Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
Communications of the ACM
Recommender systems in e-commerce
Proceedings of the 1st ACM conference on Electronic commerce
Improved Query Matching Using kd-Trees: A Latent Semantic Indexing Enhancement
Information Retrieval
A family of algorithms for approximate bayesian inference
A family of algorithms for approximate bayesian inference
Gaussian Processes for Ordinal Regression
The Journal of Machine Learning Research
Google news personalization: scalable online collaborative filtering
Proceedings of the 16th international conference on World Wide Web
Restricted Boltzmann machines for collaborative filtering
Proceedings of the 24th international conference on Machine learning
Lessons from the Netflix prize challenge
ACM SIGKDD Explorations Newsletter - Special issue on visual analytics
Addressing cold-start problem in recommendation systems
Proceedings of the 2nd international conference on Ubiquitous information management and communication
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
Scalable clustering and keyword suggestion for online advertisements
Proceedings of the Third International Workshop on Data Mining and Audience Intelligence for Advertising
A spatio-temporal approach to collaborative filtering
Proceedings of the third ACM conference on Recommender systems
Pairwise preference regression for cold-start recommendation
Proceedings of the third ACM conference on Recommender systems
Hydra: a hybrid recommender system [cross-linked rating and content information]
Proceedings of the 1st ACM international workshop on Complex networks meet information & knowledge management
fLDA: matrix factorization through latent dirichlet allocation
Proceedings of the third ACM international conference on Web search and data mining
Statistical models of music-listening sessions in social media
Proceedings of the 19th international conference on World wide web
Estimating rates of rare events with multiple hierarchies through scalable log-linear models
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Aggregating preference graphs for collaborative rating prediction
Proceedings of the fourth ACM conference on Recommender systems
A grouped ranking model for item preference parameter
Neural Computation
CoBayes: bayesian knowledge corroboration with assessors of unknown areas of expertise
Proceedings of the fourth ACM international conference on Web search and data mining
Shopping for products you don't know you need
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
Fast context-aware recommendations with factorization machines
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Localized factor models for multi-context recommendation
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
An analysis of probabilistic methods for top-N recommendation in collaborative filtering
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
Generalizing matrix factorization through flexible regression priors
Proceedings of the fifth ACM conference on Recommender systems
Modeling item selection and relevance for accurate recommendations: a bayesian approach
Proceedings of the fifth ACM conference on Recommender systems
OrdRec: an ordinal model for predicting personalized item rating distributions
Proceedings of the fifth ACM conference on Recommender systems
Bayesian latent variable models for collaborative item rating prediction
Proceedings of the 20th ACM international conference on Information and knowledge management
Vulnerabilities and countermeasures in context-aware social rating services
ACM Transactions on Internet Technology (TOIT)
Personalized recommendation of user comments via factor models
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Factorization Machines with libFM
ACM Transactions on Intelligent Systems and Technology (TIST)
New objective functions for social collaborative filtering
Proceedings of the 21st international conference on World Wide Web
A hierarchical model for ordinal matrix factorization
Statistics and Computing
Kernel-Mapping Recommender system algorithms
Information Sciences: an International Journal
Personalized click shaping through lagrangian duality for online recommendation
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Collaborative personalized tweet recommendation
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Collaborative learning of preference rankings
Proceedings of the sixth ACM conference on Recommender systems
Proceedings of the sixth ACM conference on Recommender systems
An Online Learning Framework for Refining Recency Search Results with User Click Feedback
ACM Transactions on Information Systems (TOIS)
On the prediction of re-tweeting activities in social networks: a report on WISE 2012 challenge
WISE'12 Proceedings of the 13th international conference on Web Information Systems Engineering
PENETRATE: Personalized news recommendation using ensemble hierarchical clustering
Expert Systems with Applications: An International Journal
Content recommendation on web portals
Communications of the ACM
Mining large streams of user data for personalized recommendations
ACM SIGKDD Explorations Newsletter
Context mining and integration into predictive web analytics
Proceedings of the 22nd international conference on World Wide Web companion
Scaling factorization machines to relational data
Proceedings of the VLDB Endowment
One-class collaborative filtering with random graphs
Proceedings of the 22nd international conference on World Wide Web
Picture tags and world knowledge: learning tag relations from visual semantic sources
Proceedings of the 21st ACM international conference on Multimedia
Online multi-task collaborative filtering for on-the-fly recommender systems
Proceedings of the 7th ACM conference on Recommender systems
Selecting content-based features for collaborative filtering recommenders
Proceedings of the 7th ACM conference on Recommender systems
Improving pairwise learning for item recommendation from implicit feedback
Proceedings of the 7th ACM international conference on Web search and data mining
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We present a probabilistic model for generating personalised recommendations of items to users of a web service. The Matchbox system makes use of content information in the form of user and item meta data in combination with collaborative filtering information from previous user behavior in order to predict the value of an item for a user. Users and items are represented by feature vectors which are mapped into a low-dimensional `trait space' in which similarity is measured in terms of inner products. The model can be trained from different types of feedback in order to learn user-item preferences. Here we present three alternatives: direct observation of an absolute rating each user gives to some items, observation of a binary preference (like/ don't like) and observation of a set of ordinal ratings on a user-specific scale. Efficient inference is achieved by approximate message passing involving a combination of Expectation Propagation (EP) and Variational Message Passing. We also include a dynamics model which allows an item's popularity, a user's taste or a user's personal rating scale to drift over time. By using Assumed-Density Filtering (ADF) for training, the model requires only a single pass through the training data. This is an on-line learning algorithm capable of incrementally taking account of new data so the system can immediately reflect the latest user preferences. We evaluate the performance of the algorithm on the MovieLens and Netflix data sets consisting of approximately 1,000,000 and 100,000,000 ratings respectively. This demonstrates that training the model using the on-line ADF approach yields state-of-the-art performance with the option of improving performance further if computational resources are available by performing multiple EP passes over the training data.