Comprehensive Audience Expansion based on End-to-End Neural Prediction

Jinling Jiang, Xiaoming Lin, Junjie Yao, Hua Lu

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningpeer review


In current online advertising applications, look-alike methods arevaluable and commonly used to identify new potential users, tack-ling the difficulties of audience expansion. However, the demo-graphic information and a variety of user behavior logs are highdimensional,noisy, and increasingly complex, which are challengingto extract suitable user profiles. Usually, rule-based and similarity-based approaches are proposed to profile the users’ interests andexpand the audience. However, they are specific and limited inmore complex scenarios.In this paper, we propose a new end-to-end solution, unifyingthe feature extraction and profile prediction stages. Specifically,we present a neural prediction framework and leverage it with theintuitive audience feature extraction stages. We conduct extensivestudy on a real and large advertisement dataset. The results demon-strate the advantage of the proposed approach, not only in accuracybut also generality.
TitelProceedings of the SIGIR 2019 Workshop on eCommerce, co-located with the 42st International ACM SIGIR Conference on Research and Development in Information Retrieval, eCom@SIGIR 2019, Paris, France, July 25, 2019
RedaktørerJon Degenhardt, Surya Kallumadi, Utkarsh Porwal, Andrew Trotman
ISBN (Trykt)1613-0073
StatusUdgivet - 2019
Udgivet eksterntJa
BegivenhedThe SIGIR 2019 Workshop on eCommerce - Paris, Frankrig
Varighed: 25 jul. 201925 jul. 2019


WorkshopThe SIGIR 2019 Workshop on eCommerce
NavnCEUR Workshop Proceedings

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