Memory Based Collaborative Filtering is a widely used approach to provide recommendations. It exploits similarities between ratings across a population of users by forming a weighted vote to predict unobserved ratings. Bespoke solutions are frequently adopted to deal with the problem of high quality recommendations on large data sets. A disadvantage of this approach, however, is the loss of generality and flexibility of the general collaborative filtering systems. In this paper, we have developed a methodology that allows one to build a scalable and effective collaborative filtering system on top of a conventional full-text search engine such as Apache Lucene.

翻译：基于内存的协同过滤是一种广泛使用的推荐方法。它通过利用用户群体间评分的相似性，形成加权投票来预测未观测到的评分。在处理大规模数据集的高质量推荐问题时，通常采用定制化解决方案。然而，这种方法的缺点在于丧失了通用协同过滤系统的普适性与灵活性。本文提出了一种方法，使得能够在传统全文搜索引擎（如Apache Lucene）之上构建可扩展且高效的协同过滤系统。