A semi-supervised hybrid system to enhance the recommendation of channels in terms of campaign roi

Authors:
Julie Séguéla;Gilbert Saporta
Affiliations:
Cedric-CNAM & Multiposting.fr, Paris, France;Cedric-CNAM, Paris, France
Venue:
Proceedings of the 20th ACM international conference on Information and knowledge management
Year:
2011

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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.