Towards more Effective Interactions between Consumers and Feature-Based Recommendation Agents
Towards more Effective Interactions between Consumers and Feature-Based Recommendation Agents
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通讯作者：
K. Murray;G. Häubl
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作者：
K. Murray;G. Häubl
事实证明，基于个人级别特征的偏好模型为消费者提供个性化产品推荐的软件代理可以促进更好的消费选择，同时大大减少做出这些选择所需的工作量。本文探讨了为什么这些工具尽管很有用，但尚未在市场上得到广泛采用。我们认为，造成这种情况的主要原因是推荐系统的可用性在很大程度上被忽视了——无论是在学术研究还是在实践中——并且我们概述了未来研究的路线图，这可能会导致推荐代理更容易被消费者采用。
Software agents that provide consumers with personalized product recommendations based on individual-level feature-based preference models have been shown to facilitate better consumption choices while dramatically reducing the effort required to make these choices. This article examines why, despite their usefulness, such tools have not yet been widely adopted in the marketplace. We argue that the primary reason for this is that the usability of recommendation systems has been largely neglected – both in academic research and in practice – and we outline a roadmap for future research that might lead to recommendation agents that are more readily adopted by consumers.