Computing Neighbourhoods with Language Models in a Collaborative Filtering Scenario

Language models represent a successful framework for many Information Retrieval tasks: ad hoc retrieval, pseudo-relevance feedback or expert finding are some examples. We present how language models can compute effectively user or item neighbourhoods in a collaborative filtering scenario (this idea was originally proposed in [14]). The experiments support the applicability of this approach for neighbourhoodbased recommendation surpassing the rest of the baselines. Additionally, the computational cost of this approach is small since language models have been efficiently applied to large-scale retrieval tasks such as web search.