The budgeted maximum coverage problem
Information Processing Letters
Personalization techniques for online recruitment services
Communications of the ACM - The Adaptive Web
Automated Collaborative Filtering Applications for Online Recruitment Services
AH '00 Proceedings of the International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems
Social matching: A framework and research agenda
ACM Transactions on Computer-Human Interaction (TOCHI)
Matching People and Jobs: A Bilateral Recommendation Approach
HICSS '06 Proceedings of the 39th Annual Hawaii International Conference on System Sciences - Volume 06
Resume information extraction with cascaded hybrid model
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Expert Systems with Applications: An International Journal
ICAS '07 Proceedings of the Third International Conference on Autonomic and Autonomous Systems
An architecture for a next-generation holistic e-recruiting system
Communications of the ACM - Creating a science of games
Matching resumes and jobs based on relevance models
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Cost-effective outbreak detection in networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Introduction to Information Retrieval
Introduction to Information Retrieval
Collaborative Filtering for Implicit Feedback Datasets
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Relevance and ranking in online dating systems
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Multi-document summarization via budgeted maximization of submodular functions
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
RECON: a reciprocal recommender for online dating
Proceedings of the fourth ACM conference on Recommender systems
Improving one-class collaborative filtering by incorporating rich user information
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
People recommendation based on aggregated bidirectional intentions in social network site
PKAW'10 Proceedings of the 11th international conference on Knowledge management and acquisition for smart systems and services
SCENE: a scalable two-stage personalized news recommendation system
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
MSSF: a multi-document summarization framework based on submodularity
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Diversified ranking on large graphs: an optimization viewpoint
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Finding someone you will like and who won't reject you
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
A user-centric evaluation framework for recommender systems
Proceedings of the fifth ACM conference on Recommender systems
Machine learned job recommendation
Proceedings of the fifth ACM conference on Recommender systems
Stochastic matching and collaborative filtering to recommend people to people
Proceedings of the fifth ACM conference on Recommender systems
CCR: a content-collaborative reciprocal recommender for online dating
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
MEET: a generalized framework for reciprocal recommender systems
Proceedings of the 21st ACM international conference on Information and knowledge management
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Online recruiting systems have gained immense attention in the wake of more and more job seekers searching jobs and enterprises finding candidates on the Internet. A critical problem in a recruiting system is how to maximally satisfy the desires of both job seekers and enterprises with reasonable recommendations or search results. In this paper, we investigate and compare various online recruiting systems from a product perspective. We then point out several key functions that help achieve a win-win situation between job seekers and enterprises for a successful recruiting system. Based on the observations and key functions, we design, implement and deploy a web-based application of recruiting system, named iHR, for Xiamen Talent Service Center. The system utilizes the latest advances in data mining and recommendation technologies to create a user-oriented service for a myriad of audience in job marketing community. Empirical evaluation and online user studies demonstrate the efficacy and effectiveness of our proposed system. Currently, iHR has been deployed at http://i.xmrc.com.cn/XMRCIntel.