Search Results for author: Deguang Kong

Found 11 papers, 2 papers with code

Learning Personalized User Preference from Cold Start in Multi-turn Conversations

no code implementations10 Sep 2023 Deguang Kong, Abhay Jha, Lei Yun

This paper presents a novel teachable conversation interaction system that is capable of learning users preferences from cold start by gradually adapting to personal preferences.

named-entity-recognition Named Entity Recognition +1

Personalized Search Via Neural Contextual Semantic Relevance Ranking

no code implementations10 Sep 2023 Deguang Kong, Daniel Zhou, Zhiheng Huang, Steph Sigalas

Existing neural relevance models do not give enough consideration for query and item context information which diversifies the search results to adapt for personal preference.

Document Ranking

Robust Consensus Clustering and its Applications for Advertising Forecasting

no code implementations27 Dec 2022 Deguang Kong, Miao Lu, Konstantin Shmakov, Jian Yang

Consensus clustering aggregates partitions in order to find a better fit by reconciling clustering results from different sources/executions.


Do not Waste Money on Advertising Spend: Bid Recommendation via Concavity Changes

no code implementations26 Dec 2022 Deguang Kong, Konstantin Shmakov, Jian Yang

In computational advertising, a challenging problem is how to recommend the bid for advertisers to achieve the best return on investment (ROI) given budget constraint.

Demystifying Advertising Campaign Bid Recommendation: A Constraint target CPA Goal Optimization

no code implementations26 Dec 2022 Deguang Kong, Konstantin Shmakov, Jian Yang

In cost-per-click (CPC) or cost-per-impression (CPM) advertising campaigns, advertisers always run the risk of spending the budget without getting enough conversions.

DeepLight: Deep Lightweight Feature Interactions for Accelerating CTR Predictions in Ad Serving

2 code implementations17 Feb 2020 Wei Deng, Junwei Pan, Tian Zhou, Deguang Kong, Aaron Flores, Guang Lin

To address the issue of significantly increased serving delay and high memory usage for ad serving in production, this paper presents \emph{DeepLight}: a framework to accelerate the CTR predictions in three aspects: 1) accelerate the model inference via explicitly searching informative feature interactions in the shallow component; 2) prune redundant layers and parameters at intra-layer and inter-layer level in the DNN component; 3) promote the sparsity of the embedding layer to preserve the most discriminant signals.

Click-Through Rate Prediction

Bayesian Time Series Forecasting with Change Point and Anomaly Detection

no code implementations ICLR 2018 Anderson Y. Zhang, Miao Lu, Deguang Kong, Jimmy Yang

However, their performance is easily undermined by the existence of change points and anomaly points, two structures commonly observed in real data, but rarely considered in the aforementioned methods.

Anomaly Detection Change Point Detection +3

DeepRebirth: Accelerating Deep Neural Network Execution on Mobile Devices

no code implementations16 Aug 2017 Dawei Li, Xiaolong Wang, Deguang Kong

As observed in the experiment, DeepRebirth achieves more than 3x speed-up and 2. 5x run-time memory saving on GoogLeNet with only 0. 4% drop of top-5 accuracy on ImageNet.

Model Compression

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