Since the OpenLaws portal is envisioned as an open environment for collaboration between legal professionals, recommendation will eventually become a collaborative filtering problem. This paper addresses the cold start problem for such a portal, where initial recommendations will have to be given, while collaborative filtering data is initially too sparse to produce recommendations. We implemented a hybrid recommendation approach, starting with a latent dirichlet allocation topic model, and progressing to collaborative filtering, and critically evaluated it. Tentative conclusion is that giving recommendations, even bad ones, will influence user selections.
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