Fab: content-based, collaborative recommendation
Communications of the ACM
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
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
The Journal of Machine Learning Research
Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
A maximum entropy web recommendation system: combining collaborative and content features
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Fast maximum margin matrix factorization for collaborative prediction
ICML '05 Proceedings of the 22nd international conference on Machine learning
Naïve filterbots for robust cold-start recommendations
Proceedings of the 12th 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
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Bayesian probabilistic matrix factorization using Markov chain Monte Carlo
Proceedings of the 25th international conference on Machine learning
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Relational learning via collective matrix factorization
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Matchbox: large scale online bayesian recommendations
Proceedings of the 18th international conference on World wide web
Non-linear matrix factorization with Gaussian processes
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Large-scale collaborative prediction using a nonparametric random effects model
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Regression-based latent factor models
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Large-scale behavioral targeting
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Multi-HDP: a non parametric Bayesian model for tensor factorization
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Topic modeling for personalized recommendation of volatile items
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
Recommender systems with social regularization
Proceedings of the fourth ACM international conference on Web search and data mining
Recommending ephemeral items at web scale
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Collaborative topic modeling for recommending scientific articles
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Tracking trends: incorporating term volume into temporal topic models
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
Learning multiple models for exploiting predictive heterogeneity in recommender systems
Proceedings of the 2nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems
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
Challenging the long tail recommendation
Proceedings of the VLDB Endowment
LogUCB: an explore-exploit algorithm for comments recommendation
Proceedings of the 21st ACM international conference on Information and knowledge management
News recommendation via hypergraph learning: encapsulation of user behavior and news content
Proceedings of the sixth ACM international conference on Web search and data mining
App recommendation: a contest between satisfaction and temptation
Proceedings of the sixth ACM international conference on Web search and data mining
Co-factorization machines: modeling user interests and predicting individual decisions in Twitter
Proceedings of the sixth ACM international conference on Web search and data mining
Predicting User-to-content Links in Flickr Groups
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Silence is also evidence: interpreting dwell time for recommendation from psychological perspective
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning geographical preferences for point-of-interest recommendation
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
DIGTOBI: a recommendation system for Digg articles using probabilistic modeling
Proceedings of the 22nd international conference on World Wide Web
Scientific articles recommendation
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Xbox movies recommendations: variational bayes matrix factorization with embedded feature selection
Proceedings of the 7th ACM conference on Recommender systems
Personalized news recommendation via implicit social experts
Information Sciences: an International Journal
Celebrity recommendation with collaborative social topic regression
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Collaborative filtering with social regularization for TV program recommendation
Knowledge-Based Systems
A Monte Carlo algorithm for cold start recommendation
Proceedings of the 23rd international conference on World wide web
Hi-index | 0.00 |
We propose fLDA, a novel matrix factorization method to predict ratings in recommender system applications where a "bag-of-words" representation for item meta-data is natural. Such scenarios are commonplace in web applications like content recommendation, ad targeting and web search where items are articles, ads and web pages respectively. Because of data sparseness, regularization is key to good predictive accuracy. Our method works by regularizing both user and item factors simultaneously through user features and the bag of words associated with each item. Specifically, each word in an item is associated with a discrete latent factor often referred to as the topic of the word; item topics are obtained by averaging topics across all words in an item. Then, user rating on an item is modeled as user's affinity to the item's topics where user affinity to topics (user factors) and topic assignments to words in items (item factors) are learned jointly in a supervised fashion. To avoid overfitting, user and item factors are regularized through Gaussian linear regression and Latent Dirichlet Allocation (LDA) priors respectively. We show our model is accurate, interpretable and handles both cold-start and warm-start scenarios seamlessly through a single model. The efficacy of our method is illustrated on benchmark datasets and a new dataset from Yahoo! Buzz where fLDA provides superior predictive accuracy in cold-start scenarios and is comparable to state-of-the-art methods in warm-start scenarios. As a by-product, fLDA also identifies interesting topics that explains user-item interactions. Our method also generalizes a recently proposed technique called supervised LDA (sLDA) to collaborative filtering applications. While sLDA estimates item topic vectors in a supervised fashion for a single regression, fLDA incorporates multiple regressions (one for each user) in estimating the item factors.