Do social explanations work?: studying and modeling the effects of social explanations in recommender systems
Do social explanations work?: studying and modeling the effects of social explanations in recommender systems
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DOI：
10.1145/2488388.2488487
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发表时间：
2013-04
期刊：
影响因子：
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通讯作者：
Amit Sharma;D. Cosley
中科院分区：
文献类型：
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作者：
Amit Sharma;D. Cosley
与社交网络相关联的推荐系统通常使用社交解释(例如《X，Y和2个这样的朋友》)支持推荐。我们提出了一项关于这些社会解释在音乐推荐环境中的效果的研究。我们首先对237名用户进行了一项实验，在实验中，我们展示了不同级别的社交信息的解释，并分析了它们对用户决策的影响。我们区分了两个关键决定：检查推荐的艺术家的可能性，以及基于听了几首歌对艺术家的实际评级。我们发现，虽然解释确实对可能性有一些影响，但可能性和同一艺术家的实际(收听)评级之间几乎没有相关性。基于这些见解，我们提出了一个生成性概率模型，该模型解释了解释和音乐偏好背景信息之间的相互作用，以及这如何导致对艺术家的最终可能性评级。认识到解释的影响，我们讨论了一个通用的推荐框架，该框架除了模拟用户的内在偏好外，还在推荐界面中模拟外部信息元素。
Recommender systems associated with social networks often use social explanations (e.g. "X, Y and 2 friends like this") to support the recommendations. We present a study of the effects of these social explanations in a music recommendation context. We start with an experiment with 237 users, in which we show explanations with varying levels of social information and analyze their effect on users' decisions. We distinguish between two key decisions: the likelihood of checking out the recommended artist, and the actual rating of the artist based on listening to several songs. We find that while the explanations do have some influence on the likelihood, there is little correlation between the likelihood and actual (listening) rating for the same artist. Based on these insights, we present a generative probabilistic model that explains the interplay between explanations and background information on music preferences, and how that leads to a final likelihood rating for an artist. Acknowledging the impact of explanations, we discuss a general recommendation framework that models external informational elements in the recommendation interface, in addition to inherent preferences of users.