GeoSoCa

Zhang, Jia-Dong; Chow, Chi-Yin

doi:10.1145/2766462.2767711

Cited by 240 publications

(30 citation statements)

References 32 publications

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“…GeoSoCa (Zhang & Chow, ) and Item‐based Collaborative Filtering (CF) (Sarwar, Karypis, Konstan, & Riedl, ) are implemented as baselines in our experiments.…”

Section: Resultsmentioning

confidence: 99%

Location recommendation by combining geographical, categorical, and social preferences with location popularity

Mao

2019

Proceedings of the Association for Information Science and Tech

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Location recommendation could not only increase location visibility, but free users from information overload dilemma. We propose a novel location recommendation method, which first exploits geographical proximity of locations and check‐in history of users' friends to measure geographical preference and social preference respectively, and innovatively estimates categorical preference by calculating semantic similarity between location tags based on category hierarchy. Then, all of three preferences are integrated, and location popularity is introduced as a coefficient to determine the priority of locations to be recommended. The preliminary result on a publicly available real‐word dataset shows that the proposed method significantly outperforms two state‐of‐the‐art baselines. This study expands the methods of location correlation measurement, and can meet user's personalized information needs.

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“…GeoSoCa (Zhang & Chow, ) and Item‐based Collaborative Filtering (CF) (Sarwar, Karypis, Konstan, & Riedl, ) are implemented as baselines in our experiments.…”

Section: Resultsmentioning

confidence: 99%

Location recommendation by combining geographical, categorical, and social preferences with location popularity

Mao

2019

Proceedings of the Association for Information Science and Tech

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“…For example, in reality, a person usually visits a POI, e.g., museums, and then travel to its nearby POIs, e.g., restaurants and stores. In [54], they consider the geographical correlations between POIs and make the assumption that the nearby POIs have the stronger geographic associations than the POIs that are far from them.…”

Section: 21mentioning

confidence: 99%

“…GeoSoCa [54] is another work that takes the social influence into consideration and generates the highest precision ratio when recommending new locations to user u i (locations have never been visited by user u i ). First, in GeoSoCa, they propose a kernel estimation method with an adaptive bandwidth to determine a personalized check-in distribution of POIs for each user that naturally models the geographical correlations between POIs.…”

Section: 25mentioning

confidence: 99%

Exploring timeliness for accurate recommendation in location-based social networks

Xu¹,

Yang²,

Chu³

2018

Mathematical Foundations of Computing

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An individual's location history in the real world implies his or her interests and behaviors. This paper analyzes and understands the process of Collaborative Filtering (CF) approach, which mines an individual's preference from his/her geographic location histories and recommends locations based on the similarities between the user and others. We find that a CF-based recommendation process can be summarized as a sequence of multiplications between a transition matrix and visited-location matrix. The transition matrix is usually approximated by the user's interest matrix that reflect the similarity among users, regarding to their interest in visiting different locations. The visited-location matrix provides the history of visited locations of all users, which is currently available to the recommendation system. We find that recommendation results will converge if and only if the transition matrix remains unchanged; otherwise, the recommendations will be valid for only a certain period of time. Based on our analysis, a novel location-based accurate recommendation (LAR) method is proposed, which considers the semantic meaning and category information of locations, as well as the timeliness of recommending results, to make accurate recommendations. We evaluated the precision and recall rates of LAR, using a large-scale real-world data set collected from Brightkite. Evaluation results confirm that LAR offers more accurate recommendations, comparing to the state-of-art approaches.

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“…A recent survey can be found in [4]. State of the art models like Fused Matrix Factorization Framework with the Multi-center Gaussian Model (FMFMGM) [7] and GeoSoCa [27] exploit geographical and social information of users. e idea of including the location preferences in the collaborative ltering learning is presented in GeoMF [15].…”

Section: Background and Prior Workmentioning

confidence: 99%

“…As mentioned in [4] and [27], Kernel Density Estimation (KDE) is used in several LBSNs recommender systems. KDE is calculated using the equation…”

Section: Kernel Density Estimationmentioning

confidence: 99%

Where Could We Go?

Ayala-Gómez

Daróczy

Mathioudakis

et al. 2017

Proceedings of the 2017 ACM on Web Science Conference

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Location-Based Social Networks (LBSNs) enable their users to share with their friends the places they go to and whom they go with. Additionally, they provide users with recommendations for Points of Interest (POI) they have not visited before. is functionality is of great importance for users of LBSNs, as it allows them to discover interesting places in populous cities that are not easy to explore. For this reason, previous research has focused on providing recommendations to LBSN users. Nevertheless, while most existing work focuses on recommendations for individual users, techniques to provide recommendations to groups of users are scarce.In this paper, we consider the problem of recommending a list of POIs to a group of users in the areas that the group frequents. Our data consist of activity on Swarm, a social networking app by Foursquare, and our results demonstrate that our proposed Geo-Group-Recommender (GGR), a class of hybrid recommender systems that combine the group geographical preferences using Kernel Density Estimation, category and location features and group check-ins outperform a large number of other recommender systems. Moreover, we nd evidence that user preferences dier both in venue category and in location between individual and group activities. We also show that combining individual recommendations using group aggregation strategies is not as good as building a prole for a group. Our experiments show that (GGR) outperforms the baselines in terms of precision and recall at dierent cutos. CCS CONCEPTS•Information systems →Location based services; Personalization; Recommender systems;Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or aliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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GeoSoCa

Cited by 240 publications

References 32 publications

Location recommendation by combining geographical, categorical, and social preferences with location popularity

Location recommendation by combining geographical, categorical, and social preferences with location popularity

Exploring timeliness for accurate recommendation in location-based social networks

Where Could We Go?

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