  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.
