Interaction Design for Recommender Systems
Interaction Design for Recommender Systems
复制标题
推荐系统的交互设计
DOI：
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发表时间：
2002
期刊：
影响因子：
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通讯作者：
R. Sinha
中科院分区：
文献类型：
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作者：
Kirsten Swearingen;R. Sinha
1 kirstens@sims.berkeley.edu，sinha@sims.berkeley.edu摘要推荐系统为用户提供个性化的决策指南，帮助他们做出与个人品味相关的决策。研究主要集中在驱动系统的算法上，很少从用户的角度了解设计问题。我们的研究目标是研究用户与推荐系统的交互，以制定通用的设计准则。我们研究了用户与11个在线推荐系统的交互。我们的研究强调了透明度（系统逻辑的理解），熟悉的建议，并在用户与系统的交互推荐项目的信息的作用。我们的研究结果还表明，有多个成功的推荐系统模型。
1 kirstens@sims.berkeley.edu, sinha@sims.berkeley.edu ABSTRACT Recommender systems act as personalized decision guides for users, aiding them in decision making about matters related to personal taste. Research has focused mostly on the algorithms that drive the system, with little understanding of design issues from the user’s perspective. The goal of our research is to study users’ interactions with recommender systems in order to develop general design guidelines. We have studied users’ interactions with 11 online recommender systems. Our studies have highlighted the role of transparency (understanding of system logic), familiar recommendations, and information about recommended items in the user’s interaction with the system. Our results also indicate that there are multiple models for successful recommender systems.