This series features 12 papers from the annual KDD conference, which includes explanations from 12 Alibaba experts around Alibaba Cloud technologies in practice.
10 Alibaba Cloud experts discuss feature input in prediction tasks and using PFD to maximize privileged features, particularly with Taobao recommendations.
5 researchers propose the usage of a multi-task multi-view graph representation learning framework to learn node representations from multi-view graph...
Hear 3 experts from the Alibaba Group and 2 researchers from Tsinghua University talk about recommendation algorithms and machine learning.
This article introduces Graph Contrastive Coding (GCC), pre-training framework that uses the contrastive learning method to pre-train graph neural networks.
This article talks about the Gavotte model, which is used to automatically generate titles for buyer-uploaded videos in e-commerce scenarios.
We present the SGL learning framework to learn stable graph structures from heterogeneous confounded environments.
This paper presents a new training method that allows the recommender to learn the user's next intention and more intentions to come.
6 researchers explain benefits and challenges of graph representation learning.
6 researchers propose a controllable multi-interest framework for recommendation, which is used to recommend items based on a user's click sequence.
This blog talks about Taobao's shop search service, and how Taobao successfully improved long-tail search performance with DHGAN.
Five researchers explore the audience competition for online TV series by providing the competitiveness definition, algorithm design, and experimental comparison.
6 researchers explain the typical problems of query auto-completion with search engines and why the multi-view/multi-task attentive approach can be the solution.
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