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Boosting the K-Nearest-Neighborhood based incremental collaborative filtering

机译：促进基于K最近邻的增量协作过滤

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摘要

Recommender systems which can automatically match users with their potential favorites usually rely on Collaborative Filtering (CF). Since in real-world applications the data of historical user behavior are ever growing, it is important to study the incremental CF models which can adapt to this data explosion quickly and flexibly. The rating similarity based K-Nearest-Neighborhood (RS-KNN) is a classical but still popular approach to CF; therefore, to investigate the RS-KNN based incremental CF is significant. However, current incremental RS-KNN (I-KNN) models have the drawbacks of high storage complexity and relatively low prediction accuracy. In this work, we intend to boost the RS-KNN based incremental CF. We focus on two points which are respectively (a) reducing the storage complexity while maintaining the prediction accuracy by employing the generalized Dice coefficients, and (b) improving the prediction accuracy by integrating the similarity support and linear biases as well as implementing the corresponding incremental update. The efficiency of our strategies is supported by the positive results of the experiments conducted on two real datasets.
机译：可以自动将用户与其潜在收藏夹匹配的推荐系统通常依赖于协作过滤（CF）。由于在实际应用中，历史用户行为的数据不断增长，因此研究增量CF模型非常重要，因为增量CF模型可以快速灵活地适应这种数据爆炸。基于等级相似度的K最近邻（RS-KNN）是一种经典但仍很流行的CF方法。因此，研究基于RS-KNN的增量CF具有重要意义。但是，当前的增量式RS-KNN（I-KNN）模型具有存储复杂度高和预测精度相对较低的缺点。在这项工作中，我们打算增强基于RS-KNN的增量CF。我们专注于两点，分别是（a）通过使用广义Dice系数在保持预测精度的同时降低存储复杂性，以及（b）通过集成相似性支持和线性偏差以及实现相应的增量来提高预测精度更新。在两个真实数据集上进行的实验的积极结果支持了我们策略的效率。

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