DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | 박찬영 | - |
dc.contributor.author | Choi, Seungyoon | - |
dc.contributor.author | 최승윤 | - |
dc.date.accessioned | 2024-07-30T19:31:01Z | - |
dc.date.available | 2024-07-30T19:31:01Z | - |
dc.date.issued | 2024 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=1096683&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/321466 | - |
dc.description | 학위논문(석사) - 한국과학기술원 : 산업및시스템공학과, 2024.2,[iv, 29 p :] | - |
dc.description.abstract | Interest in user modeling has surged within the industry due to its ability to create a low-dimensional representation of users by analyzing their previous behaviors. This approach is sought after for delivering personalized services to users. Earlier endeavors in user modeling primarily concentrate on acquiring task-specific user representations tailored to individual tasks. Recognizing the impracticality of developing task-specific user representations for every task, recent research introduces the concept of a universal user representation-a more generalized user representation applicable across a diverse range of tasks. Despite their effectiveness, existing approaches for learning universal user representations are impractical in real-world applications due to the progression of the task, it neglects to consider the passage of time. In this paper, we propose a novel continual user representation learning method, that facilitates positive knowledge transfer between tasks particularly in scenarios where the distribution of data changes over time. The main idea is to introduce a novel selective forward knowledge transfer module with pseudo-representing strategy that successfully alleviates the long-standing problem of continual learning, i.e., catastrophic forgetting. Moreover, we introduce a selective backward knowledge transfer module, which select a user behavior sequence containing a transformed distribution from past tasks enables the adaptation of past tasks to the current data distribution. Extensive experiments on public real-world datasets demonstrate the superiority and practicality of out model. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | 추천시스템▼a사용자 표현 학습▼a연속 학습 | - |
dc.subject | Recommendation system▼aUser representation learning▼aContinual learning | - |
dc.title | Dynamic time-aware continual user representation learning | - |
dc.title.alternative | 동적 시간 인식을 통한 지속적인 사용자 표현 학습 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :산업및시스템공학과, | - |
dc.contributor.alternativeauthor | Park, ChanYoung | - |
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