Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
Hidden Markov models for speech recognition
Technometrics
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Learning in the presence of concept drift and hidden contexts
Machine Learning
Communications of the ACM
Machine Learning - Special issue on context sensitivity and concept drift
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Content-based book recommending using learning for text categorization
DL '00 Proceedings of the fifth ACM conference on Digital libraries
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
A streaming ensemble algorithm (SEA) for large-scale classification
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Getting to know you: learning new user preferences in recommender systems
Proceedings of the 7th international conference on Intelligent user interfaces
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
Learning Collaborative Information Filters
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
Dependency networks for inference, collaborative filtering, and data visualization
The Journal of Machine Learning Research
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
A practical part-of-speech tagger
ANLC '92 Proceedings of the third conference on Applied natural language processing
IEEE Transactions on Knowledge and Data Engineering
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
The link-prediction problem for social networks
Journal of the American Society for Information Science and Technology
Google news personalization: scalable online collaborative filtering
Proceedings of the 16th international conference on World Wide Web
A Comparison of Collaborative-Filtering Recommendation Algorithms for E-commerce
IEEE Intelligent Systems
Lessons from the Netflix prize challenge
ACM SIGKDD Explorations Newsletter - Special issue on visual analytics
Context-aware recommender systems
Proceedings of the 2008 ACM conference on Recommender systems
Collaborative Filtering for Implicit Feedback Datasets
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
A Hidden Markov Model of Customer Relationship Dynamics
Marketing Science
Mind the gaps: weighting the unknown in large-scale one-class collaborative filtering
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
Recommending new movies: even a few ratings are more valuable than metadata
Proceedings of the third ACM conference on Recommender systems
Continuously variable duration hidden Markov models for automatic speech recognition
Computer Speech and Language
Collaborative filtering with temporal dynamics
Communications of the ACM
Prospective Infectious Disease Outbreak Detection Using Markov Switching Models
IEEE Transactions on Knowledge and Data Engineering
The adaptive web: methods and strategies of web personalization
The adaptive web: methods and strategies of web personalization
Factorizing personalized Markov chains for next-basket recommendation
Proceedings of the 19th international conference on World wide web
Temporal recommendation on graphs via long- and short-term preference fusion
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Music Recommendation and Discovery: The Long Tail, Long Fail, and Long Play in the Digital Music Space
Syskill & webert: Identifying interesting web sites
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Temporal Link Prediction Using Matrix and Tensor Factorizations
ACM Transactions on Knowledge Discovery from Data (TKDD)
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
A Hidden Markov Model of Developer Learning Dynamics in Open Source Software Projects
Information Systems Research
LoCA'05 Proceedings of the First international conference on Location- and Context-Awareness
HMM Word and Phrase Alignment for Statistical Machine Translation
IEEE Transactions on Audio, Speech, and Language Processing
A people-to-people content-based reciprocal recommender using hidden markov models
Proceedings of the 7th ACM conference on Recommender systems
Analysis of a context-aware recommender system model for smart urban environment
Proceedings of International Conference on Advances in Mobile Computing & Multimedia
Hi-index | 0.00 |
In this paper, we present a method to make personalized recommendations when user preferences change over time. Most of the works in the recommender systems literature have been developed under the assumption that user preference has a static pattern. However, this is a strong assumption especially when the user is observed over a long period of time. With the help of a data set on employees' blog reading behavior, we show that users' product selection behaviors change over time. We propose a hidden Markov model to correctly interpret the users' product selection behaviors and make personalized recommendations. The user preference is modeled as a hidden Markov sequence. A variable number of product selections of different types by each user in each time period requires a novel observation model. We propose a negative binomial mixture of multinomial to model such observations. This allows us to identify stable global preferences of users and to track individual users through these preferences. We evaluate our model using three real-world data sets with different characteristics. They include data on employee blog reading behavior inside a firm, users' movie rating behavior at Netflix, and users' music listening behavior collected through last.fm. We compare the recommendation performance of the proposed model with that of a number of collaborative filtering algorithms and a recently proposed temporal link prediction algorithm. We find that the proposed HMM-based collaborative filter performs as well as the best among the alternative algorithms when the data is sparse or static. However, it outperforms the existing algorithms when the data is less sparse and the user preference is changing. We further examine the performances of the algorithms using simulated data with different characteristics and highlight the scenarios where it is beneficial to use a dynamic model to generate product recommendation.