An energy-efficient mobile recommender system

By Yong Ge, Hui Xiong, Alexander Tuzhilin, Keli Xiao, Marco Gruteser, Michael J. Pazzani. Knowledge Discovery and Data Mining, 2010

371 citations recorded by the source corpus.

Subfields: Information Systems, Transportation, Signal Processing

View Paper: https://www.semanticscholar.org/paper/12a710c108d8f843df1ce3b766ed157cc905e3c2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Systems

1. Yu Zheng
2. Jie Bao
3. Mohamed F. Mokbel
4. Xing Xie
5. Ouri Wolfson
6. Hui Xiong
7. David Wilkie
8. João Mendes-Moreira

In Transportation

1. Yu Zheng
2. Xing Xie
3. Ouri Wolfson
4. Jing Yuan
5. Jie Bao
6. Mohamed F. Mokbel
7. David Wilkie
8. Guangzhong Sun

In Signal Processing

1. Yu Zheng
2. Xing Xie
3. Ouri Wolfson
4. Jing Yuan
5. Jie Bao
6. Guangzhong Sun
7. Mohamed F. Mokbel
8. David Wilkie

Citations in the corpus

• Discovering Urban Functional Zones Using Latent Activity Trajectories (2015, background): “For example, [20] and [19] studied the strategies for improving taxi drivers’ income by analyzing the pickup and drop-off behavior of taxicabs in different locations.”
• Driving with knowledge from the physical world (2011, background): “2 Smart Routing To optimize taxi drivers’ income, literatures [7][19] has proposed route recommendation services for a taxi driver by analyzing fleet trajectories.”
• Predicting Taxi–Passenger Demand Using Streaming Data (2013, background): “8 - to predict and/or characterize the pick-up quantity distribution on a short-term time horizon [8]–[10], [17], [19], [27], [28].”
• T-share: A large-scale dynamic taxi ridesharing service (2013, background): “al [14] suggests a sequence of pickup points for a taxi driver.”
• Discovering regions of different functions in a city using human mobility and POIs (2012, neutral): “[7][6] and Yuan et al.”
• Learning geographical preferences for point-of-interest recommendation (2013, methodology): “For example, previous works [27, 26, 9, 16] applied collaborative filtering (CF) based method to recommend locations and travel packages based on user trajectory data.”
• Urban computing with taxicabs (2011, background): “In other words, a lot of GPS-equipped taxis already exist in major cities around the world, generating huge volumes of trajectories everyday [5][6][15].”
• 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 [94,65,58,102,63] and users’ GPS trajectories [28,15,35,36,121,122,133].”
• Where to find my next passenger (2011, methodology): “[3] present a model to recommend a taxi driver with a sequence of pick-up points so as to maximize a taxi driver’s profit.”
• Visual Exploration of Big Spatio-Temporal Urban Data: A Study of New York City Taxi Trips (2013, background): “Enabling cities to deliver services effectively, • N. Ferreira, J. Poco, J. Freire, and C. Silva are with NYU Poly.”
• T-Finder: A Recommender System for Finding Passengers and Vacant Taxis (2013, methodology): “[6] present a novel model to recommend a taxi driver with a sequence of pick-up points so as to maximize a taxi driver’s profit.”
• Recommender system application developments: A survey (2015, background): “However, mobile data is usually more complex, which is heterogeneous, noisy, requires spatial and temporal auto-correlation, and has validation and generality problems [174].”