Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Proceedings of the 11th international conference on World Wide Web
E-Commerce Recommendation Applications
Data Mining and Knowledge Discovery
Eigentaste: A Constant Time Collaborative Filtering Algorithm
Information Retrieval
Extrapolation methods for accelerating PageRank computations
WWW '03 Proceedings of the 12th international conference on World Wide Web
The Eigentrust algorithm for reputation management in P2P networks
WWW '03 Proceedings of the 12th international conference on World Wide Web
Collaborative Filtering with Privacy
SP '02 Proceedings of the 2002 IEEE Symposium on Security and Privacy
Algorithms for estimating relative importance in networks
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
WI '05 Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Data sparsity issues in the collaborative filtering framework
WebKDD'05 Proceedings of the 7th international conference on Knowledge Discovery on the Web: advances in Web Mining and Web Usage Analysis
A Novel Recommending Algorithm Based on Topical PageRank
AI '08 Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Highly predictive blacklisting
SS'08 Proceedings of the 17th conference on Security symposium
Recommendation as link prediction: a graph kernel-based machine learning approach
Proceedings of the 9th ACM/IEEE-CS joint conference on Digital libraries
Scalable pseudo-likelihood estimation in hybrid random fields
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Improving memory-based collaborative filtering via similarity updating and prediction modulation
Information Sciences: an International Journal
Prediction of social bookmarking based on a behavior transition model
Proceedings of the 2010 ACM Symposium on Applied Computing
Enhancing link-based similarity through the use of non-numerical labels and prior information
Proceedings of the Eighth Workshop on Mining and Learning with Graphs
Recommendations Over Domain Specific User Graphs
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Classical music for rock fans?: novel recommendations for expanding user interests
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
A novel approach to compute similarities and its application to item recommendation
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
Lasso-based tag expansion and tag-boosted collaborative filtering
PCM'10 Proceedings of the Advances in multimedia information processing, and 11th Pacific Rim conference on Multimedia: Part II
Pagerank-based collaborative filtering recommendation
ICICA'10 Proceedings of the First international conference on Information computing and applications
Expert Systems with Applications: An International Journal
Userrank for item-based collaborative filtering recommendation
Information Processing Letters
Random walk based entity ranking on graph for multidimensional recommendation
Proceedings of the fifth ACM conference on Recommender systems
Mining relational context-aware graph for rater identification
Proceedings of the 2nd Challenge on Context-Aware Movie Recommendation
Determining user expertise for improving recommendation performance
Proceedings of the 6th International Conference on Ubiquitous Information Management and Communication
A generic graph-based multidimensional recommendation framework and its implementations
Proceedings of the 21st international conference companion on World Wide Web
Efficient personalized pagerank with accuracy assurance
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
PathRank: Ranking nodes on a heterogeneous graph for flexible hybrid recommender systems
Expert Systems with Applications: An International Journal
Social recommendation across multiple relational domains
Proceedings of the 21st ACM international conference on Information and knowledge management
A modified random walk framework for handling negative ratings and generating explanations
ACM Transactions on Intelligent Systems and Technology (TIST) - Special section on twitter and microblogging services, social recommender systems, and CAMRa2010: Movie recommendation in context
Tailoring recommendations to groups of users: a graph walk-based approach
Proceedings of the 2013 international conference on Intelligent user interfaces
A Random Walk Model for Item Recommendation in Social Tagging Systems
ACM Transactions on Management Information Systems (TMIS)
Leveraging biosignal and collaborative filtering for context-aware recommendation
Proceedings of the 1st ACM international workshop on Multimedia indexing and information retrieval for healthcare
Combining prestige and relevance ranking for personalized recommendation
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Integrating collaborative filtering and matching-based search for product recommendations
Journal of Theoretical and Applied Electronic Commerce Research
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Recommender systems are an emerging technology that helps consumers to find interesting products. A recommender system makes personalized product suggestions by extracting knowledge from the previous users interactions. In this paper, we present "ItemRank", a random-walk based scoring algorithm, which can be used to rank products according to expected user preferences, in order to recommend top-rank items to potentially interested users. We tested our algorithm on a standard database, the MovieLens data set, which contains data collected from a popular recommender system on movies, that has been widely exploited as a benchmark for evaluating recently proposed approaches to recommender system (e.g. [Fouss et al., 2005; Sarwar et al., 2002]). We compared ItemRank with other state-of-the-art ranking techniques (in particular the algorithms described in [Fouss et al., 2005]). Our experiments show that ItemRank performs better than the other algorithms we compared to and, at the same time, it is less complex than other proposed algorithms with respect to memory usage and computational cost too.