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Works109 from public data
- Jointly modeling aspects, ratings and sentiments for movie recommendation (JMARS)464
A probabilistic model based on collaborative filtering and topic modeling is proposed that allows it to capture the interest distribution of users and the content distribution for movies; it provides a link between interest and relevance on a per-aspect basis and it allows us to differentiate between positive and negative sentiments on aPer-Aspect basis.
- Multi-level Cross-view Contrastive Learning for Knowledge-aware Recommender System259
This paper proposes a novel multi-level cross-view contrastive learning mechanism, named MCCLK, which comprehensively considers three different graph views for KG-aware recommendation, including global-level structural view, local-level collaborative and semantic views, and a k-Nearest-Neighbor item-item semantic graph construction module is proposed.
- CQArank224
This work proposed Topic Expertise Model (TEM), a novel probabilistic generative model with GMM hybrid, to jointly model topics and expertise by integrating textual content model and link structure analysis, and proposed CQARank to measure user interests and expertise score under different topics.
- FaitCrowd199
Experimental results on two real-world datasets show that FaitCrowd can significantly reduce the error rate of aggregation compared with the state-of-the-art multi-source aggregation approaches due to its ability of learning topical expertise from question content and collected answers.
- AliMe Chat: A Sequence to Sequence and Rerank based Chatbot Engine195
An open-domain chatbot engine that integrates the joint results of Information Retrieval and Sequence to Sequence based generation models and outperforms both IR and generation based models is proposed.
- FashionBERT: Text and Image Matching with Adaptive Loss for Cross-modal Retrieval166
The fashion matching is required to pay much more attention to the fine-grained information in the fashion images and texts, so FashionBERT, which leverages patches as image features, is proposed, which learns high level representations of texts and images.
- Response Ranking with Deep Matching Networks and External Knowledge in Information-seeking Conversation Systems151
This paper proposes a learning framework on the top of deep neural matching networks that leverages external knowledge for response ranking in information-seeking conversation systems and incorporates external knowledge into deep neural models with pseudo-relevance feedback and QA correspondence knowledge distillation.
- Kaleido-BERT: Vision-Language Pre-training on Fashion Domain145
A new vision-language (VL) pre-training model dubbed Kaleido-BERT is presented, which introduces a novel kaleido strategy for fashion cross-modality representations from transformers, and design alignment guided masking to jointly focus more on image-text semantic relations.
- Analyzing and Characterizing User Intent in Information-seeking Conversations142
A new dataset designed for this purpose is introduced and used to analyze information-seeking conversations by user intent distribution, co-occurrence, and flow patterns and finds some highly recurring patterns in user intent during an information- seeking process.
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- IncepText: A New Inception-Text Module with Deformable PSROI Pooling for Multi-Oriented Scene Text Detection124
A novel end-to-end scene text detector IncepText is proposed from an instance-aware segmentation perspective and deformable PSROI pooling is introduced to deal with multi-oriented text detection.
- AliMe Assist: An Intelligent Assistant for Creating an Innovative E-commerce Experience114
In this paper, the AliMe Assist system is demonstrated, the underlying techniques are presented, and the experience in dealing with real-world QA in the E-commerce field is shared.
- User Intent Prediction in Information-seeking Conversations113
This paper extracts features based on the content, structural, and sentiment characteristics of a given utterance, and uses classic machine learning methods to perform user intent prediction, and finds that structural features contribute most to the prediction performance.
- AdaBERT: Task-Adaptive BERT Compression with Differentiable Neural Architecture Search112
A novel compression method that leverages differentiable Neural Architecture Search to automatically compress BERT into task-adaptive small models for specific tasks and incorporates a task-oriented knowledge distillation loss to provide search hints and an efficiency-aware loss as search constraints, which enables a good trade-off between efficiency and effectiveness for task-adaptive BERT compression.
- Modelling Domain Relationships for Transfer Learning on Retrieval-based Question Answering Systems in E-commerce105
This paper proposes a general framework, which can effectively and efficiently adapt the shared knowledge learned from a resource-rich source domain to aresource-poor target domain and proposes an efficient and effective hybrid model by combining a sentence encoding- based method and a sentence interaction-based method as the base model.
- A Hybrid Retrieval-Generation Neural Conversation Model94
This paper proposes a hybrid neural conversation model that combines the merits of both response retrieval and generation methods and shows that the proposed model outperforms both retrieval- based methods and generation-based methods under both automatic evaluation metrics and human evaluation.
- Semantic Analysis and Helpfulness Prediction of Text for Online Product Reviews82
This paper isolates review helpfulness prediction from its outer layer tasks, employ two interpretable semantic features, and use human scoring of helpfulness as ground truth, and results show that the two semantic features can accurately predict helpfulness scores and greatly improve the performance compared with using features previously used.
- Meta-Learning Adversarial Domain Adaptation Network for Few-Shot Text Classification75
A novel meta-learning framework integrated with an adversarial domain adaptation network is proposed, aiming to improve the adaptive ability of the model and generate high-quality text embedding for new classes.
- Meta-KD: A Meta Knowledge Distillation Framework for Language Model Compression across Domains70
This work proposes a Meta-Knowledge Distillation (Meta-KD) framework to build a meta-teacher model that captures transferable knowledge across domains and passes such knowledge to students and demonstrates the effectiveness and superiority of this framework on public multi-domain NLP tasks.
- TransPrompt: Towards an Automatic Transferable Prompting Framework for Few-shot Text Classification66
In TransPrompt, a transferable prompting framework for few-shot learning across similar NLP tasks, a multi-task meta-knowledge acquisition procedure is employed to train a meta-learner that captures cross-task transferable knowledge.
- SMedBERT: A Knowledge-Enhanced Pre-trained Language Model with Structured Semantics for Medical Text Mining61
In SMedBERT, a medical PLM trained on large-scale medical corpora, incorporating deep structured semantic knowledge from neighbours of linked-entity, the mention-neighbour hybrid attention is proposed to learn heterogeneous-entity information, which infuses the semantic representations of entity types into the homogeneous neighbouring entity structure.
- R1-ShareVL: Incentivizing Reasoning Capability of Multimodal Large Language Models via Share-GRPO59
This work proposes Share-GRPO, a novel RL approach that expands the question space for a given question via data transformation techniques, and encourages MLLM to effectively explore diverse reasoning trajectories over the expanded question space and shares the discovered reasoning trajectories across the expanded questions during RL.
- A Minimax Game for Instance based Selective Transfer Learning58
This work proposes a general Minimax Game based model for selective transfer learning that outperforms the competing methods by a large margin and is shown to speed up the training process of the learning task in the target domain than traditional TL methods.
- It Is Not Just What We Say, But How We Say Them: LDA-based Behavior-Topic Model58
Textual information exchanged among users on online social network platforms provides deep understanding into users’ interest and behavioral patterns, which has not yet been well incorporated in the existing topic modeling.
- Artificial Intelligence for Prosthetics: Challenge Solutions55
In the NeurIPS 2018 Artificial Intelligence for Prosthetics challenge, participants were tasked with building a controller for a musculoskeletal model with a goal of matching a given time-varying velocity vector by incorporating expert knowledge and using imitation learning.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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