Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
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
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
Competitive recommendation systems
STOC '02 Proceedings of the thiry-fourth annual ACM symposium on Theory of computing
A graph-based recommender system for digital library
Proceedings of the 2nd ACM/IEEE-CS joint conference on Digital libraries
Eigentaste: A Constant Time Collaborative Filtering Algorithm
Information Retrieval
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Convergent algorithms for collaborative filtering
Proceedings of the 4th ACM conference on Electronic commerce
Recommendation Systems: A Probabilistic Analysis
FOCS '98 Proceedings of the 39th Annual Symposium on Foundations of Computer Science
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
Using mixture models for collaborative filtering
STOC '04 Proceedings of the thirty-sixth annual ACM symposium on Theory of computing
Improved recommendation systems
SODA '05 Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms
IEEE Transactions on Knowledge and Data Engineering
Modeling relationships at multiple scales to improve accuracy of large recommender systems
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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
Grocery shopping recommendations based on basket-sensitive random walk
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
On social networks and collaborative recommendation
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Challenges in Personalizing and Decentralizing the Web: An Overview of GOSSPLE
SSS '09 Proceedings of the 11th International Symposium on Stabilization, Safety, and Security of Distributed Systems
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
Aggregating preference graphs for collaborative rating prediction
Proceedings of the fourth ACM conference on Recommender systems
List-wise learning to rank with matrix factorization for collaborative filtering
Proceedings of the fourth ACM conference on Recommender systems
Mining mood-specific movie similarity with matrix factorization for context-aware recommendation
Proceedings of the Workshop on Context-Aware Movie Recommendation
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
Exploiting user interests for collaborative filtering: interests expansion via personalized ranking
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
Expert Systems with Applications: An International Journal
Application of random walks to decentralized recommender systems
OPODIS'10 Proceedings of the 14th international conference on Principles of distributed systems
User-based Collaborative Filtering: Sparsity and Performance
Proceedings of the 2010 conference on STAIRS 2010: Proceedings of the Fifth Starting AI Researchers' Symposium
Categorising social tags to improve folksonomy-based recommendations
Web Semantics: Science, Services and Agents on the World Wide Web
Exploiting geographical influence for collaborative point-of-interest recommendation
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
The GOSSPLE anonymous social network
Proceedings of the ACM/IFIP/USENIX 11th International Conference on Middleware
An improved privacy-preserving DWT-based collaborative filtering scheme
Expert Systems with Applications: An International Journal
Mining relational context-aware graph for rater identification
Proceedings of the 2nd Challenge on Context-Aware Movie Recommendation
A recommendation system based on a subset of raters
Proceedings of the 6th International Conference on Ubiquitous Information Management and Communication
Multimedia Tools and Applications
Social knowledge-based recommender system. Application to the movies domain
Expert Systems with Applications: An International Journal
A slope one collaborative filtering recommendation algorithm using uncertain neighbors optimizing
WAIM'11 Proceedings of the 2011 international conference on Web-Age Information Management
How random walks can help tourism
ECIR'12 Proceedings of the 34th European conference on Advances in Information Retrieval
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Smoothing approach to alleviate the meager rating problem in collaborative recommender systems
Future Generation Computer Systems
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
Unifying rating-oriented and ranking-oriented collaborative filtering for improved recommendation
Information Sciences: an International Journal
A Random Walk Model for Item Recommendation in Social Tagging Systems
ACM Transactions on Management Information Systems (TMIS)
Using geospatial metadata to boost collaborative filtering
Proceedings of the 7th ACM conference on Recommender systems
Recommending scientific articles using bi-relational graph-based iterative RWR
Proceedings of the 7th ACM conference on Recommender systems
Location recommendation in location-based social networks using user check-in data
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
A Multimedia Recommender System
ACM Transactions on Internet Technology (TOIT)
Modeling and broadening temporal user interest in personalized news recommendation
Expert Systems with Applications: An International Journal
A Monte Carlo algorithm for cold start recommendation
Proceedings of the 23rd international conference on World wide web
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Collaborative Filtering is one of the most widely used approaches in recommendation systems which predicts user preferences by learning past user-item relationships. In recent years, item-oriented collaborative filtering methods came into prominence as they are more scalable compared to user-oriented methods. Item-oriented methods discover item-item relationships from the training data and use these relations to compute predictions. In this paper, we propose a novel item-oriented algorithm, Random Walk Recommender, that first infers transition probabilities between items based on their similarities and models finite length random walks on the item space to compute predictions. This method is especially useful when training data is less than plentiful, namely when typical similarity measures fail to capture actual relationships between items. Aside from the proposed prediction algorithm, the final transition probability matrix computed in one of the intermediate steps can be used as an item similarity matrix in typical item-oriented approaches. Thus, this paper suggests a method to enhance similarity matrices under sparse data as well. Experiments on MovieLens data show that Random Walk Recommender algorithm outperforms two other item-oriented methods in different sparsity levels while having the best performance difference in sparse datasets.