Applied multivariate techniques
Applied multivariate techniques
Methods and metrics for cold-start recommendations
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Implicit feedback for inferring user preference: a bibliography
ACM SIGIR Forum
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Web search engine based on DNS
Journal of Network and Computer Applications
A time-based approach to effective recommender systems using implicit feedback
Expert Systems with Applications: An International Journal
Collaborative search and sensemaking of patents
CHI '08 Extended Abstracts on Human Factors in Computing Systems
Enhanced Content-Based Filtering Using Diverse Collaborative Prediction for Movie Recommendation
ACIIDS '09 Proceedings of the 2009 First Asian Conference on Intelligent Information and Database Systems
International Journal of Approximate Reasoning
Information Sciences: an International Journal
Journal of Network and Computer Applications
A patent quality analysis for innovative technology and product development
Advanced Engineering Informatics
Enhancing meta-portals using dynamic user context personalization techniques
Journal of Network and Computer Applications
Development and performance evaluation of a new RSS tool for a Web-based system: RSS_PROYECT
Journal of Network and Computer Applications
Editorial: Collaboration technologies and applications
Journal of Network and Computer Applications
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Patents' search is increasingly critical for a company's technological advancement and sustainable marketing strategy. When most innovative designs are created collaboratively by a diverse team of researchers and technologists, patent knowledge management becomes time consuming with repeated efforts creating additional task conflicts. This research develops an intelligent recommendation methodology and system to enable timely and effective patent search prior, during, and after design collaboration to prevent potential infringement of existing intellectual property rights (IPR) and to secure new IPR for market advantage. The research develops an algorithm to dynamically search related patents in global patent databases. The system clusters users with similar patent search behaviors and, subsequently, infers new patent recommendations based on inter-cluster group member behaviors and characteristics. First, the methodology evaluates the filtered information obtained from collaborative patent searches. Second, the system clusters existing users and identifies users' neighbors based on the collaborative filtering algorithm. Using the clusters of users and their behaviors, the system recommends related patents. When collaborative design teams are planning R&D policies or searching patents and prior art claims to create new IP and prevent or settles IP legal disputes, the intelligent recommendation system identifies and recommends patents with greater efficiency and accuracy than previous systems and methods described in the literature.