Artificial Intelligence


An End-to-end Model of Predicting Diverse Ranking OnHeterogeneous Feeds

Authors: Zizhe Gao, Zheng Gao, Heng Huang, Zhuoren Jiang, Yuliang Yan

As an external assistance for online shopping, multimedia content (feed) plays an important role in e-Commerce eld. Feeds in formats of post, item list and video bring in richer auxiliary information and more authentic assessments of commodities (items). In Alibaba, the largest Chinese online retailer, besides traditional item search engine (ISE), a content search engine (CSE) is utilized for feeds recommendation as well. However, the diversity of feed types raises a challenge for the CSE to rank heterogeneous feeds. In this paper, a two-step end-to-end model including Heterogeneous Type Sorting and Homogeneous Feed Ranking is proposed to address this problem. In the first step, an independent Multi-Armed bandit (iMAB) model is proposed first, and an improved personalized Markov Deep Neural Network (pMDNN) model is developed later on. In the second step, an existing Deep Structured Semantic Model (DSSM) is utilized for homogeneous feed ranking. A/B test on Alibaba product environment shows that, by considering user preference and feed type dependency, pMDNN model significantly outperforms than iMAB model to solve heterogeneous feed ranking problem.

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Submission history

[v1] 2018-06-21 03:50:18

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