Search Results for author: Wenwu Ou

Found 31 papers, 11 papers with code

From Known to Unknown: Knowledge-guided Transformer for Time-Series Sales Forecasting in Alibaba

no code implementations17 Sep 2021 Xinyuan Qi, Kai Hou, Tong Liu, Zhongzhong Yu, Sihao Hu, Wenwu Ou

Except for introducing future knowledge for prediction, we propose Aliformer based on the bidirectional Transformer, which can utilize the historical information, current factor, and future knowledge to predict future sales.

Time Series Time Series Forecasting

End-to-End User Behavior Retrieval in Click-Through RatePrediction Model

no code implementations10 Aug 2021 Qiwei Chen, Changhua Pei, Shanshan Lv, Chao Li, Junfeng Ge, Wenwu Ou

Recently, researchers have found that the performance of CTR model can be improved greatly by taking user behavior sequence into consideration, especially long-term user behavior sequence.

Click-Through Rate Prediction Recommendation Systems

GRN: Generative Rerank Network for Context-wise Recommendation

no code implementations2 Apr 2021 Yufei Feng, Binbin Hu, Yu Gong, Fei Sun, Qingwen Liu, Wenwu Ou

Specifically, we first design the evaluator, which applies Bi-LSTM and self-attention mechanism to model the contextual information in the labeled final ranking list and predict the interaction probability of each item more precisely.

Recommendation Systems

Explore User Neighborhood for Real-time E-commerce Recommendation

no code implementations28 Feb 2021 Xu Xie, Fei Sun, Xiaoyong Yang, Zhao Yang, Jinyang Gao, Wenwu Ou, Bin Cui

On the one hand, it utilizes UI relations and user neighborhood to capture both global and local information.

Collaborative Filtering Recommendation Systems

Revisit Recommender System in the Permutation Prospective

no code implementations24 Feb 2021 Yufei Feng, Yu Gong, Fei Sun, Junfeng Ge, Wenwu Ou

Afterwards, for the candidate list set, the PRank stage provides a unified permutation-wise ranking criterion named LR metric, which is calculated by the rating scores of elaborately designed permutation-wise model DPWN.

Recommendation Systems Re-Ranking

Towards Long-term Fairness in Recommendation

1 code implementation10 Jan 2021 Yingqiang Ge, Shuchang Liu, Ruoyuan Gao, Yikun Xian, Yunqi Li, Xiangyu Zhao, Changhua Pei, Fei Sun, Junfeng Ge, Wenwu Ou, Yongfeng Zhang

We focus on the fairness of exposure of items in different groups, while the division of the groups is based on item popularity, which dynamically changes over time in the recommendation process.

Fairness Recommendation Systems

Safe Coupled Deep Q-Learning for Recommendation Systems

no code implementations8 Jan 2021 Runsheng Yu, Yu Gong, Rundong Wang, Bo An, Qingwen Liu, Wenwu Ou

Firstly, we introduce a novel training scheme with two value functions to maximize the accumulated long-term reward under the safety constraint.

Q-Learning Recommendation Systems

Personalized Adaptive Meta Learning for Cold-start User Preference Prediction

no code implementations22 Dec 2020 Runsheng Yu, Yu Gong, Xu He, Bo An, Yu Zhu, Qingwen Liu, Wenwu Ou

Recently, many existing studies regard the cold-start personalized preference prediction as a few-shot learning problem, where each user is the task and recommended items are the classes, and the gradient-based meta learning method (MAML) is leveraged to address this challenge.

Few-Shot Learning

Learning User Representations with Hypercuboids for Recommender Systems

3 code implementations11 Nov 2020 Shuai Zhang, Huoyu Liu, Aston Zhang, Yue Hu, Ce Zhang, Yumeng Li, Tanchao Zhu, Shaojian He, Wenwu Ou

Furthermore, we present two variants of hypercuboids to enhance the capability in capturing the diversities of user interests.

Collaborative Filtering Recommendation Systems

Commonsense knowledge adversarial dataset that challenges ELECTRA

no code implementations25 Oct 2020 Gongqi Lin, Yuan Miao, Xiaoyong Yang, Wenwu Ou, Lizhen Cui, Wei Guo, Chunyan Miao

To investigate machine comprehension models' ability in handling the commonsense knowledge, we created a Question and Answer Dataset with common knowledge of Synonyms (QADS).

Reading Comprehension Word Sense Disambiguation

MTBRN: Multiplex Target-Behavior Relation Enhanced Network for Click-Through Rate Prediction

no code implementations13 Aug 2020 Yufei Feng, Fuyu Lv, Binbin Hu, Fei Sun, Kun Kuang, Yang Liu, Qingwen Liu, Wenwu Ou

In this paper, we propose a new framework named Multiplex Target-Behavior Relation enhanced Network (MTBRN) to leverage multiplex relations between user behaviors and target item to enhance CTR prediction.

Click-Through Rate Prediction Recommendation Systems

Understanding Echo Chambers in E-commerce Recommender Systems

1 code implementation6 Jul 2020 Yingqiang Ge, Shuya Zhao, Honglu Zhou, Changhua Pei, Fei Sun, Wenwu Ou, Yongfeng Zhang

Current research on recommender systems mostly focuses on matching users with proper items based on user interests.

Recommendation Systems

Semi-supervised Collaborative Filtering by Text-enhanced Domain Adaptation

1 code implementation28 Jun 2020 Wenhui Yu, Xiao Lin, Junfeng Ge, Wenwu Ou, Zheng Qin

This causes two difficulties in designing effective algorithms: first, the majority of users only have a few interactions with the system and there is no enough data for learning; second, there are no negative samples in the implicit feedbacks and it is a common practice to perform negative sampling to generate negative samples.

Collaborative Filtering Domain Adaptation +1

ATBRG: Adaptive Target-Behavior Relational Graph Network for Effective Recommendation

no code implementations25 May 2020 Yufei Feng, Binbin Hu, Fuyu Lv, Qingwen Liu, Zhiqiang Zhang, Wenwu Ou

Specifically, to associate the given target item with user behaviors over KG, we propose the graph connect and graph prune techniques to construct adaptive target-behavior relational graph.

Recommendation Systems

Privileged Features Distillation at Taobao Recommendations

no code implementations11 Jul 2019 Chen Xu, Quan Li, Junfeng Ge, Jinyang Gao, Xiaoyong Yang, Changhua Pei, Fei Sun, Jian Wu, Hanxiao Sun, Wenwu Ou

To guarantee the consistency of off-line training and on-line serving, we usually utilize the same features that are both available.

Query-based Interactive Recommendation by Meta-Path and Adapted Attention-GRU

1 code implementation24 Jun 2019 Yu Zhu, Yu Gong, Qingwen Liu, Yingcai Ma, Wenwu Ou, Junxiong Zhu, Beidou Wang, Ziyu Guan, Deng Cai

A novel query-based interactive recommender system is proposed in this paper, where \textbf{personalized questions are accurately generated from millions of automatically constructed questions} in Step 1, and \textbf{the recommendation is ensured to be closely-related to users' feedback} in Step 2.

Recommendation Systems

Compositional Network Embedding

no code implementations17 Apr 2019 Tianshu Lyu, Fei Sun, Peng Jiang, Wenwu Ou, Yan Zhang

Node ID is not generalizable and, thus, the existing methods have to pay great effort in cold-start problem.

Link Prediction Network Embedding +1

Personalized Re-ranking for Recommendation

1 code implementation15 Apr 2019 Changhua Pei, Yi Zhang, Yongfeng Zhang, Fei Sun, Xiao Lin, Hanxiao Sun, Jian Wu, Peng Jiang, Wenwu Ou

Ranking is a core task in recommender systems, which aims at providing an ordered list of items to users.

Recommendation Systems Re-Ranking

BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer

5 code implementations14 Apr 2019 Fei Sun, Jun Liu, Jian Wu, Changhua Pei, Xiao Lin, Wenwu Ou, Peng Jiang

To address this problem, we train the bidirectional model using the Cloze task, predicting the masked items in the sequence by jointly conditioning on their left and right context.

Sequential Recommendation

Value-aware Recommendation based on Reinforced Profit Maximization in E-commerce Systems

no code implementations3 Feb 2019 Changhua Pei, Xinru Yang, Qing Cui, Xiao Lin, Fei Sun, Peng Jiang, Wenwu Ou, Yongfeng Zhang

Existing recommendation algorithms mostly focus on optimizing traditional recommendation measures, such as the accuracy of rating prediction in terms of RMSE or the quality of top-$k$ recommendation lists in terms of precision, recall, MAP, etc.

Recommendation Systems reinforcement-learning

Learning to Collaborate: Multi-Scenario Ranking via Multi-Agent Reinforcement Learning

no code implementations17 Sep 2018 Jun Feng, Heng Li, Minlie Huang, Shichen Liu, Wenwu Ou, Zhirong Wang, Xiaoyan Zhu

The first one is lack of collaboration between scenarios meaning that each strategy maximizes its own objective but ignores the goals of other strategies, leading to a sub-optimal overall performance.

Multi-agent Reinforcement Learning reinforcement-learning

Multi-Source Pointer Network for Product Title Summarization

no code implementations21 Aug 2018 Fei Sun, Peng Jiang, Hanxiao Sun, Changhua Pei, Wenwu Ou, Xiaobo Wang

For the second constraint, we restore the key information by copying words from the knowledge encoder with the help of the soft gating mechanism.

Sentence Summarization

Perceive Your Users in Depth: Learning Universal User Representations from Multiple E-commerce Tasks

no code implementations28 May 2018 Yabo Ni, Dan Ou, Shichen Liu, Xiang Li, Wenwu Ou, An-Xiang Zeng, Luo Si

In this work, we propose to learn universal user representations across multiple tasks for more e ective personalization.

Automatic Generation of Chinese Short Product Titles for Mobile Display

1 code implementation30 Mar 2018 Yu Gong, Xusheng Luo, Kenny Q. Zhu, Wenwu Ou, Zhao Li, Lu Duan

This paper studies the problem of automatically extracting a short title from a manually written longer description of E-commerce products for display on mobile devices.

Extractive Summarization

Cascade Ranking for Operational E-commerce Search

no code implementations7 Jun 2017 Shichen Liu, Fei Xiao, Wenwu Ou, Luo Si

Real-world search applications often involve multiple factors of preferences or constraints with respect to user experience and computational costs such as search accuracy, search latency, size of search results and total CPU cost, while most existing search solutions only address one or two factors; 2).

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