인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 저자정보
초록·키워드
With the recent popularity of e-commerce websites, analyzing user characteristics from purchase history data has become an important challenge for companies. User2Vec is an effective tool to achieve this task, which is a simple neural network model to describe the characteristics using embeddings. However, User2Vec does not utilize auxiliary information, and the relevance between users and items are not explicitly considered into model learning process. To capture the characteristics more accurately, it is effective to consider auxiliary information, and to evaluate the relevance with items for each user. In this study, we propose a model called Attentive and Auxiliary-Informative User2Vec, which learns both user-specific and item-specific embeddings and auxiliary information embeddings simultaneously and utilizes the attention mechanism to associate users and items. By our method, it has become possible to perform characteristic analysis associating users and items, considering various auxiliary information in addition to purchase history data. Additionally, characteristic analysis that excludes the influence of auxiliary information can be performed. To demonstrate the effectiveness of our method, we perform an evaluation experiment with artificial data assuming purchase history. Furthermore, we apply our method to actual user review rating data for movies and show a case study of user characteristic analysis. Implementations are available at https://github.com/tishii2479/aau2v.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
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UCI(KEPA) : I410-151-25-02-091259941
