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Real-time recommendation with locality sensitive hashing

机译：具有当地敏感散列的实时推荐

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摘要

Neighborhood-based collaborative filtering (CF) methods are widely used in recommender systems because they are easy-to-implement and highly effective. One of the significant challenges of these methods is the ability to scale with the increasing amount of data since finding nearest neighbors requires a search over all of the data. Approximate nearest neighbor (ANN) methods eliminate this exhaustive search by only looking at the data points that are likely to be similar. Locality sensitive hashing (LSH) is a well-known technique for ANN search in high dimensional spaces. It is also effective in solving the scalability problem of neighborhood-based CF. In this study, we provide novel improvements to the current LSH based recommender algorithms and make a systematic evaluation of LSH in neighborhood-based CF. Besides, we make extensive experiments on real-life datasets to investigate various parameters of LSH and their effects on multiple metrics used to evaluate recommender systems. Our proposed algorithms have better running time performance than the standard LSH-based applications while preserving the prediction accuracy in reasonable limits. Also, the proposed algorithms have a large positive impact on aggregate diversity which has recently become an important evaluation measure for recommender algorithms.
机译：基于邻域的协作滤波（CF）方法广泛用于推荐系统，因为它们易于实现，非常有效。这些方法的重大挑战之一是能够随着数据量的增加，因为找到最近的邻居需要搜索所有数据。近似最近邻（ANN）方法仅通过查看可能类似的数据点来消除这种详尽的搜索。地区敏感散列（LSH）是一个众所周知的高维空间中搜索的技术。解决基于邻域的CF的可扩展性问题也是有效的。在本研究中，我们为基于LSH的推荐算法提供了新颖的改进，并在基于邻域的CF中对LSH进行了系统的评估。此外，我们对现实生活数据集进行了广泛的实验，以调查LSH的各种参数及其对用于评估推荐系统的多个度量的影响。我们所提出的算法具有比标准LSH的应用程序更好的运行时间性能，同时在合理的限制中保持预测精度。此外，所提出的算法对聚合多样性具有大的积极影响，最近成为推荐算法的重要评估措施。

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