Automatic text processing: the transformation, analysis, and retrieval of information by computer
Automatic text processing: the transformation, analysis, and retrieval of information by computer
A broader approach to personalization
Communications of the ACM
Automatic personalization based on Web usage mining
Communications of the ACM
Effective personalization based on association rule discovery from web usage data
Proceedings of the 3rd international workshop on Web information and data management
Personalized web search by mapping user queries to categories
Proceedings of the eleventh international conference on Information and knowledge management
Semantic Matching of Web Services Capabilities
ISWC '02 Proceedings of the First International Semantic Web Conference on The Semantic Web
An architecture to support scalable online personalization on the Web
The VLDB Journal — The International Journal on Very Large Data Bases
Creating Adaptive Web Sites Through Usage-Based Clustering of URLs
KDEX '99 Proceedings of the 1999 Workshop on Knowledge and Data Engineering Exchange
Discovering and ranking web services with BASIL: a personalized approach with biased focus
Proceedings of the 2nd international conference on Service oriented computing
The SMART Retrieval System—Experiments in Automatic Document Processing
The SMART Retrieval System—Experiments in Automatic Document Processing
Making mashups with marmite: towards end-user programming for the web
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
MashMaker: mashups for the masses
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
IT Professional
Similarity search for web services
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Proceedings of the 13th international conference on Intelligent user interfaces
NectaRSS, an intelligent RSS feed reader
Journal of Network and Computer Applications
MatchUp: Autocompletion for Mashups
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
ACM SIGMOD Record
Proceedings of the VLDB Endowment
Semantic-Based Mashup of Composite Applications
IEEE Transactions on Services Computing
Quality-based recommendations for mashup composition
ICWE'10 Proceedings of the 10th international conference on Current trends in web engineering
Composing enterprise mashup components and services using architecture integration patterns
Journal of Systems and Software
Semantics-based discovery, selection and mediation for presentation-oriented mashups
Proceedings of the 5th International Workshop on Web APIs and Service Mashups
Collaborative browsing system based on semantic mashup with open APIs
Expert Systems with Applications: An International Journal
An optimization strategy for mashups performance based on relational algebra
APWeb'12 Proceedings of the 14th Asia-Pacific international conference on Web Technologies and Applications
Assisting end-user development in browser-based mashup tools
Proceedings of the 34th International Conference on Software Engineering
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Mashups are key category of Web 2.0 personalized applications. Due to personalization property of Web 2.0 applications, number of mashups hosted by a mashup platform is increasing. End-users are overwhelmed by the increasing number of mashups. Therefore, they cannot easily find mashups of their interest. In this paper, we propose a novel mashup ranking technique based on the popular Vector Space Model (VSM) for mashups that use RSS feeds as data sources. Mashups that are ranked higher would be more interesting to end-users. In order to evaluate our mashup ranking technique, we implement it in a prototype where end-users select mashups that they consider interesting. We implicitly collect the end-user mashup selections and record the outcome of our ranking technique, and then we analyze them. Recorded R-Precision value in our technique is on an average 30% higher than R-Precision value in binary ranking technique which shows an improvement in capturing mashups that resemble end-user interest. In our design, we make sure our mashup ranking technique scales well to increasing number of mashups.