Explaining Recommendations Using Contexts
Explaining Recommendations Using Contexts
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使用上下文解释建议
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Tomoko Ohkuma
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作者：
Masahiro Sato;Budrul Ahsan;Koki Nagatani;Takashi Sonoda;Qian Zhang;Tomoko Ohkuma
推荐系统支持用户决策，对推荐的解释进一步促进了它们的有用性。以前的解释风格是基于类似的用户、类似的项目、用户的人口统计数据和项目的内容。上下文，例如使用场景和随行人员，没有用于解释，尽管它们影响用户决策。在本文中，我们提出了一种上下文风格的解释方法，呈现适合消费推荐项目的上下文。情境风格解释的预期影响是：1)说服力：识别合适的使用情境激励用户消费物品；2)有用性：设想情境有助于用户做出正确的选择，因为物品的价值取决于情境。我们通过在餐厅推荐设置中基于众包的用户研究来评估上下文风格的说服力和实用性。将上下文风格的解释与人口统计学和内容风格的解释进行比较。我们还将上下文风格和其他解释风格结合起来，证实混合风格提高了解释的说服力和有用性。
Recommender systems support user decision-making, and explanations of recommendations further facilitate their usefulness. Previous explanation styles are based on similar users, similar items, demographics of users, and contents of items. Contexts, such as usage scenarios and accompanying persons, have not been used for explanations, although they influence user decisions. In this paper, we propose a context style explanation method, presenting contexts suitable for consuming recommended items. The expected impacts of context style explanations are 1) persuasiveness: recognition of suitable context for usage motivates users to consume items, and 2) usefulness: envisioning context helps users to make right choices because the values of items depend on contexts. We evaluate context style persuasiveness and usefulness by a crowdsourcing-based user study in a restaurant recommendation setting. The context style explanation is compared to demographic and content style explanations. We also combine context style and other explanation styles, confirming that hybrid styles improve persuasiveness and usefulness of explanation.