no code implementations • 8 May 2024 • Kai Zheng, Haijun Zhao, Rui Huang, Beichuan Zhang, Na Mou, Yanan Niu, Yang song, Hongning Wang, Kun Gai
To address this issue, we propose an improved ranking principle for multi-stage systems, namely the Generalized Probability Ranking Principle (GPRP), to emphasize both the selection bias in each stage of the system pipeline as well as the underlying interest of users.
no code implementations • 7 May 2024 • Jian Jia, Yipei Wang, Yan Li, Honggang Chen, Xuehan Bai, Zhaocheng Liu, Jian Liang, Quan Chen, Han Li, Peng Jiang, Kun Gai
Contemporary recommender systems predominantly rely on collaborative filtering techniques, employing ID-embedding to capture latent associations among users and items.
1 code implementation • 29 Apr 2024 • Zijian Zhang, Shuchang Liu, Jiaao Yu, Qingpeng Cai, Xiangyu Zhao, Chunxu Zhang, Ziru Liu, Qidong Liu, Hongwei Zhao, Lantao Hu, Peng Jiang, Kun Gai
M3oE integrates multi-domain information, maps knowledge across domains and tasks, and optimizes multiple objectives.
no code implementations • 17 Apr 2024 • Jiao Ou, Jiayu Wu, Che Liu, Fuzheng Zhang, Di Zhang, Kun Gai
Existing methods target instructions from real instruction dialogues as a learning goal and fine-tune a user simulator for posing instructions.
no code implementations • 6 Apr 2024 • Yabin Zhang, Wenhui Yu, Erhan Zhang, Xu Chen, Lantao Hu, Peng Jiang, Kun Gai
For the model part, we adopt Generative Pre-training Transformer (GPT) as the sequential recommendation model and design a user modular to capture personalized information.
no code implementations • 4 Apr 2024 • Ziru Liu, Shuchang Liu, Zijian Zhang, Qingpeng Cai, Xiangyu Zhao, Kesen Zhao, Lantao Hu, Peng Jiang, Kun Gai
In the landscape of Recommender System (RS) applications, reinforcement learning (RL) has recently emerged as a powerful tool, primarily due to its proficiency in optimizing long-term rewards.
no code implementations • 16 Feb 2024 • Yihong Tang, Jiao Ou, Che Liu, Fuzheng Zhang, Di Zhang, Kun Gai
Experiments on models improved by RoleAD indicate that our adversarial dataset ameliorates this deficiency, with the improvements demonstrating a degree of generalizability in ordinary scenarios.
1 code implementation • 5 Feb 2024 • Yang Jin, Zhicheng Sun, Kun Xu, Liwei Chen, Hao Jiang, Quzhe Huang, Chengru Song, Yuliang Liu, Di Zhang, Yang song, Kun Gai, Yadong Mu
In light of recent advances in multimodal Large Language Models (LLMs), there is increasing attention to scaling them from image-text data to more informative real-world videos.
Ranked #64 on Visual Question Answering on MM-Vet
no code implementations • 29 Jan 2024 • Xiaobei Wang, Shuchang Liu, Xueliang Wang, Qingpeng Cai, Lantao Hu, Han Li, Peng Jiang, Kun Gai, Guangming Xie
Furthermore, we show that a reward-based future decomposition strategy can better express the item-wise future impact and improve the recommendation accuracy in the long term.
1 code implementation • 14 Nov 2023 • GuanYu Lin, Chen Gao, Yu Zheng, Yinfeng Li, Jianxin Chang, Yanan Niu, Yang song, Kun Gai, Zhiheng Li, Depeng Jin, Yong Li
In this paper, we propose a meta-learning method to annotate the unlabeled data from loss and gradient perspectives, which considers the noises in both positive and negative instances.
no code implementations • 14 Nov 2023 • Lei Lin, Jiayi Fu, Pengli Liu, Qingyang Li, Yan Gong, Junchen Wan, Fuzheng Zhang, Zhongyuan Wang, Di Zhang, Kun Gai
Although chain-of-thought (CoT) prompting combined with language models has achieved encouraging results on complex reasoning tasks, the naive greedy decoding used in CoT prompting usually causes the repetitiveness and local optimality.
1 code implementation • 14 Nov 2023 • GuanYu Lin, Chen Gao, Yu Zheng, Jianxin Chang, Yanan Niu, Yang song, Kun Gai, Zhiheng Li, Depeng Jin, Yong Li, Meng Wang
Recent proposed cross-domain sequential recommendation models such as PiNet and DASL have a common drawback relying heavily on overlapped users in different domains, which limits their usage in practical recommender systems.
no code implementations • 3 Nov 2023 • Jiao Ou, Junda Lu, Che Liu, Yihong Tang, Fuzheng Zhang, Di Zhang, Kun Gai
In this paper, we propose DialogBench, a dialogue evaluation benchmark that contains 12 dialogue tasks to probe the capabilities of LLMs as human-like dialogue systems should have.
no code implementations • 16 Oct 2023 • Yunli Wang, Zhiqiang Wang, Jian Yang, Shiyang Wen, Dongying Kong, Han Li, Kun Gai
Concretely, we employ multi-task learning to adaptively combine the optimization of relaxed and full targets, which refers to metrics Recall@m@k and OPA respectively.
no code implementations • 11 Oct 2023 • Jiayi Fu, Lei Lin, Xiaoyang Gao, Pengli Liu, Zhengzong Chen, Zhirui Yang, ShengNan Zhang, Xue Zheng, Yan Li, Yuliang Liu, Xucheng Ye, Yiqiao Liao, Chao Liao, Bin Chen, Chengru Song, Junchen Wan, Zijia Lin, Fuzheng Zhang, Zhongyuan Wang, Di Zhang, Kun Gai
Recent advancements in large language models (LLMs) have demonstrated remarkable abilities in handling a variety of natural language processing (NLP) downstream tasks, even on mathematical tasks requiring multi-step reasoning.
Ranked #88 on Arithmetic Reasoning on GSM8K (using extra training data)
no code implementations • 11 Oct 2023 • Yuchong Sun, Che Liu, Jinwen Huang, Ruihua Song, Fuzheng Zhang, Di Zhang, Zhongyuan Wang, Kun Gai
In this paper, we address these challenges by introducing Parrot, a highly scalable solution designed to automatically generate high-quality instruction-tuning data, which are then used to enhance the effectiveness of chat models in multi-turn conversations.
no code implementations • 6 Oct 2023 • Zhenghai Xue, Qingpeng Cai, Tianyou Zuo, Bin Yang, Lantao Hu, Peng Jiang, Kun Gai, Bo An
One challenge in large-scale online recommendation systems is the constant and complicated changes in users' behavior patterns, such as interaction rates and retention tendencies.
1 code implementation • NeurIPS 2023 • Kesen Zhao, Shuchang Liu, Qingpeng Cai, Xiangyu Zhao, Ziru Liu, Dong Zheng, Peng Jiang, Kun Gai
For each task, KuaiSim also provides evaluation protocols and baseline recommendation algorithms that further serve as benchmarks for future research.
1 code implementation • 9 Sep 2023 • Yang Jin, Kun Xu, Liwei Chen, Chao Liao, Jianchao Tan, Quzhe Huang, Bin Chen, Chenyi Lei, An Liu, Chengru Song, Xiaoqiang Lei, Di Zhang, Wenwu Ou, Kun Gai, Yadong Mu
Specifically, we introduce a well-designed visual tokenizer to translate the non-linguistic image into a sequence of discrete tokens like a foreign language that LLM can read.
no code implementations • 11 Aug 2023 • Yue Feng, Shuchang Liu, Zhenghai Xue, Qingpeng Cai, Lantao Hu, Peng Jiang, Kun Gai, Fei Sun
For response generation, we utilize the generation ability of LLM as a language interface to better interact with users.
no code implementations • 8 Aug 2023 • Yunzhu Pan, Chen Gao, Jianxin Chang, Yanan Niu, Yang song, Kun Gai, Depeng Jin, Yong Li
To enhance the robustness of our model, we then introduce a multi-task learning module to simultaneously optimize two kinds of feedback -- passive-negative feedback and traditional randomly-sampled negative feedback.
no code implementations • 1 Aug 2023 • Cheng Wu, Chaokun Wang, Jingcao Xu, Ziyang Liu, Kai Zheng, Xiaowei Wang, Yang song, Kun Gai
Specifically, we present GACN, a novel Generative Adversarial Contrastive learning Network for graph representation learning.
no code implementations • 7 Jun 2023 • Ziyang Liu, Chaokun Wang, Jingcao Xu, Cheng Wu, Kai Zheng, Yang song, Na Mou, Kun Gai
Recommender systems play a crucial role in addressing the issue of information overload by delivering personalized recommendations to users.
no code implementations • NeurIPS 2023 • Zhenghai Xue, Qingpeng Cai, Shuchang Liu, Dong Zheng, Peng Jiang, Kun Gai, Bo An
Data with dynamics shift are separated according to their environment parameters to train the corresponding policy.
1 code implementation • 4 Jun 2023 • Shuchang Liu, Qingpeng Cai, Zhankui He, Bowen Sun, Julian McAuley, Dong Zheng, Peng Jiang, Kun Gai
In this work, we aim to learn a policy that can generate sufficiently diverse item lists for users while maintaining high recommendation quality.
1 code implementation • 22 May 2023 • Jingcao Xu, Chaokun Wang, Cheng Wu, Yang song, Kai Zheng, Xiaowei Wang, Changping Wang, Guorui Zhou, Kun Gai
Secondly, existing methods utilizing self-supervised learning (SSL) to tackle the data sparsity neglect the serious optimization imbalance between the SSL task and the target task.
1 code implementation • 18 May 2023 • Zihua Si, Zhongxiang Sun, Xiao Zhang, Jun Xu, Xiaoxue Zang, Yang song, Kun Gai, Ji-Rong Wen
In our paper, we propose a Search-Enhanced framework for the Sequential Recommendation (SESRec) that leverages users' search interests for recommendation, by disentangling similar and dissimilar representations within S&R behaviors.
1 code implementation • 7 Feb 2023 • Ziru Liu, Jiejie Tian, Qingpeng Cai, Xiangyu Zhao, Jingtong Gao, Shuchang Liu, Dayou Chen, Tonghao He, Dong Zheng, Peng Jiang, Kun Gai
To be specific, the RMTL structure can address the two aforementioned issues by (i) constructing an MTL environment from session-wise interactions and (ii) training multi-task actor-critic network structure, which is compatible with most existing MTL-based recommendation models, and (iii) optimizing and fine-tuning the MTL loss function using the weights generated by critic networks.
1 code implementation • 7 Feb 2023 • Shuchang Liu, Qingpeng Cai, Bowen Sun, Yuhao Wang, Ji Jiang, Dong Zheng, Kun Gai, Peng Jiang, Xiangyu Zhao, Yongfeng Zhang
To overcome this challenge, we propose a hyper-actor and critic learning framework where the policy decomposes the item list generation process into a hyper-action inference step and an effect-action selection step.
1 code implementation • 7 Feb 2023 • Weiqi Zhao, Dian Tang, Xin Chen, Dawei Lv, Daoli Ou, Biao Li, Peng Jiang, Kun Gai
Most previous studies neglect user's conformity and entangle interest with it, which may cause the recommender systems fail to provide satisfying results.
no code implementations • 6 Feb 2023 • Yuan Zhang, Xue Dong, Weijie Ding, Biao Li, Peng Jiang, Kun Gai
Embedding-based retrieval (EBR) methods are widely used in modern recommender systems thanks to its simplicity and effectiveness.
no code implementations • 5 Feb 2023 • Jianxin Chang, Chenbin Zhang, Zhiyi Fu, Xiaoxue Zang, Lin Guan, Jing Lu, Yiqun Hui, Dewei Leng, Yanan Niu, Yang song, Kun Gai
And for the user-item cross features, we compress each into a one-dimentional bias term in the attention score calculation to save the computational cost.
1 code implementation • 3 Feb 2023 • Qingpeng Cai, Zhenghai Xue, Chi Zhang, Wanqi Xue, Shuchang Liu, Ruohan Zhan, Xueliang Wang, Tianyou Zuo, Wentao Xie, Dong Zheng, Peng Jiang, Kun Gai
One the one hand, the platforms aims at optimizing the users' cumulative watch time (main goal) in long term, which can be effectively optimized by Reinforcement Learning.
no code implementations • 3 Feb 2023 • Qingpeng Cai, Shuchang Liu, Xueliang Wang, Tianyou Zuo, Wentao Xie, Bin Yang, Dong Zheng, Peng Jiang, Kun Gai
In this paper, we choose reinforcement learning methods to optimize the retention as they are designed to maximize the long-term performance.
1 code implementation • 2 Feb 2023 • Jianxin Chang, Chenbin Zhang, Yiqun Hui, Dewei Leng, Yanan Niu, Yang song, Kun Gai
By infusing personalized selection of Embedding and personalized modification of DNN parameters, PEPNet tailored to the interests of each individual obtains significant performance gains, with online improvements exceeding 1\% in multiple task metrics across multiple domains.
1 code implementation • 6 Dec 2022 • Wanqi Xue, Qingpeng Cai, Zhenghai Xue, Shuo Sun, Shuchang Liu, Dong Zheng, Peng Jiang, Kun Gai, Bo An
Though promising, the application of RL heavily relies on well-designed rewards, but designing rewards related to long-term user engagement is quite difficult.
no code implementations • 20 Aug 2022 • Xudong Gong, Qinlin Feng, Yuan Zhang, Jiangling Qin, Weijie Ding, Biao Li, Peng Jiang, Kun Gai
However, as users continue to watch videos and feedback, the changing context leads the ranking of the server-side recommendation system inaccurate.
1 code implementation • 1 Jun 2022 • Wanqi Xue, Qingpeng Cai, Ruohan Zhan, Dong Zheng, Peng Jiang, Kun Gai, Bo An
Meanwhile, reinforcement learning (RL) is widely regarded as a promising framework for optimizing long-term engagement in sequential recommendation.
no code implementations • 18 Feb 2021 • Jin Li, Jie Liu, Shangzhou Li, Yao Xu, Ran Cao, Qi Li, Biye Jiang, Guan Wang, Han Zhu, Kun Gai, Xiaoqiang Zhu
When receiving a user request, matching system (i) finds the crowds that the user belongs to; (ii) retrieves all ads that have targeted those crowds.
no code implementations • 5 Dec 2020 • Zhilin Zhang, Xiangyu Liu, Zhenzhe Zheng, Chenrui Zhang, Miao Xu, Junwei Pan, Chuan Yu, Fan Wu, Jian Xu, Kun Gai
In e-commerce advertising, the ad platform usually relies on auction mechanisms to optimize different performance metrics, such as user experience, advertiser utility, and platform revenue.
1 code implementation • 25 Nov 2020 • Chao Du, Zhifeng Gao, Shuo Yuan, Lining Gao, Ziyan Li, Yifan Zeng, Xiaoqiang Zhu, Jian Xu, Kun Gai, Kuang-Chih Lee
In this paper, we propose a novel Deep Uncertainty-Aware Learning (DUAL) method to learn CTR models based on Gaussian processes, which can provide predictive uncertainty estimations while maintaining the flexibility of deep neural networks.
no code implementations • 3 Sep 2020 • Zhaoqing Peng, Junqi Jin, Lan Luo, Yaodong Yang, Rui Luo, Jun Wang, Wei-Nan Zhang, Haiyang Xu, Miao Xu, Chuan Yu, Tiejian Luo, Han Li, Jian Xu, Kun Gai
To drive purchase in online advertising, it is of the advertiser's great interest to optimize the sequential advertising strategy whose performance and interpretability are both important.
no code implementations • 20 Aug 2020 • Liyi Guo, Rui Lu, Haoqi Zhang, Junqi Jin, Zhenzhe Zheng, Fan Wu, Jin Li, Haiyang Xu, Han Li, Wenkai Lu, Jian Xu, Kun Gai
For e-commerce platforms such as Taobao and Amazon, advertisers play an important role in the entire digital ecosystem: their behaviors explicitly influence users' browsing and shopping experience; more importantly, advertiser's expenditure on advertising constitutes a primary source of platform revenue.
2 code implementations • 31 Jul 2020 • Zhe Wang, Liqin Zhao, Biye Jiang, Guorui Zhou, Xiaoqiang Zhu, Kun Gai
We name it COLD (Computing power cost-aware Online and Lightweight Deep pre-ranking system).
no code implementations • ICML 2020 • Xiaotian Hao, Zhaoqing Peng, Yi Ma, Guan Wang, Junqi Jin, Jianye Hao, Shan Chen, Rongquan Bai, Mingzhou Xie, Miao Xu, Zhenzhe Zheng, Chuan Yu, Han Li, Jian Xu, Kun Gai
In E-commerce, advertising is essential for merchants to reach their target users.
1 code implementation • ICML 2020 • Jingwei Zhuo, Ziru Xu, Wei Dai, Han Zhu, Han Li, Jian Xu, Kun Gai
Retrieving relevant targets from an extremely large target set under computational limits is a common challenge for information retrieval and recommendation systems.
no code implementations • 17 Jun 2020 • Biye Jiang, Pengye Zhang, Rihan Chen, Binding Dai, Xinchen Luo, Yin Yang, Guan Wang, Guorui Zhou, Xiaoqiang Zhu, Kun Gai
These stages usually allocate resource manually with specific computing power budgets, which requires the serving configuration to adapt accordingly.
1 code implementation • 10 Jun 2020 • Pi Qi, Xiaoqiang Zhu, Guorui Zhou, Yujing Zhang, Zhe Wang, Lejian Ren, Ying Fan, Kun Gai
Serving the main traffic in our real system now, SIM models user behavior data with maximum length reaching up to 54000, pushing SOTA to 54x.
no code implementations • 9 May 2020 • Xiaotian Hao, Junqi Jin, Jianye Hao, Jin Li, Weixun Wang, Yi Ma, Zhenzhe Zheng, Han Li, Jian Xu, Kun Gai
Bipartite b-matching is fundamental in algorithm design, and has been widely applied into economic markets, labor markets, etc.
1 code implementation • 30 Apr 2020 • Jiarui Jin, Yuchen Fang, Wei-Nan Zhang, Kan Ren, Guorui Zhou, Jian Xu, Yong Yu, Jun Wang, Xiaoqiang Zhu, Kun Gai
Position bias is a critical problem in information retrieval when dealing with implicit yet biased user feedback data.
1 code implementation • 14 Nov 2019 • Bo Wang, Quan Chen, Min Zhou, Zhiqiang Zhang, Xiaogang Jin, Kun Gai
Feature matters for salient object detection.
no code implementations • IJCNLP 2019 • Zhangming Chan, Xiuying Chen, Yongliang Wang, Juntao Li, Zhiqiang Zhang, Kun Gai, Dongyan Zhao, Rui Yan
Different from other text generation tasks, in product description generation, it is of vital importance to generate faithful descriptions that stick to the product attribute information.
no code implementations • 19 Aug 2019 • Dagui Chen, Junqi Jin, Wei-Nan Zhang, Fei Pan, Lvyin Niu, Chuan Yu, Jun Wang, Han Li, Jian Xu, Kun Gai
We refer to this process as Leverage.
no code implementations • 25 Jun 2019 • Guorui Zhou, Kailun Wu, Weijie Bian, Zhao Yang, Xiaoqiang Zhu, Kun Gai
In this paper, we model user behavior using an interest delay model, study carefully the embedding mechanism, and obtain two important results: (i) We theoretically prove that small aggregation radius of embedding vectors of items which belongs to a same user interest domain will result in good generalization performance of deep CTR model.
2 code implementations • 22 May 2019 • Qi Pi, Weijie Bian, Guorui Zhou, Xiaoqiang Zhu, Kun Gai
To our knowledge, this is one of the first industrial solutions that are capable of handling long sequential user behavior data with length scaling up to thousands.
1 code implementation • 2 May 2019 • Kan Ren, Jiarui Qin, Yuchen Fang, Wei-Nan Zhang, Lei Zheng, Weijie Bian, Guorui Zhou, Jian Xu, Yong Yu, Xiaoqiang Zhu, Kun Gai
In order to tackle these challenges, in this paper, we propose a Hierarchical Periodic Memory Network for lifelong sequential modeling with personalized memorization of sequential patterns for each user.
no code implementations • 26 Mar 2019 • Yuchi Zhang, Yongliang Wang, Liping Zhang, Zhiqiang Zhang, Kun Gai
In fact, this objective term guides the encoder towards the "best encoder" of the decoder to enhance the expressiveness.
1 code implementation • NeurIPS 2019 • Han Zhu, Daqing Chang, Ziru Xu, Pengye Zhang, Xiang Li, Jie He, Han Li, Jian Xu, Kun Gai
The previous work Tree-based Deep Model (TDM) \cite{zhu2018learning} greatly improves recommendation accuracy using tree index.
15 code implementations • 11 Sep 2018 • Guorui Zhou, Na Mou, Ying Fan, Qi Pi, Weijie Bian, Chang Zhou, Xiaoqiang Zhu, Kun Gai
Easy-to-use, Modular and Extendible package of deep-learning based CTR models. DeepFM, DeepInterestNetwork(DIN), DeepInterestEvolutionNetwork(DIEN), DeepCrossNetwork(DCN), AttentionalFactorizationMachine(AFM), Neural Factorization Machine(NFM), AutoInt
Ranked #1 on Click-Through Rate Prediction on Amazon Dataset
no code implementations • 10 Sep 2018 • Weixun Wang, Junqi Jin, Jianye Hao, Chunjie Chen, Chuan Yu, Wei-Nan Zhang, Jun Wang, Xiaotian Hao, Yixi Wang, Han Li, Jian Xu, Kun Gai
In this paper, we investigate the problem of advertising with adaptive exposure: can we dynamically determine the number and positions of ads for each user visit under certain business constraints so that the platform revenue can be increased?
no code implementations • 10 Sep 2018 • Di Wu, Cheng Chen, Xun Yang, Xiujun Chen, Qing Tan, Jian Xu, Kun Gai
With this formulation, we derive the optimal impression allocation strategy by solving the optimal bidding functions for contracts.
Multi-agent Reinforcement Learning reinforcement-learning +1
2 code implementations • 5 Sep 2018 • Quan Chen, Tiezheng Ge, Yanyu Xu, Zhiqiang Zhang, Xinxin Yang, Kun Gai
SHM is the first algorithm that learns to jointly fit both semantic information and high quality details with deep networks.
Ranked #5 on Image Matting on AIM-500
6 code implementations • 21 Apr 2018 • Xiao Ma, Liqin Zhao, Guan Huang, Zhi Wang, Zelin Hu, Xiaoqiang Zhu, Kun Gai
To the best of our knowledge, this is the first public dataset which contains samples with sequential dependence of click and conversion labels for CVR modeling.
no code implementations • 27 Feb 2018 • Junqi Jin, Chengru Song, Han Li, Kun Gai, Jun Wang, Wei-Nan Zhang
Real-time advertising allows advertisers to bid for each impression for a visiting user.
no code implementations • 23 Feb 2018 • Di Wu, Xiujun Chen, Xun Yang, Hao Wang, Qing Tan, Xiaoxun Zhang, Jian Xu, Kun Gai
Our analysis shows that the immediate reward from environment is misleading under a critical resource constraint.
4 code implementations • 8 Jan 2018 • Han Zhu, Xiang Li, Pengye Zhang, Guozheng Li, Jie He, Han Li, Kun Gai
In systems with large corpus, however, the calculation cost for the learnt model to predict all user-item preferences is tremendous, which makes full corpus retrieval extremely difficult.
no code implementations • 17 Nov 2017 • Tiezheng Ge, Liqin Zhao, Guorui Zhou, Keyu Chen, Shuying Liu, Huimin Yi, Zelin Hu, Bochao Liu, Peng Sun, Haoyu Liu, Pengtao Yi, Sui Huang, Zhiqiang Zhang, Xiaoqiang Zhu, Yu Zhang, Kun Gai
So we propose to model user preference jointly with user behavior ID features and behavior images.
2 code implementations • 14 Aug 2017 • Guorui Zhou, Ying Fan, Runpeng Cui, Weijie Bian, Xiaoqiang Zhu, Kun Gai
Models applied on real time response task, like click-through rate (CTR) prediction model, require high accuracy and rigorous response time.
17 code implementations • 21 Jun 2017 • Guorui Zhou, Chengru Song, Xiaoqiang Zhu, Ying Fan, Han Zhu, Xiao Ma, Yanghui Yan, Junqi Jin, Han Li, Kun Gai
In this way, user features are compressed into a fixed-length representation vector, in regardless of what candidate ads are.
Ranked #1 on Click-Through Rate Prediction on Amazon
3 code implementations • 18 Apr 2017 • Kun Gai, Xiaoqiang Zhu, Han Li, Kai Liu, Zhe Wang
CTR prediction in real-world business is a difficult machine learning problem with large scale nonlinear sparse data.
no code implementations • 27 Feb 2017 • Han Zhu, Junqi Jin, Chang Tan, Fei Pan, Yifan Zeng, Han Li, Kun Gai
Moreover, the platform has to be responsible for the business revenue and user experience.
no code implementations • NeurIPS 2010 • Kun Gai, Guangyun Chen, Chang-Shui Zhang
Experiments show that our method significantly outperforms both SVM with the uniform combination of basis kernels and other state-of-art MKL approaches.