DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Myaeng, Sung-Hyon | - |
dc.contributor.advisor | 맹성현 | - |
dc.contributor.author | Kang, Junmo | - |
dc.date.accessioned | 2022-04-27T19:31:57Z | - |
dc.date.available | 2022-04-27T19:31:57Z | - |
dc.date.issued | 2021 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=948438&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/296113 | - |
dc.description | 학위논문(석사) - 한국과학기술원 : 전산학부, 2021.2,[iii, 20 p. :] | - |
dc.description.abstract | Like search, a recommendation task accepts an input query or cue and provides desirable items, often based on a ranking function. Such a ranking approach rarely considers explicit dependency among the recommended items. In this work, we propose a generative approach to tag recommendation, where semantic tags are selected one at a time conditioned on the previously generated tags to model inter-dependency among the generated tags. We apply this tag recommendation approach to an Instagram data set where an array of context feature types (image, location, time, and text) are available for posts. To exploit the inter-dependency among the distinct types of features, we adopt a simple yet effective architecture using self-attention, making deep interactions possible. Empirical results show that our method is significantly superior to not only the usual ranking schemes but also autoregressive models for tag recommendation. They indicate that it is critical to fuse mutually supporting features at an early stage to induce extensive and comprehensive view on inter-context interaction in generating tags in a recurrent feedback loop. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | Generative model▼aHashtag recommendation▼aRanking▼aNatural language processing▼aInformation retrieval | - |
dc.subject | 생성 모델▼a해시태그 추천▼a랭킹▼a자연어 처리▼a정보 검색 | - |
dc.title | (A) sequence-oblivious generation method for context-aware hashtag recommendation | - |
dc.title.alternative | 컨텍스트 기반 해시태그 추천을 위한 순서 비인지 생성 기법 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :전산학부, | - |
dc.contributor.alternativeauthor | 강준모 | - |
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