Proceedings ArticleDOI
A sentiment-enhanced personalized location recommendation system
Dingqi Yang,Daqing Zhang,Zhiyong Yu,Zhu Wang +3 more
- pp 119-128
TLDR
This research proposes a hybrid user location preference model by combining the preference extracted from check-ins and text-based tips which is processed using sentiment analysis techniques and develops a location based social matrix factorization algorithm that takes both user social influence and venue similarity influence into account in location recommendation.Abstract:
Although online recommendation systems such as recommendation of movies or music have been systematically studied in the past decade, location recommendation in Location Based Social Networks (LBSNs) is not well investigated yet. In LBSNs, users can check in and leave tips commenting on a venue. These two heterogeneous data sources both describe users' preference of venues. However, in current research work, only users' check-in behavior is considered in users' location preference model, users' tips on venues are seldom investigated yet. Moreover, while existing work mainly considers social influence in recommendation, we argue that considering venue similarity can further improve the recommendation performance. In this research, we ameliorate location recommendation by enhancing not only the user location preference model but also recommendation algorithm. First, we propose a hybrid user location preference model by combining the preference extracted from check-ins and text-based tips which are processed using sentiment analysis techniques. Second, we develop a location based social matrix factorization algorithm that takes both user social influence and venue similarity influence into account in location recommendation. Using two datasets extracted from the location based social networks Foursquare, experiment results demonstrate that the proposed hybrid preference model can better characterize user preference by maintaining the preference consistency, and the proposed algorithm outperforms the state-of-the-art methods.read more
Citations
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Proceedings ArticleDOI
GeoMF: joint geographical modeling and matrix factorization for point-of-interest recommendation
TL;DR: The results indicate that weighted matrix factorization is superior to other forms of factorization models and that incorporating the spatial clustering phenomenon in human mobility behavior on the LBSNs into matrixfactorization improves recommendation performance.
Journal ArticleDOI
Modeling User Activity Preference by Leveraging User Spatial Temporal Characteristics in LBSNs
TL;DR: A STAP model is proposed that first models the spatial and temporal activity preference separately, and then uses a principle way to combine them for preference inference, and a context-aware fusion framework is put forward to combine the temporal and spatial activity preference models for preferences inference.
Proceedings ArticleDOI
Exploring temporal effects for location recommendation on location-based social networks
TL;DR: A novel location recommendation framework is introduced, based on the temporal properties of user movement observed from a real-world LBSN dataset, which exhibits the significance of temporal patterns in explaining user behavior, and demonstrates their power to improve location recommendation performance.
Proceedings ArticleDOI
GeoSoCa: Exploiting Geographical, Social and Categorical Correlations for Point-of-Interest Recommendations
Jia-Dong Zhang,Chi-Yin Chow +1 more
TL;DR: A new POI recommendation approach called GeoSoCa is proposed through exploiting geographical correlations, social correlations and categorical correlations among users and POIs to achieve significantly superior recommendation quality compared to other state-of-the-artPOI recommendation techniques.
Proceedings Article
Content-aware point of interest recommendation on location-based social networks
TL;DR: This work models the three types of information available on LB-SNs w.r.t. POI properties, user interests, and sentiment indications under a unified POI recommendation framework with the consideration of their relationship to check-in actions, and demonstrates the significance of content information in explaining user behavior.
References
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