GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Recommending and evaluating choices in a virtual community of use
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Fab: content-based, collaborative recommendation
Communications of the ACM
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Recommendation as classification: using social and content-based information in recommendation
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
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
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Combining collaborative filtering with personal agents for better recommendations
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Evaluation of Item-Based Top-N Recommendation Algorithms
Proceedings of the tenth international conference on Information and knowledge management
Getting to know you: learning new user preferences in recommender systems
Proceedings of the 7th international conference on Intelligent user interfaces
Eigentaste: A Constant Time Collaborative Filtering Algorithm
Information Retrieval
Learning Collaborative Information Filters
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Latent Class Models for Collaborative Filtering
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Collaborative Filtering by Personality Diagnosis: A Hybrid Memory and Model-Based Approach
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
ACM Transactions on Information Systems (TOIS)
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
A collaborative filtering algorithm and evaluation metric that accurately model the user experience
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
A joint framework for collaborative and content filtering
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Fast maximum margin matrix factorization for collaborative prediction
ICML '05 Proceedings of the 22nd international conference on Machine learning
Experiences with GroupLens: marking usenet useful again
ATEC '97 Proceedings of the annual conference on USENIX Annual Technical Conference
Interfaces for eliciting new user preferences in recommender systems
UM'03 Proceedings of the 9th international conference on User modeling
Collaborative filtering with the simple Bayesian classifier
PRICAI'00 Proceedings of the 6th Pacific Rim international conference on Artificial intelligence
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Applying collaborative filtering techniques to movie search for better ranking and browsing
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem
Information Sciences: an International Journal
Eigentaste 5.0: constant-time adaptability in a recommender system using item clustering
Proceedings of the 2007 ACM conference on Recommender systems
A hybrid collaborative filtering recommender system using a new similarity measure
ACOS'07 Proceedings of the 6th Conference on WSEAS International Conference on Applied Computer Science - Volume 6
Addressing cold-start problem in recommendation systems
Proceedings of the 2nd international conference on Ubiquitous information management and communication
Whom should I trust?: the impact of key figures on cold start recommendations
Proceedings of the 2008 ACM symposium on Applied computing
Tied boltzmann machines for cold start recommendations
Proceedings of the 2008 ACM conference on Recommender systems
Key figure impact in trust-enhanced recommender systems
AI Communications - Recommender Systems
Personalized recommendation on dynamic content using predictive bilinear models
Proceedings of the 18th international conference on World wide web
Learning preferences of new users in recommender systems: an information theoretic approach
ACM SIGKDD Explorations Newsletter
Regression-based latent factor models
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Pairwise preference regression for cold-start recommendation
Proceedings of the third ACM conference on Recommender systems
A unified approach to building hybrid recommender systems
Proceedings of the third ACM conference on Recommender systems
Selecting a small number of products for effective user profiling in collaborative filtering
Expert Systems with Applications: An International Journal
Weighted Nonnegative Matrix Co-Tri-Factorization for Collaborative Prediction
ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
fLDA: matrix factorization through latent dirichlet allocation
Proceedings of the third ACM international conference on Web search and data mining
A contextual-bandit approach to personalized news article recommendation
Proceedings of the 19th international conference on World wide web
Prediction of social bookmarking based on a behavior transition model
Proceedings of the 2010 ACM Symposium on Applied Computing
Time-evolution of IPTV recommender systems
Proceedings of the 8th international interactive conference on Interactive TV&Video
Scholarly paper recommendation via user's recent research interests
Proceedings of the 10th annual joint conference on Digital libraries
International Journal of Approximate Reasoning
Who is talking about what: social map-based recommendation for content-centric social websites
Proceedings of the fourth ACM conference on Recommender systems
Design and user issues in personality-based recommender systems
Proceedings of the fourth ACM conference on Recommender systems
Developing trust networks based on user tagging information for recommendation making
WISE'10 Proceedings of the 11th international conference on Web information systems engineering
Multiple domain user personalization
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Who is Doing What and When: Social Map-Based Recommendation for Content-Centric Social Web Sites
ACM Transactions on Intelligent Systems and Technology (TIST)
Generalizing matrix factorization through flexible regression priors
Proceedings of the fifth ACM conference on Recommender systems
Wisdom of the better few: cold start recommendation via representative based rating elicitation
Proceedings of the fifth ACM conference on Recommender systems
Enhancing collaborative filtering systems with personality information
Proceedings of the fifth ACM conference on Recommender systems
A market-based approach to address the new item problem
Proceedings of the fifth ACM conference on Recommender systems
A collaborative filtering approach to mitigate the new user cold start problem
Knowledge-Based Systems
Overcoming browser cookie churn with clustering
Proceedings of the fifth ACM international conference on Web search and data mining
Impact of data characteristics on recommender systems performance
ACM Transactions on Management Information Systems (TMIS)
A latent model for collaborative filtering
International Journal of Approximate Reasoning
High quality recommendations for small communities: the case of a regional parent network
Proceedings of the sixth ACM conference on Recommender systems
Using profile expansion techniques to alleviate the new user problem
Information Processing and Management: an International Journal
Knowledge-Based Systems
Mixing bandits: a recipe for improved cold-start recommendations in a social network
Proceedings of the 7th Workshop on Social Network Mining and Analysis
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The goal of a recommender system is to suggest items of interest to a user based on historical behavior of a community of users. Given detailed enough history, item-based collaborative filtering (CF) often performs as well or better than almost any other recommendation method. However, in cold-start situations - where a user, an item, or the entire system is new - simple non-personalized recommendations often fare better. We improve the scalability and performance of a previous approach to handling cold-start situations that uses filterbots, or surrogate users that rate items based only on user or item attributes. We show that introducing a very small number of simple filterbots helps make CF algorithms more robust. In particular, adding just seven global filterbots improves both user-based and item-based CF in cold-start user, cold-start item, and cold-start system settings. Performance is better when data is scarce, performance is no worse when data is plentiful, and algorithm efficiency is negligibly affected. We systematically compare a non-personalized baseline, user-based CF, item-based CF, and our bot-augmented user- and item-based CF algorithms using three data sets (Yahoo! Movies, MovieLens, and EachMovie) with the normalized MAE metric in three types of cold-start situations. The advantage of our "naïve filterbot" approach is most pronounced for the Yahoo! data, the sparsest of the three data sets.