A Joint Dynamic Ranking System with DNN and Vector-based Clustering Bandit
Authors: 
Yu Liu, Xiaoxiao Xu, Jincheng Wang, Yong Li, Changping Peng, Yongjun Bao, Weipeng P.YanAuthors Info & Claims
Yu Liu, Xiaoxiao Xu, Jincheng Wang, Yong Li, Changping Peng, Yongjun Bao, Weipeng P.YanAuthors Info & ClaimsPages 645 - 650
Abstract
The ad-ranking module is the core of the advertising recommender system. Existing ad-ranking modules are mainly based on the deep neural network click-through rate prediction model. Recently an innovative ad-ranking paradigm called DNN-MAB has been introduced to address DNN-only paradigms’ weakness in perceiving highly dynamic user intent over time. We introduce the DNN-MAB paradigm into our ad-ranking system to alleviate the Matthew effect that harms the user experience. Due to data sparsity, however, the actual performance of DNN-MAB is lower than expected. In this paper, we propose an innovative ad-ranking paradigm called DNN-VMAB to solve these problems. Based on vectorization and clustering, it utilizes latent collaborative information in user behavior data to find a set of ads with higher relativity and diversity. As an integration of the essences of classical collaborative filtering, deep click-through rate prediction model, and contextual multi-armed bandit, it can improve platform revenue and user experience. Both offline and online experiments show the advantage of our new algorithm over DNN-MAB and some other existing algorithms.
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Published In
September 2020
796 pages
ISBN:9781450375832
DOI:10.1145/3383313
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Published: 22 September 2020
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RecSys '20: Fourteenth ACM Conference on Recommender Systems
September 22 - 26, 2020
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