Semantic Knowledge Discovery for User Profiling for Location-Based Recommender Systems

DOI

10.2991/hcis.k.210704.001How to use a DOI?

Keywords

Location-based recommender system; Foursquare; sentiment analysis; topic modelling; venue recommendation; places of interest

Abstract

This paper introduces a purposed Location-based Recommender System (LBRS) that combines sentiment analysis and topic modelling techniques to improve user profiling for enhancing recommendations of Points of Interest (POIs). Using additional feature extraction, we built user profiles from a Foursquare dataset to evaluate our model and provide recommendations based on user opinions toward venues. Our combined model performed favourably against the baseline models, with an overall improved accuracy of 0.67. The limitations were the use of one dataset and that user profiles were constructed using predicted emotions extracted as features from review data with topic modelling, rather than literal user emotions. Nevertheless, this provides a step forward in user profile and emotion scoring, contributing further to the development of LBRS in the Tourism domain.

Copyright

© 2021 The Authors. Publishing services by Atlantis Press International B.V.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Xiaohui Tao
AU  - Nischal Sharma
AU  - Patrick Delaney
AU  - Aimin Hu
PY  - 2021
DA  - 2021/07/19
TI  - Semantic Knowledge Discovery for User Profiling for Location-Based Recommender Systems
JO  - Human-Centric Intelligent Systems
SP  - 32
EP  - 42
VL  - 1
IS  - 1-2
SN  - 2667-1336
UR  - https://doi.org/10.2991/hcis.k.210704.001
DO  - 10.2991/hcis.k.210704.001
ID  - Tao2021
ER  -