Learning and Revising User Profiles: The Identification ofInteresting Web Sites
Machine Learning - Special issue on multistrategy learning
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
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Recommendation Systems: A Probabilistic Analysis
FOCS '98 Proceedings of the 39th Annual Symposium on Foundations of Computer Science
Collaborative filtering with decoupled models for preferences and ratings
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
The multiple multiplicative factor model for collaborative filtering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
ACM Transactions on the Web (TWEB)
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Collaborative filtering with temporal dynamics
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Predicting user interests from contextual information
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Time-Sensitive Language Modelling for Online Term Recurrence Prediction
ICTIR '09 Proceedings of the 2nd International Conference on Theory of Information Retrieval: Advances in Information Retrieval Theory
The foundations of cost-sensitive learning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Factorizing personalized Markov chains for next-basket recommendation
Proceedings of the 19th international conference on World wide web
Understanding web browsing behaviors through Weibull analysis of dwell time
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
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
Performance of recommender algorithms on top-n recommendation tasks
Proceedings of the fourth ACM conference on Recommender systems
WI-IAT '10 Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Adaptive bootstrapping of recommender systems using decision trees
Proceedings of the fourth ACM international conference on Web search and data mining
Utilizing marginal net utility for recommendation in e-commerce
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Utilizing related products for post-purchase recommendation in e-commerce
Proceedings of the fifth ACM conference on Recommender systems
Correlating financial time series with micro-blogging activity
Proceedings of the fifth ACM international conference on Web search and data mining
Care to comment?: recommendations for commenting on news stories
Proceedings of the 21st international conference on World Wide Web
Increasing temporal diversity with purchase intervals
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Time-sensitive query auto-completion
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Modeling user variance in time-biased gain
Proceedings of the Symposium on Human-Computer Interaction and Information Retrieval
Maximizing revenue from strategic recommendations under decaying trust
Proceedings of the 21st ACM international conference on Information and knowledge management
Opportunity model for e-commerce recommendation: right product; right time
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Large-scale social recommender systems: challenges and opportunities
Proceedings of the 22nd international conference on World Wide Web companion
Proceedings of the 7th ACM conference on Recommender systems
Modeling contextual agreement in preferences
Proceedings of the 23rd international conference on World wide web
| Hi-index | 0.00 |
Tenure is a critical factor for an individual to consider when making a job transition. For instance, software engineers make a job transition to senior software engineers in a span of 2 years on average, or it takes for approximately 3 years for realtors to switch to brokers. While most existing work on recommender systems focuses on finding what to recommend to a user, this paper places emphasis on when to make appropriate recommendations and its impact on the item selection in the context of a job recommender system. The approach we propose, however, is general and can be applied to any recommendation scenario where the decision-making process is dependent on the tenure (i.e., the time interval) between successive decisions. Our approach is inspired by the proportional hazards model in statistics. It models the tenure between two successive decisions and related factors. We further extend the model with a hierarchical Bayesian framework to address the problem of data sparsity. The proposed model estimates the likelihood of a user's decision to make a job transition at a certain time, which is denoted as the tenure-based decision probability. New and appropriate evaluation metrics are designed to analyze the model's performance on deciding when is the right time to recommend a job to a user. We validate the soundness of our approach by evaluating it with an anonymous job application dataset across 140+ industries on LinkedIn. Experimental results show that the hierarchical proportional hazards model has better predictability of the user's decision time, which in turn helps the recommender system to achieve higher utility/user satisfaction.