Learning travel recommendations from user-generated GPS traces

By Yu Zheng, Xing Xie. 2011

421 citations recorded by the source corpus.

Subfields: Information Systems, Transportation, Signal Processing

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Citations in the corpus

Mobile recommender systems in tourism (2014, background): “Those systems are largely inspired by features often offered by popular social networking platforms, providing access to repositories of user‐generated content [77]; as such, they are designed to support visitors to explore a city as well as share their visit experiences [18],[32],[34],[74],[98].”
A review of urban computing for mobile phone traces: current methods, challenges and opportunities (2013, methodology): “This finer granularity enables us to identify locations of users more accurately and thus to adapt data preprocessing methods that have been previously applied to GPS records [48, 47, 49, 17].”
Methodologies for Cross-Domain Data Fusion: An Overview (2015, background): “Additionally, non-negativity is inherent to the data being considered in many applications, such as location recommendation [2], [88], traffic estimation [53], and processing of audio spectrums.”
Transfer Learning in Collaborative Filtering with Uncertain Ratings (2012, background): “…filtering (Goldberg et al. 1992; Koren 2008; Rendle 2012) as an intelligent component in recommender systems (Linden, Smith, and York 2003; Zheng and Xie 2011) has gained extensive interest in both academia and industry, while most previous works can only make use of point-wise ratings.”
Where You Like to Go Next: Successive Point-of-Interest Recommendation (2013, neutral): “The GPS trajectory data usually consist of small number of users, but dense records [Zheng and Xie, 2011; Cao et al. , 2010].”
Content-Aware Point of Interest Recommendation on Location-Based Social Networks (2015, background): “It was firstly studied on GPS trajectory data (Zheng et al. 2009; Zheng and Xie 2011).”
Trajectory Data Mining (2015, background): “, ∆tn−1 → sn , therefore facilitating a diversity of applications, such as travel recommendations [152] [154], destination prediction [116], taxi recommendation [127][129], and gas consumption estimation[132][133].”
Recommendations in location-based social networks: a survey (2015, background): “A number of sequential location recommender systems have been proposed based on either users’ geo-tagged social media posts [58, 63, 65, 94, 102] and users’ GPS trajectories [15, 28, 35, 36, 121, 122, 133].”
Factorization Machines with libFM (2012, background): “Traditionally, nearest neighbor models have attracted a lot of research in the recommender system community (e.g., [Linden et al. 2003; Sarwar et al. 2001; Zheng and Xie 2011]).”
Recommender systems survey (2013, background): “It is possible to collect travel GPS traces from users and use the database to generate recommendations [249].”
Location-based and preference-aware recommendation using sparse geo-social networking data (2012, methodology): “To address the p roblem of scalability, [26] proposed a location-based CF model using”
Transfer learning in heterogeneous collaborative filtering domains (2013, background): “Examples include query-document in information retrieval, author-word in academic publications, user-community in social network services [59], location-activity in ubiquitous computing [60], and even drug-protein in biomedicine, etc.”
Mobile Crowd Sensing and Computing (2015, background): “For instance, by understanding the location history of different individuals, GeoLife [Zheng et al. 2011] measured the similarity among users, based on which a personalized place recommendation service was provided.”
Fused Matrix Factorization with Geographical and Social Influence in Location-Based Social Networks (2012, background): “One line of research is to solve POI recommendation based on the extracted stay points from GPS trajectory logs of several hundred monitored users (Zheng et al. 2009; 2010a; 2010b; Leung, Lee, and Lee 2011; Zheng and Xie 2011; Cao, Cong, and Jensen 2010).”