주요 논문
3
*2026년 기준 최근 7년 이내 논문에 한해 Impact Factor가 표기됩니다.
1
Conference paper
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인용수 10
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2024Proxy-based Item Representation for Attribute and Context-aware Recommendation
Jinseok Seol, Minseok Gang, Sang‐goo Lee, Jaehui Park
Neural network approaches in recommender systems have shown remarkable success by representing a large set of items as a learnable vector embedding table. However, infrequent items may suffer from inadequate training opportunities, making it difficult to learn meaningful representations. We examine that in attribute and context-aware settings, the poorly learned embeddings of infrequent items impair the recommendation accuracy. To address such an issue, we propose a proxy-based item representation that allows each item to be expressed as a weighted sum of learnable proxy embeddings. Here, the proxy weight is determined by the attributes and context of each item and may incorporate bias terms in case of frequent items to further reflect collaborative signals. The proxy-based method calculates the item representations compositionally, ensuring each representation resides inside a well-trained simplex and, thus, acquires guaranteed quality. Additionally, that the proxy embeddings are shared across all items allows the infrequent items to borrow training signals of frequent items in a unified model structure and end-to-end manner. Our proposed method is a plug-and-play model that can replace the item encoding layer of any neural network-based recommendation model, while consistently improving the recommendation performance with much smaller parameter usage. Experiments conducted on real-world recommendation benchmark datasets demonstrate that our proposed model outperforms state-of-the-art models in terms of recommendation accuracy by up to 17% while using only 10% of the parameters.
https://doi.org/10.1145/3616855.3635824
Computer science
Proxy (statistics)
Recommender system
Embedding
Collaborative filtering
Artificial neural network
Benchmark (surveying)
Artificial intelligence
Machine learning
Set (abstract data type)
2
Article
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인용수 36
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2022Retrieval-Augmented Response Generation for Knowledge-Grounded Conversation in the Wild
Yeonchan Ahn, Sang‐goo Lee, Junho Shim, Jaehui Park
IF 3.9 (2022)
IEEE Access
Users on the internet usually have conversations on interesting facts or topics along with diverse knowledge from the web. However, most existing knowledge-grounded conversation models consider only a single document regarding the topic of a conversation. The recently proposed retrieval-augmented models generate a response based on multiple documents; however, they ignore the given topic and use only the local context of the conversation. To this end, we introduce a novel retrieval-augmented response generation model that retrieves an appropriate range of documents relevant to both the topic and local context of a conversation and uses them for generating a knowledge-grounded response. Our model first accepts both topic words extracted from the whole conversation and the tokens before the response to yield multiple representations. It then chooses representations of the first N token and ones of keywords from the conversation and document encoders and compares the two groups of representation from the conversation with those groups of the document, respectively. For training, we introduce a new data-weighting scheme to encourage the model to produce knowledge-grounded responses without ground truth knowledge. Both automatic and human evaluation results with a large-scale dataset show that our models can generate more knowledgeable, diverse, and relevant responses compared to the state-of-the-art models.
https://doi.org/10.1109/access.2022.3228964
Conversation
Computer science
Context (archaeology)
Security token
Information retrieval
Representation (politics)
Natural language processing
Artificial intelligence
The Internet
World Wide Web
3
Article
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인용수 6
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2020Exploiting Text Matching Techniques for Knowledge-Grounded Conversation
Yeonchan Ahn, Sang‐goo Lee, Jaehui Park
IF 3.367 (2020)
IEEE Access
Knowledge-grounded conversation models aim at generating informative responses for the given dialogue context, based on external knowledge. To generate an informative and context-coherent response, it is important to conjugate dialogue context and external knowledge in a balanced manner. However, existing studies have paid less attention to finding appropriate knowledge sentences from external knowledge sources than to generating proper sentences with correct dialogue acts. In this paper, we propose two knowledge selection strategies: 1) Reduce-Match and 2) Match-Reduce and explore several neural knowledge-grounded conversation models based on each strategy. Models based on Reduce-Match strategy first distill the whole dialogue context into a single vector with salient features preserved and then compare this context vector with the representation of knowledge sentences to predict a relevant knowledge sentence. Models based on Match-Reduce strategy first match every turn of the context with knowledge sentences to capture fine-grained interactions and aggregate them while minimizing information loss to predict the knowledge sentence. Experimental results show that conversation models using each of our knowledge selection strategies outperform the competitive baselines not only in terms of knowledge selection accuracy but also in response generation performance. Our best model based on Match-Reduce outperforms the baselines in the comparative studies with the Wizard of Wikipedia dataset. Also, our best model based on Reduce-Match outperforms them with the CMU Document Grounded Conversations dataset.
https://doi.org/10.1109/access.2020.3007893
Conversation
Computer science
Matching (statistics)
Natural language processing
Psychology
Communication