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Learning to recommend similar items from human judgments

机译：学习从人的判断中推荐相似的项目

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摘要

Similar item recommendations—a common feature of many Web sites—point users to other interesting objects given a currently inspected item. A common way of computing such recommendations is to use a similarity function, which expresses how much alike two given objects are. Such similarity functions are usually designed based on the specifics of the given application domain. In this work, we explore how such functions can be learned from human judgments of similarities between objects, using two domains of "quality and taste"—cooking recipe and movie recommendation—as guiding scenarios. In our approach, we first collect a few thousand pairwise similarity assessments with the help of crowdworkers. Using these data, we then train different machine learning models that can be used as similarity functions to compare objects. Offline analyses reveal for both application domains that models that combine different types of item characteristics are the best predictors for human-perceived similarity. To further validate the usefulness of the learned models, we conducted additional user studies. In these studies, we exposed participants to similar item recommendations using a set of models that were trained with different feature subsets. The results showed that the combined models that exhibited the best offline prediction performance led to the highest user-perceived similarity, but also to recommendations that were considered useful by the participants, thus confirming the feasibility of our approach.
机译：相似的项目建议（许多网站的共同功能）将用户指向当前正在检查项目的其他有趣对象。计算此类建议的一种常用方法是使用相似度函数，该函数表示两个给定对象的相似度。通常基于给定应用程序域的细节设计此类相似性功能。在这项工作中，我们探索如何使用两个“质量和口味”域（烹饪食谱和电影推荐）作为指导方案，从人类对物体之间相似性的判断中学习这些功能。在我们的方法中，我们首先在群众工作者的帮助下收集了数千对成对相似性评估。然后，使用这些数据，我们可以训练不同的机器学习模型，这些模型可以用作相似性函数来比较对象。离线分析显示，对于这两个应用程序领域，结合了不同类型项目特征的模型是人类感知相似性的最佳预测指标。为了进一步验证所学模型的实用性，我们进行了其他用户研究。在这些研究中，我们使用一组经过不同特征子集训练的模型，向参与者展示了类似的项目建议。结果表明，表现出最佳脱机预测性能的组合模型导致最高的用户感知相似度，但也导致参与者认为有用的建议，从而证实了我们方法的可行性。

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