Randomized algorithms
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Link prediction and path analysis using Markov chains
Proceedings of the 9th international World Wide Web conference on Computer networks : the international journal of computer and telecommunications netowrking
A general probabilistic framework for clustering individuals and objects
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Visualization of navigation patterns on a Web site using model-based clustering
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 11th international conference on World Wide Web
Using Markov models for web site link prediction
Proceedings of the thirteenth ACM conference on Hypertext and hypermedia
Web mining for web personalization
ACM Transactions on Internet Technology (TOIT)
Extrapolation methods for accelerating PageRank computations
WWW '03 Proceedings of the 12th international conference on World Wide Web
Probabilistic User Behavior Models
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
SEWeP: using site semantics and a taxonomy to enhance the Web personalization process
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
FS-Miner: efficient and incremental mining of frequent sequence patterns in web logs
Proceedings of the 6th annual ACM international workshop on Web information and data management
Mining history of changes to web access patterns
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Link prediction approach to collaborative filtering
Proceedings of the 5th ACM/IEEE-CS joint conference on Digital libraries
Web path recommendations based on page ranking and Markov models
Proceedings of the 7th annual ACM international workshop on Web information and data management
Ranking Pages by Topology and Popularity within Web Sites
World Wide Web
Structure and value synopses for XML data graphs
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
A web usage mining algorithm for web personalization
Intelligent Decision Technologies
MUADDIB: A distributed recommender system supporting device adaptivity
ACM Transactions on Information Systems (TOIS)
Dependable Recommendations in Social Internetworking
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
A web personalizing technique using adaptive data structures: The case of bursts in web visits
Journal of Systems and Software
| Hi-index | 0.00 |
The continuous growth in the size and use of the World Wide Web imposes new methods of design and development of online information services. The need for predicting the users' needs in order to improve the usability and user retention of a Web site is more than evident and can be addressed by personalizing it. Recommendation algorithms aim at proposing “next” pages to users based on their current visit and past users' navigational patterns. In the vast majority of related algorithms, however, only the usage data is used to produce recommendations, disregarding the structural properties of the Web graph. Thus important—in terms of PageRank authority score—pages may be underrated. In this work, we present UPR, a PageRank-style algorithm which combines usage data and link analysis techniques for assigning probabilities to Web pages based on their importance in the Web site's navigational graph. We propose the application of a localized version of UPR (l-UPR) to personalized navigational subgraphs for online Web page ranking and recommendation. Moreover, we propose a hybrid probabilistic predictive model based on Markov models and link analysis for assigning prior probabilities in a hybrid probabilistic model. We prove, through experimentation, that this approach results in more objective and representative predictions than the ones produced from the pure usage-based approaches.