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

R-Rec: A rule-based system for contextual suggestion using tag-description similarity