Algorithms for scalable synchronization on shared-memory multiprocessors
ACM Transactions on Computer Systems (TOCS)
Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Learning implicit user interest hierarchy for context in personalization
Proceedings of the 8th international conference on Intelligent user interfaces
The Journal of Machine Learning Research
Adaptive web search based on user profile constructed without any effort from users
Proceedings of the 13th international conference on World Wide Web
ICML '06 Proceedings of the 23rd international conference on Machine learning
Predictive discrete latent factor models for large scale dyadic data
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 18th international conference on World wide web
Large-scale behavioral targeting
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Audience selection for on-line brand advertising: privacy-friendly social network targeting
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient methods for topic model inference on streaming document collections
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
PLDA: Parallel Latent Dirichlet Allocation for Large-Scale Applications
AAIM '09 Proceedings of the 5th International Conference on Algorithmic Aspects in Information and Management
Beyond DCG: user behavior as a predictor of a successful search
Proceedings of the third ACM international conference on Web search and data mining
Distributed Algorithms for Topic Models
The Journal of Machine Learning Research
User profiles for personalized information access
The adaptive web
Dynamic adaptation strategies for long-term and short-term user profile to personalize search
APWeb/WAIM'07 Proceedings of the joint 9th Asia-Pacific web and 8th international conference on web-age information management conference on Advances in data and web management
A characterization of online browsing behavior
Proceedings of the 19th international conference on World wide web
A contextual-bandit approach to personalized news article recommendation
Proceedings of the 19th international conference on World wide web
Online multiscale dynamic topic models
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
An architecture for parallel topic models
Proceedings of the VLDB Endowment
Scalable inference in latent variable models
Proceedings of the fifth ACM international conference on Web search and data mining
Web-scale user modeling for targeting
Proceedings of the 21st international conference companion on World Wide Web
TEM: a novel perspective to modeling content onmicroblogs
Proceedings of the 21st international conference companion on World Wide Web
Supercharging recommender systems using taxonomies for learning user purchase behavior
Proceedings of the VLDB Endowment
Estimating conversion rate in display advertising from past erformance data
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Probabilistic macro behavioral targeting
Proceedings of the 2012 workshop on Data-driven user behavioral modelling and mining from social media
Enabling direct interest-aware audience selection
Proceedings of the 21st ACM international conference on Information and knowledge management
Web-scale multi-task feature selection for behavioral targeting
Proceedings of the 21st ACM international conference on Information and knowledge management
Latent factor models with additive and hierarchically-smoothed user preferences
Proceedings of the sixth ACM international conference on Web search and data mining
Intuitive Topic Discovery by Incorporating Word-Pair's Connection Into LDA
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Incorporating popularity in topic models for social network analysis
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Distributed large-scale natural graph factorization
Proceedings of the 22nd international conference on World Wide Web
Towards a robust modeling of temporal interest change patterns for behavioral targeting
Proceedings of the 22nd international conference on World Wide Web
Proceedings of the 22nd international conference on World Wide Web
A probabilistic graphical model for brand reputation assessment in social networks
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
I act, therefore I judge: network sentiment dynamics based on user activity change
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Real time bid optimization with smooth budget delivery in online advertising
Proceedings of the Seventh International Workshop on Data Mining for Online Advertising
Behavior analysis of low-literate users of a viral speech-based telephone service
Proceedings of the 4th Annual Symposium on Computing for Development
Scalable dynamic nonparametric Bayesian models of content and users
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Historical user activity is key for building user profiles to predict the user behavior and affinities in many web applications such as targeting of online advertising, content personalization and social recommendations. User profiles are temporal, and changes in a user's activity patterns are particularly useful for improved prediction and recommendation. For instance, an increased interest in car-related web pages may well suggest that the user might be shopping for a new vehicle.In this paper we present a comprehensive statistical framework for user profiling based on topic models which is able to capture such effects in a fully \emph{unsupervised} fashion. Our method models topical interests of a user dynamically where both the user association with the topics and the topics themselves are allowed to vary over time, thus ensuring that the profiles remain current. We describe a streaming, distributed inference algorithm which is able to handle tens of millions of users. Our results show that our model contributes towards improved behavioral targeting of display advertising relative to baseline models that do not incorporate topical and/or temporal dependencies. As a side-effect our model yields human-understandable results which can be used in an intuitive fashion by advertisers.