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.
@article{arxiv.1707.01238,
title = {R-Rec: A rule-based system for contextual suggestion using tag-description similarity},
author = {Kshitij Singh and Manajit Chakraborty and C. Ravindranath Chowdary},
journal= {arXiv preprint arXiv:1707.01238},
year = {2017}
}