S3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization
- Kun Zhou, Haibo Wang, Ji-rong Wen
- Computer ScienceInternational Conference on Information and…
- 18 August 2020
This work proposes the model S3-Rec, which stands for Self-Supervised learning for Sequential Recommendation, based on the self-attentive neural architecture, to utilize the intrinsic data correlation to derive self-supervision signals and enhance the data representations via pre-training methods for improving sequential recommendation.
Counterfactual VQA: A Cause-Effect Look at Language Bias
- Yulei Niu, Kaihua Tang, Hanwang Zhang, Zhiwu Lu, Xiansheng Hua, Ji-rong Wen
- Computer ScienceComputer Vision and Pattern Recognition
- 8 June 2020
A novel counterfactual inference framework is proposed, which enables the language bias to be captured as the direct causal effect of questions on answers and reduced by subtracting the direct language effect from the total causal effect.
Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusion
- Kun Zhou, Wayne Xin Zhao, Shuqing Bian, Yuanhang Zhou, Ji-rong Wen, Jingsong Yu
- Computer ScienceKnowledge Discovery and Data Mining
- 8 July 2020
This work incorporates both word-oriented and entity-oriented knowledge graphs~(KG) to enhance the data representations in CRSs, and adopts Mutual Information Maximization to align the word-level andentity-level semantic spaces.
RocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-ranking
- Ruiyang Ren, Yingqi Qu, Ji-rong Wen
- Computer ScienceConference on Empirical Methods in Natural…
- 14 October 2021
A novel joint training approach for dense passage retrieval and passage reranking is proposed, where the dynamic listwise distillation is introduced, where a unified listwise training approach is designed for both the retriever and the re-ranker.
Scalable Graph Neural Networks via Bidirectional Propagation
- Ming Chen, Zhewei Wei, Ji-rong Wen
- Computer ScienceNeural Information Processing Systems
- 29 October 2020
GBP is the first method that achieves sub-linear time complexity for both the precomputation and the training phases and can deliver superior performance on a graph with over 60 million nodes and 1.8 billion edges in less than half an hour on a single machine.
Towards Topic-Guided Conversational Recommender System
- Kun Zhou, Yuanhang Zhou, Wayne Xin Zhao, Xiaoke Wang, Ji-rong Wen
- Computer ScienceInternational Conference on Computational…
- 8 October 2020
This paper presents the task of topic-guided conversational recommendation, and proposes an effective approach to this task, and contributes a new CRS dataset named TG-ReDial (Recommendation through Topic-Guided Dialog), which has two major features.
Improving Sequential Recommendation with Knowledge-Enhanced Memory Networks
- Jin Huang, Wayne Xin Zhao, Hongjian Dou, Ji-rong Wen, Edward Y. Chang
- Computer ScienceAnnual International ACM SIGIR Conference on…
- 27 June 2018
This paper proposes a novel knowledge enhanced sequential recommender that integrates the RNN-based networks with Key-Value Memory Network (KV-MN) and incorporates knowledge base information to enhance the semantic representation of KV- MN.
Improving Multi-hop Knowledge Base Question Answering by Learning Intermediate Supervision Signals
- Gaole He, Yunshi Lan, Jing Jiang, Wayne Xin Zhao, Ji-rong Wen
- Computer Science, EducationWeb Search and Data Mining
- 11 January 2021
A novel teacher-student approach for the multi-hop KBQA task, where the teacher network tries to learn intermediate supervision signals for improving the reasoning capacity of the student network, and both forward and backward reasoning are utilized to enhance the learning of intermediate entity distributions.
PAIR: Leveraging Passage-Centric Similarity Relation for Improving Dense Passage Retrieval
This work proposes a novel approach that leverages both query-centric and PAssage-centric sImilarity Relations (called PAIR) for dense passage retrieval that significantly outperforms previous state-of-the-art models on both MSMARCO and Natural Questions datasets.