Jia-Dong Zhang

GeoSoCa: Exploiting Geographical, Social and Categorical Correlations for Point-of-Interest Recommendations

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 check-in 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 users 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.

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

Abstract With the rapid growth of location-based social networks (LBSNs), location recommendations play an important role in shaping the life of individuals. Fortunately, a variety of community-contributed data, such as geographical information, social friendships and residence information, enable us to mine users’ reality and infer their preferences on locations. In this paper, we propose an effective and efficient location recommendation framework called CoRe. CoRe achieves three key goals in this work. (1) We model a personalized check-in probability density over the two-dimensional geographic coordinates for each user. (2) We propose an efficient approximation approach to predict the probability of a user visiting a new location using her personalized check-in probability density. (3) We develop a new method to measure the similarity between users based on their social friendship and residence information, and then devise a fusion rule to integrate the geographical influence with the social influence so as to improve the user preference model on location recommendations. Finally, we conduct extensive experiments to evaluate the recommendation accuracy , recommendation efficiency and approximation error of CoRe using two large-scale real data sets collected from two popular LBSNs: Foursquare and Gowalla. Experimental results show that CoRe achieves significantly superior performance compared to other state-of-the-art geo-social recommendation techniques.

Spatiotemporal Sequential Influence Modeling for Location Recommendations: A Gravity-based Approach

Recommending to users personalized locations is an important feature of Location-Based Social Networks (LBSNs), which benefits users who wish to explore new places and businesses to discover potential customers. In LBSNs, social and geographical influences have been intensively used in location recommendations. However, human movement also exhibits spatiotemporal sequential patterns, but only a few current studies consider the spatiotemporal sequential influence of locations on users’ check-in behaviors. In this article, we propose a new gravity model for location recommendations, called LORE, to exploit the spatiotemporal sequential influence on location recommendations. First, LORE extracts sequential patterns from historical check-in location sequences of all users as a Location-Location Transition Graph (L2TG), and utilizes the L2TG to predict the probability of a user visiting a new location through the developed additive Markov chain that considers the effect of all visited locations in the check-in history of the user on the new location. Furthermore, LORE applies our contrived gravity model to weigh the effect of each visited location on the new location derived from the personalized attractive force (i.e., the weight) between the visited location and the new location. The gravity model effectively integrates the spatiotemporal, social, and popularity influences by estimating a power-law distribution based on (i) the spatial distance and temporal difference between two consecutive check-in locations of the same user, (ii) the check-in frequency of social friends, and (iii) the popularity of locations from all users. Finally, we conduct a comprehensive performance evaluation for LORE using three large-scale real-world datasets collected from Foursquare, Gowalla, and Brightkite. Experimental results show that LORE achieves significantly superior location recommendations compared to other state-of-the-art location recommendation techniques.

Point-of-interest recommendations in location-based social networks

Location-based social networks (LBSNs), e.g., Foursquare, Gowalla and Yelp, bridge the physical world with the virtual online world. LBSNs have accumulated plenty of community-contributed data such as social links between users, check-ins of users on points-of-interest (POIs), geographical information and categories of POIs, which reflect the preferences of users to POIs. Recommending users with their preferred POIs benefits people to explore new places and businesses to discover potential customers. This paper aims to recommend personalized POIs for users based on their preferences that are learned from the community-contributed data. To this end, this paper models the social, categorical, geographical, sequential, and temporal influences on the visiting preferences of users to POIs.

REAL: A Reciprocal Protocol for Location Privacy in Wireless Sensor Networks

K-anonymity has been used to protect location privacy for location monitoring services in wireless sensor networks (WSNs), where sensor nodes work together to report k-anonymized aggregate locations to a server. Each k-anonymized aggregate location is a cloaked area that contains at least k persons. However, we identify an attack model to show that overlapping aggregate locations still pose privacy risks because an adversary can infer some overlapping areas with less than k persons that violates the k-anonymity privacy requirement. In this paper, we propose a reciprocal protocol for location privacy (REAL) in WSNs. In REAL, sensor nodes are required to autonomously organize their sensing areas into a set of non-overlapping and highly accurate k-anonymized aggregate locations. To confront the three key challenges in REAL, namely, self-organization, reciprocity property and high accuracy, we design a state transition process, a locking mechanism and a time delay mechanism, respectively. We compare the performance of REAL with current protocols through simulated experiments. The results show that REAL protects location privacy, provides more accurate query answers, and reduces communication and computational costs.

Enabling Kernel-Based Attribute-Aware Matrix Factorization for Rating Prediction

In recommender systems, one key task is to predict the personalized rating of a user to a new item and then return the new items having the top predicted ratings to the user. Recommender systems usually apply collaborative filtering techniques (e.g., matrix factorization) over a sparse user-item rating matrix to make rating prediction. However, the collaborative filtering techniques are severely affected by the data sparsity of the underlying user-item rating matrix and often confront the cold-start problems for new items and users. Since the attributes of items and social links between users become increasingly accessible in the Internet, this paper exploits the rich attributes of items and social links of users to alleviate the rating sparsity effect and tackle the cold-start problems. Specifically, we first propose a Kernel-based Attribute-aware M atrix Factorization model called KAMF to integrate the attribute information of items into matrix factorization. KAMF can discover the nonlinear interactions among attributes, users, and items, which mitigate the rating sparsity effect and deal with the cold-start problem for new items by nature. Further, we extend KAMF to address the cold-start problem for new users by utilizing the social links between users. Finally, we conduct a comprehensive performance evaluation for KAMF using two large-scale real-world data sets recently released in Yelp and MovieLens. Experimental results show that KAMF achieves significantly superior performance against other state-of-the-art rating prediction techniques.

CRATS: An LDA-Based Model for Jointly Mining Latent Communities, Regions, Activities, Topics, and Sentiments from Geosocial Network Data

Geosocial networks like Yelp and Foursquare have been rapidly growing and accumulating plenty of data such as social links between users, user check-ins to venues, venue geographical locations, venue categories, and user textual comments on venues. These data contain rich knowledge on the users social interactions in communities, geographical mobility patterns between regions, categorical preferences on activities, aspect interests in topics, and opinion expressions for sentiments. Such knowledge is essential for two key applications, namely, text sentiment classification and venue recommendations, which will be developed in this paper. To extract the knowledge from the data, the key task is to discover the latent communities, regions, activities, topics, and sentiments of users. However, these latent variables are interdependent, e.g., users in the same community usually travel on nearby regions and share common activities and topics, which renders a big challenge for modeling these latent variables. To tackle this challenge, in this study, we propose an LDA-based model called CRATS that jointly mines the latent Communities, Regions, Activities, Topics, and Sentiments based on the important dependencies among these latent variables. To the best of our knowledge, this is the first study to jointly model these five latent variables. Finally, we conduct a comprehensive performance evaluation for CRATS in different applications, including text sentiment classification and venue recommendations, using three large-scale real-world geosocial network data sets collected from Yelp and Foursquare. Experimental results show that CRATS achieves significantly superior performance against other state-of-the-art techniques.

EventRec: Personalized Event Recommendations for Smart Event-Based Social Networks

In recent years, there has been a tremendous increase in the popularity of event-based social networks which allow social and physical interactions among their members. One major challenge for their members is the difficulty of searching events that meet their preferences from a large number of upcoming events. To tackle this challenge, we propose a personalized event recommendation framework called EventRec that exploits the geographical, social and temporal influences of events on users to generate personalized event recommendations. In EventRec, we model the influence of two-dimensional geographical location of an event using the Kernel Density Estimation method along with the popularity of the event location. Furthermore, the social influence in EventRec does not rely only on the relevance of a group to a user, but it also considers the relevance of the group to her friends. The geographical and social influences are integrated with the temporal influence that considers the preferences of the user and her friends on the days of the week and time of events. Our performance evaluation is conducted using two large Meetup.com data sets, and experimental results show that the quality of recommendations of EventRec outperforms the state-of-the-art event recommendation techniques.

SoCaST: Exploiting Social, Categorical and Spatio-Temporal Preferences for Personalized Event Recommendations

In event-based social networks, an event recommender helps users to discover events that align with their preferences from a large number of upcoming events. In this paper, we propose a personalized event recommender called SoCaST based on the geographical, categorical, social and temporal influences of events on users to provide event recommendations. SoCaST uses an adaptive Kernel Density Estimation (KDE) to model the personalized two-dimensional geographical location. The categorical influence indicates how an event category is relevant to a user and its popularity, while the social influence is modeled as the relevance of a group to a user and her friends. Furthermore, geographical, categorical, and social influences are fused with the temporal influence which is modeled through the KDE method to generate event recommendations. Performance evaluation of SoCaST is conducted by using two large-scale Meetup.com data sets. Experimental results show that SoCaST provides better event recommendations than the state-of-the-art recommendation techniques.