Location-based and preference-aware recommendation using sparse geo-social networking data

By Jie Bao, Yu Zheng, Mohamed F. Mokbel. SIGSPATIAL/GIS, 2012

717 citations recorded by the source corpus.

Subfields: Information Systems, Transportation, Signal Processing

View Paper: https://www.semanticscholar.org/paper/bddd97d46a2e5a42c0496cea66dafb58dd6040ad

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Citations in the corpus

Urban Computing: Concepts, Methodologies, and Applications (2014, background): “The geo-tagged social media may also re.ect human mobility patterns in urban spaces, therefore enabling some useful applications, such as travel recommendations [Wei et al. 2012; Bao et al. 2012, 2014; Yoon et al. 2010, 2011] and location choosing for a business [Karamshuk et al. 2012].”
Exploiting Geographical Neighborhood Characteristics for Location Recommendation (2014, background): “For instance, social opinions from users’ online social friends [27] or local experts in a new city [2] have been considered for location recommendation.”
Learning geographical preferences for point-of-interest recommendation (2013, background): “More recent works, such as [23, 3], began to explore user preferences, social influence, and geographical influence for POI recommendations.”
A Survey on Trajectory Data Management, Analytics, and Learning (2020, background): “ype Exemplary Datasets #Trajectories Applications Human GPS tracking GeoLife [208] 17,621 Human mobility [196] Urban area Check-in Foursquare [20] Gowalla [44] 104,478 Tourism planning [169] Mobility prediction [64] Online sharing OpenStreetMap [10] 8.7 million Commuting analytic”
GeoSoCa: Exploiting Geographical, Social and Categorical Correlations for Point-of-Interest Recommendations (2015, methodology): “One way is to simply consider the current locations of users to ﬁlter out the POIs that are far from the users [1, 4, 20, 14].”
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.”
Recommender Systems (2016, neutral): “A significant amount of recent work has focussed on location-aware recommender systems [64, 108, 343, 447, 464, 645, 649].”
Trajectory Data Mining (2015, methodology): “[5] apply the similar idea in a collaborative filtering framework to conduct the travel recommendation, concerned with a user’s preferences, social environment and current location.”
Understanding urban human activity and mobility patterns using large-scale location-based data from online social media (2013, background): “formation provided by the location-based data which has already enabled many novel applications such as recommendation system for physical locations (or activity) [1, 2] or recommending potential customers or friend [3, 4]; and determining popular travel routes in a city [5].”
Recommendations in location-based social networks: a survey (2015, methodology): “Foursquare 2 [8] This dataset6 contains 221,128 check-ins generated by 49,062 users in New York City (NYC) and 104,478 check-ins generated by 31,544 users in Los Angeles (LA).”