ABSTRACT Content-based recommendation techniques usually require a large number of training examples for model construction, which however may not always be available in many real-world scenarios. To address the training data availability constraint common to the content-based approach, we develop a collaborative expansion-based approach to expand the size of training examples, which could lead to improved content-based recommendations. We use a book rating data set collected from Amazon to evaluate our proposed method and compare its performance against those of two salient benchmark techniques. The results show that our method outperforms the benchmark techniques consistently and significantly. Our method expands the size of training examples for a focal customer by leveraging the available preferences of his or her referent group, and thereby better supports personalized recommendations than existing techniques that solely follow content-based or collaborative filtering, without incurring costs to identify, collect, and analyze additional information. This study reveals the value and feasibility of collaborative expansion as a viable means to increase training size for the focal customer and thus address the training data availability constraint that seriously hinders the performance of content-based recommender systems.

中文翻译：

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小线索告诉：有效的基于内容的推荐的协作扩展方法

摘要 基于内容的推荐技术通常需要大量的训练样本来构建模型，但在许多实际场景中可能并不总是可用。为了解决基于内容的方法常见的训练数据可用性限制，我们开发了一种基于协作扩展的方法来扩展训练示例的大小，这可能会导致改进的基于内容的推荐。我们使用从 Amazon 收集的图书评分数据集来评估我们提出的方法，并将其性能与两种显着的基准技术进行比较。结果表明，我们的方法始终且显着地优于基准技术。我们的方法通过利用他或她的参考群体的可用偏好来扩展针对焦点客户的训练示例的规模，从而比仅遵循基于内容或协同过滤的现有技术更好地支持个性化推荐，而不会产生识别、收集和分析附加信息的成本。本研究揭示了协作扩展的价值和可行性，作为一种可行的手段来增加焦点客户的培训规模，从而解决严重阻碍基于内容的推荐系统性能的培训数据可用性限制。