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[SS11] A Semi-Supervised Hybrid System to Enhance the Recommendation of Channels in terms of Campaign ROI
Conférence Internationale avec comité de lecture : CIKM'11, 20th ACM Conference on Information and Knowledge Management, October 2011, pp.2265-2268, Glasgow, GB,Mots clés: Recommender systems, feature extraction, knowledge management
Résumé:
In domains such as Marketing, Advertising or even Human
Resources (sourcing), decision-makers have to choose the
most suitable channels according to their objectives when
starting a campaign. In this paper, three recommender systems
providing channel (“userâ€) ranking for a given campaign
(“itemâ€) are introduced. This work refers exclusively
to the new item problem, which is still a challenging topic in
the literature. The first two systems are standard contentbased
recommendation approaches, with different rating estimation
techniques (model-based vs heuristic-based). To
overcome the lacks of previous approaches, we introduce a
new hybrid system using a supervised similarity based on
PLS components. Algorithms are compared in a case study:
purpose is to predict the ranking of job boards (job search
web sites) in terms of ROI (return on investment) per job
posting. In this application, the semi-supervised hybrid system
outperforms standard approaches.
Equipe:
msdma
BibTeX
| @inproceedings { | |||
| SS11, | |||
| title | = | "{A Semi-Supervised Hybrid System to Enhance the Recommendation of Channels in terms of Campaign ROI}", | |
| author | = | " J. Séguéla and G. Saporta ", | |
| booktitle | = | "{CIKM'11, 20th ACM Conference on Information and Knowledge Management}", | |
| year | = | 2011, | |
| month | = | "October", | |
| pages | = | "2265-2268", | |
| address | = | "Glasgow, GB", | |
| } | |||
