  Contextual Suggestion deals with search techniques for complex information
needs that are highly focused on context and user needs. In this paper, we
propose \emph{R-Rec}, a novel rule-based technique to identify and recommend
appropriate points-of-interest to a user given her past preferences. We try to
embody the information that the user shares in the form of rating and tags of
any previous point(s)-of-interest and use it to rank the unrated candidate
suggestions. The ranking function is computed based on the similarity between a
suggestion and the places that the user like and the dissimilarity between the
suggestion and the places disliked by the user. Experiments carried out on
TREC-Contextual Suggestion 2015 dataset reveal the efficacy of our method.
