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Data Preparation for Mining World Wide Web Browsing Patterns
TLDR
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. Expand
Web mining: information and pattern discovery on the World Wide Web
TLDR
This paper defines Web mining and presents an overview of the various research issues, techniques, and development efforts, and briefly describes WEBMINER, a system for Web usage mining, and concludes the paper by listing research issues. Expand
Automatic personalization based on Web usage mining
TLDR
The ability to track users’ browsing behavior down to individual mouse clicks has brought the vendor and end customer closer than ever before, and it is now possible for a vendor to personalize his product message for individual customers at a massive scale, a phenomenon that is being referred to as mass customization. Expand
Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness
TLDR
This study shows that both user-based and item-based algorithms are highly vulnerable to specific attack models, but that hybrid algorithms may provide a higher degree of robustness. Expand
Effective personalization based on association rule discovery from web usage data
TLDR
This paper proposes effective and scalable techniques for Web personalization based on association rule discovery from usage data that can achieve better recommendation effectiveness, while maintaining a computational advantage over direct approaches to collaborative filtering such as the k-nearest-neighbor strategy. Expand
Discovery and Evaluation of Aggregate Usage Profiles for Web Personalization
TLDR
The results indicate that using the generated aggregate profiles, the technique can achieve effective personalization at early stages of users' visits to a site, based only on anonymous clickstream data and without the benefit of explicit input by these users or deeper knowledge about them. Expand
Web search personalization with ontological user profiles
TLDR
This work presents an approach to personalized search that involves building models of user context as ontological profiles by assigning implicitly derived interest scores to existing concepts in a domain ontology. Expand
Personalized recommendation in social tagging systems using hierarchical clustering
TLDR
This work presents a personalization algorithm for recommendation in folksonomies which relies on hierarchical tag clusters and presents extensive experimental results on two real world dataset, suggesting that guysonomies encompassing only one topic domain, rather than many topics, present an easier target for recommendation. Expand
Data Mining for Web Personalization
TLDR
An overview of Web personalization process viewed as an application of data mining requiring support for all the phases of a typical data mining cycle, including data collection and pre-processing, pattern discovery and evaluation, and finally applying the discovered knowledge in real-time to mediate between the user and the Web. Expand
Context-aware music recommendation based on latenttopic sequential patterns
TLDR
This paper presents a context-aware music recommender system which infers contextual information based on the most recent sequence of songs liked by the user, and uses topic modeling to determine a set of latent topics for each song, representing different contexts. Expand
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