CoRe: Exploiting the personalized influence of two-dimensional geographic coordinates for location recommendations

Chen

Liu

2018

IJGI

Abstract:With the rapid development of remote sensing technology, the quantity and variety of remote sensing images are growing so quickly that proactive and personalized access to data has become an inevitable trend. One of the active approaches is remote sensing image recommendation, which can offer related image products to users according to their preference. Although multiple studies on remote sensing retrieval and recommendation have been performed, most of these studies model the user profiles only from the perspective of spatial area or image features. In this paper, we propose a spatiotemporal recommendation method for remote sensing data based on the probabilistic latent topic model, which is named the Space-Time Periodic Task model (STPT). User retrieval behaviors of remote sensing images are represented as mixtures of latent tasks, which act as links between users and images. Each task is associated with the joint probability distribution of space, time and image characteristics. Meanwhile, the von Mises distribution is introduced to fit the distribution of tasks over time. Then, we adopt Gibbs sampling to learn the random variables and parameters and present the inference algorithm for our model. Experiments show that the proposed STPT model can improve the capability and efficiency of remote sensing image data services.

Section: Recommendation For Spatial Datamentioning

confidence: 99%

A Space-Time Periodic Task Model for Recommendation of Remote Sensing Images

Chen

Liu

2018

IJGI

“…More sophistically, the studies [4,11,15,16,21,24,27] model the distance between two locations visited by the same user as a common distribution for all users, e.g., a power-law distribution or a multi-center Gaussian model. In particular, our previous papers [28,29,30] personalize the geographical influence by modeling a personalized nonparametric distribution for each user.…”

Section: Related Workmentioning

confidence: 99%

Lore

Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

Chow

2014

Self Cite

194

Providing location recommendations becomes an important feature for location-based social networks (LBSNs), since it helps users explore new places and makes LBSNs more prevalent to users. In LBSNs, geographical influence and social influence have been intensively used in location recommendations based on the facts that geographical proximity of locations significantly affects users' check-in behaviors and social friends often have common interests. Although human movement exhibits sequential patterns, most current studies on location recommendations do not consider any sequential influence of locations on users' check-in behaviors. In this paper, we propose a new approach called LORE to exploit sequential influence on location recommendations. First, LORE incrementally mines sequential patterns from location sequences and represents the sequential patterns as a dynamic Location-Location Transition Graph (L 2 TG). LORE then predicts the probability of a user visiting a location by Additive Markov Chain (AMC) with L 2 TG. Finally, LORE fuses sequential influence with geographical influence and social influence into a unified recommendation framework; in particular the geographical influence is modeled as two-dimensional check-in probability distributions rather than one-dimensional distance probability distributions in existing works. We conduct a comprehensive performance evaluation for LORE using two large-scale real data sets collected from Foursquare and Gowalla. Experimental results show that LORE achieves significantly superior location recommendations compared to other state-of-the-art recommendation techniques.

“…Our adaptive kernel estimation method can improve the predictive ability of the estimated checkin distribution for a user, in comparison to the kernel density estimation with a fixed bandwidth [30,31,34,35] and the common distance distribution for all users [9,13,15,22,23,28,29]. (Section 3.2)…”

Section: Introductionmentioning

confidence: 99%

“…Our method is distinct from the current works [2,4,20,21,23,27,30,31,34,35,36] that derive the similarities between users in terms of their social links and then integrate them into the traditional collaborative filtering techniques. (Section 3.3)…”

Section: Introductionmentioning

confidence: 99%

GeoSoCa

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval

Chow

2015

Self Cite

240

Recommending users with their preferred points-of-interest (POIs), e.g., museums and restaurants, has become an important feature for location-based social networks (LBSNs), which benefits people to explore new places and businesses to discover potential customers. However, because users only check in a few POIs in an LBSN, the user-POI checkin interaction is highly sparse, which renders a big challenge for POI recommendations. To tackle this challenge, in this study we propose a new POI recommendation approach called GeoSoCa through exploiting geographical correlations, social correlations and categorical correlations among users and POIs. The geographical, social and categorical correlations can be learned from the historical check-in data of users on POIs and utilized to predict the relevance score of a user to an unvisited POI so as to make recommendations for users. First, in GeoSoCa we 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. Then, GeoSoCa aggregates the check-in frequency or rating of a user's friends on a POI and models the social check-in frequency or rating as a power-law distribution to employ the social correlations between users. Further, GeoSoCa applies the bias of a user on a POI category to weigh the popularity of a POI in the corresponding category and models the weighed popularity as a power-law distribution to leverage the categorical correlations between POIs. Finally, we conduct a comprehensive performance evaluation for GeoSoCa using two large-scale real-world check-in data sets collected from Foursquare and Yelp. Experimental results show that GeoSoCa achieves significantly superior recommendation quality compared to other state-of-the-art POI recommendation techniques.