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A Semi-Supervised Hybrid System to Enhance the Recommendation of Channels in terms of Campaign ROI

Abstract : 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 (?user?) ranking for a given campaign (?item?) 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.
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Julie Séguéla, Gilbert Saporta. A Semi-Supervised Hybrid System to Enhance the Recommendation of Channels in terms of Campaign ROI. CIKM'11, 20th ACM Conference on Information and Knowledge Management, Oct 2011, Glasgow, United Kingdom. pp.2265-2268. ⟨hal-01125926⟩

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