In this paper we study the challenges and evaluate the effectiveness of data collected from the web for recommendations. We provide experimental results, including a user study, showing that our methods produce good recommendations in realistic applications. We propose a new evaluation metric, that takes into account the difficulty of prediction. We show that the new metric aligns well with the results from a user study. Categories and Subject Descriptors

We present a graph-based model for representing the lexical cohesion of a discourse. In the graph structure, vertices correspond to the content words of a text and edges connecting pairs of words encode how closely the words are related semantically. We show that such a structure can be used to distinguish literal and non-literal usages of multi-word expressions. 1