A Case Study on Recommender Systems in Online Conferences: Behavioral Analysis through A/B Testing
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- OKOSO Ayano
- Toyota Central R&D Labs., Inc.
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- OTAKI Keisuke
- Toyota Central R&D Labs., Inc.
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- ISHII Yoshinao
- Toyota Central R&D Labs., Inc.
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- KOIDE Satoshi
- Toyota Central R&D Labs., Inc.
説明
<p>Owing to the COVID-19 pandemic, many academic conferences are now being held online. Our study focuses on online video conferences, where participants can watch pre-recorded embedded videos on a conference website. In online video conferences, participants must efficiently find videos that match their interests among many candidates. There are few opportunities to encounter videos that they may not have planned to watch but may be of interest to them unless participants actively visit the conference. To alleviate these problems, the introduction of a recommender system seems promising. In this paper, we implemented typical recommender systems for the online video conference with 4,000 participants and analyzed users' behavior through A/B testing. Our results showed that users receiving recommendations based on collaborative filtering had a higher continuous video-viewing rate and spent longer on the website than those without recommendations. In addition, these users were exposed to broader videos and tended to view more from categories that are usually less likely to view together. Furthermore, the impact of the recommender system was most significant among users who spent less time on the site.</p>
収録刊行物
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- IEICE Transactions on Information and Systems
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IEICE Transactions on Information and Systems E107.D (5), 650-658, 2024-05-01
一般社団法人 電子情報通信学会
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詳細情報 詳細情報について
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- CRID
- 1390581468909837312
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- ISSN
- 17451361
- 09168532
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- Crossref
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- 抄録ライセンスフラグ
- 使用不可