-tailored support underpinned by organisational psychology and aimed at facilitating the functioning of the community as a whole (i.e. as an entity). A framework describing how key community processes -transactive memory, shared mental models, and cognitive centrality -can be utilised to derive knowledge sharing patterns from community log data is described. The framework includes two parts: (i) extraction of a community model that represents the community based on the key processes identified and (ii) identification of knowledge sharing behaviour patterns that are used to generate adaptive notifications. Although the notifications target individual members, they aim to influence individuals' behaviour in a way that can benefit the functioning of the community as a whole. A validation study has been performed to examine the effect of community-adapted notifications on individual members and on the community as a whole using a close-knit community of researchers sharing references. The study shows that notification messages can improve members' awareness and perception of how they relate to other members in the community. Interesting observations have been made about the linking between the physical and the virtual community, and how this may influence members' awareness and knowledge sharing behaviour. Broader implications for using log data to derive community models based on key community processes and generating community-adapted notifications are discussed.

show abstract

Personalizing Web Directories with the Aid of Web Usage Data

Cited by 23 publications

References 30 publications

Webometrics benefitting from web mining? An investigation of methods and applications of two research fields

Webometrics benefitting from web mining? An investigation of methods and applications of two research fields

Discovery of Web user communities and their role in personalization

Adaptive notifications to support knowledge sharing in close-knit virtual communities

Contact Info

Product

Resources

About