A semi-supervised hybrid system to enhance the recommendation of channels in terms of campaign roi
Pages 2265 - 2268
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 content-based 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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- A semi-supervised hybrid system to enhance the recommendation of channels in terms of campaign roi
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Published In
October 2011
2712 pages
ISBN:9781450307178
DOI:10.1145/2063576
Copyright © 2011 ACM.
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Published: 24 October 2011
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CIKM '11: International Conference on Information and Knowledge Management
October 24 - 28, 2011
Glasgow, Scotland, UK
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