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.

当新用户和新项目以非常快的速度加入系统时，基于协作筛选的推荐系统通常会遇到可伸缩性问题。我们通过采用基于分解的推荐方法来解决此问题。我们使用Voronoi图根据位置对推荐域中的用户进行分区，并在每个分区（单元）中单独执行推荐器算法。由于我们消除了使用整个用户集运行算法的需要，因此大大减少了推荐时间。我们进一步解决了改善居住在Voronoi小区外围区域的用户的推荐质量的问题。我们方法的主要目标是减少建议时间，而又不会大大损害建议的准确性，我们提出的方法正确解决了这一问题。进行的实验结果通过将四个不同大小的公开可用数据集（MovieLens-100K，MovieLens-1M，翻书和TripAdvisor数据集。在MAE，RMSE，精度，召回率和F1指标方面的建议准确性也很不错。