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
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
A Framework for Collaborative, Content-Based and Demographic Filtering
Artificial Intelligence Review - Special issue on data mining on the Internet
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
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
Eigentaste: A Constant Time Collaborative Filtering Algorithm
Information Retrieval
Learning Collaborative Information Filters
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Collaborative Filtering by Personality Diagnosis: A Hybrid Memory and Model-Based Approach
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
A joint framework for collaborative and content filtering
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Taxonomy-driven computation of product recommendations
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Rank-R Approximation of Tensors: Using Image-as-Matrix Representation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Scalable collaborative filtering using cluster-based smoothing
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Collaborative prediction using ensembles of Maximum Margin Matrix Factorizations
ICML '06 Proceedings of the 23rd international conference on Machine learning
Beyond streams and graphs: dynamic tensor analysis
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Naïve filterbots for robust cold-start recommendations
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Predictive discrete latent factor models for large scale dyadic data
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
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
Matchbox: large scale online bayesian recommendations
Proceedings of the 18th international conference on World wide web
Personalized recommendation on dynamic content using predictive bilinear models
Proceedings of the 18th international conference on World wide web
Regression-based latent factor models
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Latent class models for collaborative filtering
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Who is talking about what: social map-based recommendation for content-centric social websites
Proceedings of the fourth ACM conference on Recommender systems
RECON: a reciprocal recommender for online dating
Proceedings of the fourth ACM conference on Recommender systems
Evaluating the dynamic properties of recommendation algorithms
Proceedings of the fourth ACM conference on Recommender systems
Enhanced email spam filtering through combining similarity graphs
Proceedings of the fourth ACM international conference on Web search and data mining
Functional matrix factorizations for cold-start recommendation
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Handling data sparsity in collaborative filtering using emotion and semantic based features
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Who is Doing What and When: Social Map-Based Recommendation for Content-Centric Social Web Sites
ACM Transactions on Intelligent Systems and Technology (TIST)
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
Finding a needle in a haystack of reviews: cold start context-based hotel recommender system
Proceedings of the sixth ACM conference on Recommender systems
PRemiSE: personalized news recommendation via implicit social experts
Proceedings of the 21st ACM international conference on Information and knowledge management
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
Using profile expansion techniques to alleviate the new user problem
Information Processing and Management: an International Journal
Knowledge-Based Systems
Addressing cold-start in app recommendation: latent user models constructed from twitter followers
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
DIGTOBI: a recommendation system for Digg articles using probabilistic modeling
Proceedings of the 22nd international conference on World Wide Web
Mixing bandits: a recipe for improved cold-start recommendations in a social network
Proceedings of the 7th Workshop on Social Network Mining and Analysis
Proceedings of the 7th ACM conference on Recommender systems
Personalized news recommendation via implicit social experts
Information Sciences: an International Journal
Multi-prototype label ranking with novel pairwise-to-total-rank aggregation
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
User Modeling and User-Adapted Interaction
Facing the cold start problem in recommender systems
Expert Systems with Applications: An International Journal
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Recommender systems are widely used in online e-commerce applications to improve user engagement and then to increase revenue. A key challenge for recommender systems is providing high quality recommendation to users in ``cold-start" situations. We consider three types of cold-start problems: 1) recommendation on existing items for new users; 2) recommendation on new items for existing users; 3) recommendation on new items for new users. We propose predictive feature-based regression models that leverage all available information of users and items, such as user demographic information and item content features, to tackle cold-start problems. The resulting algorithms scale efficiently as a linear function of the number of observations. We verify the usefulness of our approach in three cold-start settings on the MovieLens and EachMovie datasets, by comparing with five alternatives including random, most popular, segmented most popular, and two variations of Vibes affinity algorithm widely used at Yahoo! for recommendation.