Semantic Knowledge Discovery for User Profiling for Location-Based Recommender Systems
- https://doi.org/10.2991/hcis.k.210704.001How to use a DOI?
- Location-based recommender system; Foursquare; sentiment analysis; topic modelling; venue recommendation; places of interest
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.
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- 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/).
Cite this article
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 - https://doi.org/10.2991/hcis.k.210704.001 ID - Tao2021 ER -