Personalized Ranking for Context-Aware Venue Suggestion
Information Retrieval
2017-05-23 v1
Abstract
Making personalized and context-aware suggestions of venues to the users is very crucial in venue recommendation. These suggestions are often based on matching the venues' features with the users' preferences, which can be collected from previously visited locations. In this paper we present a novel user-modeling approach which relies on a set of scoring functions for making personalized suggestions of venues based on venues content and reviews as well as users context. Our experiments, conducted on the dataset of the TREC Contextual Suggestion Track, prove that our methodology outperforms state-of-the-art approaches by a significant margin.
Cite
@article{arxiv.1705.07311,
title = {Personalized Ranking for Context-Aware Venue Suggestion},
author = {Mohammad Aliannejadi and Ida Mele and Fabio Crestani},
journal= {arXiv preprint arXiv:1705.07311},
year = {2017}
}
Comments
The 32nd ACM SIGAPP Symposium On Applied Computing (SAC), Marrakech, Morocco, April 4-6, 2017