Context-aware neural collaborative filtering : use case of applying context information to deep learning recommendation accuracy = 다양한 Context정보 반영이 딥러닝 상품 추천 정확도에 미치는 영향 use case of applying context information to deep learning recommendation accuracy

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Collaborative filtering is a method widely applied by the recommendation system. The basic idea of collaborative filtering is prediction using similar tastes of customers. Mostly, the collaborative filtering method is implemented in two dimensions, [user * item]. Because of using only two dimensions, there is a limitation in Hit ratio. In order to raise Hit Ratio of Collaborative filtering, adding context information (e.g., time, place, people, etc.) is one of the solutions. In this paper we contribute these things. For adding context information we modified the original deep learning program which was published in neural collaborative filtering thesis. After adding context information, Hit ratio is raised from 38% (2 dimension [user * item]) to 75%. We proposed how to estimate the hit ratio of additional context dimensions. Using our estimation, marketers can calculate the hit ratio before running all the combinations of context information. In traditional marketing, marketers should do Segmentation, targeting and finding proper products with tons of data. But our method perform the similar tasks, so marketers can save time and energy
Advisors
Cho, Daegonresearcher조대곤researcher
Description
한국과학기술원 :정보경영프로그램,
Publisher
한국과학기술원
Issue Date
2018
Identifier
325007
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 정보경영프로그램, 2018.8,[viii, 20 p. :]

Keywords

Recommendation system▼adeep learning▼acontext-aware▼acollaborative filtering; 상품추천▼a딥러닝▼arecommendation▼acontext-aware▼acollaborative filtering

URI
http://hdl.handle.net/10203/267155
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=842751&flag=dissertation
Appears in Collection
MT-Theses_Master(석사논문)
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