Location-Based Recommendation System Using Bayesian User's Preference Model in Mobile Devices

By Moon-Hee Park, Jin-Hyuk Hong, Sung-Bae Cho. International Conference on Ubiquitous Intelligence and Computing, 2007

434 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Information Systems, Transportation

View Paper: https://www.semanticscholar.org/paper/9a6a4fc09ff9a4beeaf6752d3033c289c427b863

How researchers see this paper

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

In Computer Vision and Pattern Recognition

1. Yu Zheng
2. J. Konstan
3. Xing Xie
4. John Riedl
5. Fernando Ortega
6. Antonio Hernando
7. Jesús Bobadilla
8. Qiang Yang

In Information Systems

1. Yu Zheng
2. Qiang Yang
3. Xing Xie
4. Fernando Ortega
5. Antonio Hernando
6. Jesús Bobadilla
7. John Riedl
8. J. Konstan

In Transportation

1. Yu Zheng
2. Xing Xie
3. Jie Bao
4. Mohamed F. Mokbel
5. Qiang Yang
6. David Wilkie
7. Vincent Wenchen Zheng
8. Wei-Ying Ma

Citations in the corpus

• Dynamics of information diffusion and its applications on complex networks (2016, background): “Particularly, either cellular towers or GPS allows locating of the device,whichprovide rich contextual information on the environment and facilitatemany location based services [33], such as advertising [34], recommendation [35], location-based social networking [34], navigation [36], etc.”
• Recommender Systems (2016, neutral): “A significant amount of recent work has focussed on location-aware recommender systems [64, 108, 343, 447, 464, 645, 649].”
• A new collaborative filtering metric that improves the behavior of recommender systems (2010, methodology): “Among the most widely-used models we have: bayesian classifiers [8], neural networks [18] and fuzzy systems [39].”
• Collaborative Filtering Meets Mobile Recommendation: A User-Centered Approach (2010, background): “…recommending some specific types of locations, such as shops (Takeuchi and Sugimoto 2006) restaurants (Horozov, Narasimhan, and Vasudevan 2006; Park, Hong, and Cho 2007), and hot spots for tourism (Zheng et al. 2009); or only recognizing the user activities from sensor data rather than…”
• Location-based and preference-aware recommendation using sparse geo-social networking data (2012, background): “2 Personalized Location Recommendation Some simple personalized recommendation systems request a user to manually specify her personal interests by categori s (like restaurants and parks) [11, 18], which will be employed to de termine the POIs (around the user) to be shown on a mobile interfa ce.”
• Mining significant semantic locations from GPS data (2010, background): “Several locations recommender systems [19,22] can recommend locations to users based on real-world data.”
• Collaborative location and activity recommendations with GPS history data (2010, background): “In [15], a system considering both users’ preferences and location contexts is shown to recommend restaurants.”
• Time-aware recommender systems: a comprehensive survey and analysis of existing evaluation protocols (2013, background): “In fact, some researchers have recruited users for creating datasets with contextual information, but have performed offline evaluation with the created datasets, as e.g. described in Adomavicius et al. (2005) and Park et al. (2007).”
• Learning travel recommendations from user-generated GPS traces (2011, positive): “Mean­while, another branch of work [Abowd et al. 1997; Park et al. 2007] started involving a user s location history in these systems to provide the user with a more personalized recommendation.”
• A location-based news article recommendation with explicit localized semantic analysis (2013, background): “The popularity of handheld devices with a GPS makes the user location available for various kinds of recommendations [12, 17, 19].”
• Recommender systems: from algorithms to user experience (2012, background): “Park et al. (2007) describe a recommender for a mobile device that uses the user’s location, the current time, and even the weather as contextual information in choosing the best items to recommend.”
• Recommendations in location-based social networks: a survey (2015, methodology): “, [76], to recommend locations based on the location’s categories, user generated tags, etc.”
• A Bayesian Framework for Learning Rule Sets for Interpretable Classification (2017, background): “The closest work to ours is probably that of Park et al. (2007) who also consider Bayesian predictive models for context aware recommender systems to restaurants.”
• Mining interesting locations and travel sequences from GPS trajectories (2009, background): “H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval clustering, retrieval model.”