@article{oai:ipsj.ixsq.nii.ac.jp:00237543,
 author = {片山, 一 and 牛尼, 剛聡 and Hajime, Katayama and Taketoshi, Ushiama},
 issue = {3},
 journal = {情報処理学会論文誌データベース（TOD）},
 month = {Jul},
 note = {本論文では，デートやドライブといった特定の目的のために興味のあるスポット（Point of Interest: POI）を探索する際に，ユーザの目的に適したPOIとその理由を提示する手法を提案する．本手法では，POI に関するレビューを用いて追加事前学習を行った BERT モデルに対して，レビューテキストを用いて POI を予測する自己教師あり学習でファインチューニングを行う．そして，BERT の事前学習の際に用いられるタスクの 1 つである Next Sentence Prediction タスクを用いて推薦対象の POI がクエリに対し適合すると考えられる理由をレビューから抽出する．そして，被験者実験の結果によって提案手法の有効性を示す．, In this study, we propose a method to present POIs suitable for a user's purpose and the reasons why the POI is suitable for that purpose by directly inputting the purpose of searching for a Point of Interest (POI) as a query, such as “a good place to go on a date" or "a good place to drive”. The proposed method uses a pre-trained model, BERT, which has been additionally pre-trained with reviews of POIs, and then fine-tuned with a task that predicts the POIs to which the reviews refer from the review text, for POI recommendation. In the recommendation method, the Next Sentence Prediction task, one of the tasks used in BERT pre-training, is used to extract reasons from the reviews why the POIs to be recommended are likely to be relevant to the query. In this paper, we also verify the effectiveness of the proposed methods by conducting subject experiments for each method.},
 pages = {1--11},
 title = {BERTの自己教師あり学習を用いた説明性を有するPOI推薦手法},
 volume = {17},
 year = {2024}
}