OtherTube: Facilitating Content Discovery and Reflection by Exchanging YouTube Recommendations with Strangers
OtherTube: Facilitating Content Discovery and Reflection by Exchanging YouTube Recommendations with Strangers
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DOI：
10.1145/3491102.3502028
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发表时间：
2022-01
期刊：
影响因子：
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通讯作者：
M. Bhuiyan;Carlos Augusto Bautista Isaza;Tanushree Mitra;Sang Won Lee
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文献类型：
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作者：
M. Bhuiyan;Carlos Augusto Bautista Isaza;Tanushree Mitra;Sang Won Lee
为了提高参与度，YouTube等平台上的推荐算法越来越多地个性化用户的订阅源，限制用户接触多样化的内容，剥夺了他们与其他人相比反思自己兴趣的机会。在这项工作中，我们研究如何与陌生人交换建议可以帮助用户发现新的内容和反映。我们通过开发YouTube浏览器扩展来测试这个想法，它可以显示陌生人的个性化YouTube推荐。YouTube允许用户（i）创建匿名个人资料进行社交比较，（ii）与他人分享他们推荐的视频，以及（iii）浏览陌生人的YouTube推荐。我们进行了为期10天的用户研究（n = 41），然后进行了研究后访谈（n = 11）。我们的研究结果表明，用户发现并开发了新的兴趣，从看到YouTube的建议。我们确定了影响与交换推荐的交互和参与的用户和内容特征;例如，年轻用户与YouTube的交互更多，而一些内容的感知无关性阻碍了用户观看某些视频。用户反映了他们和其他人的兴趣，认识到相似性和差异性。我们的工作显示了利用与陌生人交换个性化推荐的设计的希望。
To promote engagement, recommendation algorithms on platforms like YouTube increasingly personalize users’ feeds, limiting users’ exposure to diverse content and depriving them of opportunities to reflect on their interests compared to others’. In this work, we investigate how exchanging recommendations with strangers can help users discover new content and reflect. We tested this idea by developing OtherTube—a browser extension for YouTube that displays strangers’ personalized YouTube recommendations. OtherTube allows users to (i) create an anonymized profile for social comparison, (ii) share their recommended videos with others, and (iii) browse strangers’ YouTube recommendations. We conducted a 10-day-long user study (n = 41) followed by a post-study interview (n = 11). Our results reveal that users discovered and developed new interests from seeing OtherTube recommendations. We identified user and content characteristics that affect interaction and engagement with exchanged recommendations; for example, younger users interacted more with OtherTube, while the perceived irrelevance of some content discouraged users from watching certain videos. Users reflected on their interests as well as others’, recognizing similarities and differences. Our work shows promise for designs leveraging the exchange of personalized recommendations with strangers.