GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Horting hatches an egg: a new graph-theoretic approach to collaborative filtering
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Studying Recommendation Algorithms by Graph Analysis
Journal of Intelligent Information Systems
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Taxonomy-driven computation of product recommendations
Proceedings of the thirteenth ACM international conference on Information and knowledge management
IEEE Transactions on Knowledge and Data Engineering
Personalized recommendation in social tagging systems using hierarchical clustering
Proceedings of the 2008 ACM conference on Recommender systems
TrustWalker: a random walk model for combining trust-based and item-based recommendation
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Introduction to Algorithms, Third Edition
Introduction to Algorithms, Third Edition
OSS: a semantic similarity function based on hierarchical ontologies
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
User profiles for personalized information access
The adaptive web
Networks: An Introduction
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In this paper we present two recommendation algorithms, called Node-Edge-Based and Node-Based recommendation algorithms. These algorithms are designed to recommend items to users connected via social network. Our algorithms are based on three main features: a social network analysis measure (degree centrality), the graph searching algorithm (Depth First Search algorithm), and the semantic similarity measure (which measures the closeness between the input item and users). We apply these algorithms to a real dataset (Amazon dataset) and we compare them with item-based collaborative filtering and hybrid recommendation algorithms. Our results show good precision as well as in a good performance in terms of runtime. Moreover, Node-Edge-Based and Node-Based algorithms search a small part of the dataset, compared to item-based and hybrid recommendation algorithms.