The School of Information and Library Science at the University of North Carolina at Chapel Hill (UNCSILS) submitted two runs to the Contextual Suggestion Track. Given a geographical context, both our runs (UNCSILS_BASE and UNCSILS_PARAM) scored venues from the same candidate set gathered using the Yelp API. Our baseline run (UNCSILS_BASE) followed a nearest neighbor approach. For a given profile/context pair, the candidate venues were scored using the weighted average rating associated with the venues in the profile. The weighting was implemented based on the cosine similarity between the candidate venue and the profile venue using TF.IDF term weighting. The goal of this approach was to score each candidate venue based on the rating associated with the most similar venues in the profile. Our experimental run (UNCSILS_PARAM) boosted the contribution from the profile venue with the greatest similarity with the candidate venue and rating. The experimental run (UNCSILS_PARAM) outperformed the baseline run (UNCSILS_BASE) by a small, but statistically significant margin.