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Scalable recommendations using decomposition techniques based on Voronoi diagrams

机译：使用基于Voronoi图的分解技术可扩展的建议

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摘要

Collaborative filtering based recommender systems typically suffer from scalability issues when new users and items join the system at a very rapid rate. We tackle this concerning issue by employing a decomposition based recommendation approach. We partition the users in the recommendation domain with respect to location using a Voronoi Diagram and execute the recommender algorithm individually in each partition (cell). This results in a much reduced recommendation time as we eliminate the need for running the algorithm using the entire user set. We further address the problem of improving the recommendation quality of the users residing in the peripheral region of a Voronoi cell. The primary objective of our approach is to bring down the recommendation time without compromising the accuracies of recommendations much, which is rightly addressed by our proposed method. The outcomes of the experiments performed demonstrate the scalability as well as efficacy of our method by reducing the runtime of the baseline CF algorithm by at least 65% for each of these four publicly available datasets of varying sizes - MovieLens-100K, MovieLens-1M, Book-Crossing and TripAdvisor datasets. The accuracies of recommendations in terms of MAE, RMSE, Precision, Recall and F1 metrics also hold good.
机译：基于协同过滤的推荐系统通常在新用户和项目以非常快速的速率加入系统时遭受可扩展性问题。我们通过采用基于分解的建议方法来解决这个问题。我们使用Voronoi图对推荐域中的用户分区，并在每个分区（单元）中单独执行推荐人算法。这导致在使用整个用户集中消除运行算法的需求的大大降低的推荐时间。我们进一步解决了提高驻留在Voronoi小区的外围区域中用户的推荐质量的问题。我们方法的主要目标是在不损害建议的准确性范围内降低建议时间，这是由我们提出的方法正确解决的。执行的实验结果证明了通过将基线CF算法的运行时间减少了这四个不同尺寸的四个公共数据集的每个四个公共数据集，Movielens-100k，Movielens-1M，书籍交叉和TripAdvisor数据集。在MAE，RMSE，精确，召回和F1指标方面的建议准确性也良好。

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