Context Aware POI Recommendation using Bipartite Graph

With the swift proliferation of handheld mobile devices, location based social networking (LBSNs) services have gained immense attention allowing users to discover their point of interest (POI). Application of collaborative filtering techniques in POI recommendation becomes challenging due to the sparsity of large user-POI rating matrix. Further, in the context of LBSNs, the spatiotemporal information is pivotal to capture user\u27s real-time preferences. In this work we propose a graph based POI recommendation approach, Context Aware POI with Social Trust (CAST) which integrates the geographical influence of the POIs and the influence of the social connections with the user rankings derived from the weighted bipartite graph. Experiments have been conducted with six state-of-the-art baselines using two real-world LBSN data sets. Findings reveal that user ranking on bipartite graph is a significant contributor to the performance along with social, geographical and spatial influence

Privacy-preserving point-of-interest recommendation based on geographical and social influence

We investigate a privacy-preserving problem for point-of-interest (POI) recommendation system for rapidly growing location-based social networks (LBSNs). The LBSN-based recommendation algorithms usually consider three factors: user similarity, social influence between friends and geographical influence in. The LBSN-based recommendation system first needs to collect relevant information of users and then provide them with potentially interesting contents. However, sensitive information of users may be leaked when the recommendation is provided. In this article, we focus on preventing user’s privacy from disclosure upon geographical location and friend relationship factors. We propose a geographical location privacy-preserving algorithm (GLP) that achieves -privacy and present a friend relationship privacy-preserving algorithm (FRP) through adding Laplacian distributed noise for fusing the user trusts. Subsequently, we integrate the GLP and FRP algorithms into a general recommendation system and build a privacy-preserving recommendation system. The novel system enjoys the privacy guarantee under the metric differential entropy through theoretical analysis. Experimental results demonstrate a good trade-off between privacy and accuracy of the proposed recommendation system

Personalized ranking metric embedding for next new POI recommendation

The rapidly growing of Location-based Social Networks (LBSNs) provides a vast amount of check-in data, which enables many services, e.g., point-of-interest (POI) recommendation. In this paper, we study the next new POI recommendation problem in which new POIs with respect to users' current location are to be recommended. The challenge lies in the difficulty in precisely learning users' sequential information and personalizing the recommendation model. To this end, we resort to the Metric Embedding method for the recommendation, which avoids drawbacks of the Matrix Factorization technique. We propose a personalized ranking metric embedding method (PRME) to model personalized check-in sequences. We further develop a PRME-G model, which integrates sequential information, individual preference, and geographical influence, to improve the recommendation performance. Experiments on two real-world LBSN datasets demonstrate that our new algorithm outperforms the state-of-the-art next POI recommendation methods

Category-Aware Location Embedding for Point-of-Interest Recommendation

Recently, Point of interest (POI) recommendation has gained ever-increasing importance in various Location-Based Social Networks (LBSNs). With the recent advances of neural models, much work has sought to leverage neural networks to learn neural embeddings in a pre-training phase that achieve an improved representation of POIs and consequently a better recommendation. However, previous studies fail to capture crucial information about POIs such as categorical information. In this paper, we propose a novel neural model that generates a POI embedding incorporating sequential and categorical information from POIs. Our model consists of a check-in module and a category module. The check-in module captures the geographical influence of POIs derived from the sequence of users' check-ins, while the category module captures the characteristics of POIs derived from the category information. To validate the efficacy of the model, we experimented with two large-scale LBSN datasets. Our experimental results demonstrate that our approach significantly outperforms state-of-the-art POI recommendation methods.Comment: 4 pages, 1 figure

Joint Geographical and Temporal Modeling based on Matrix Factorization for Point-of-Interest Recommendation

With the popularity of Location-based Social Networks, Point-of-Interest (POI) recommendation has become an important task, which learns the users' preferences and mobility patterns to recommend POIs. Previous studies show that incorporating contextual information such as geographical and temporal influences is necessary to improve POI recommendation by addressing the data sparsity problem. However, existing methods model the geographical influence based on the physical distance between POIs and users, while ignoring the temporal characteristics of such geographical influences. In this paper, we perform a study on the user mobility patterns where we find out that users' check-ins happen around several centers depending on their current temporal state. Next, we propose a spatio-temporal activity-centers algorithm to model users' behavior more accurately. Finally, we demonstrate the effectiveness of our proposed contextual model by incorporating it into the matrix factorization model under two different settings: i) static and ii) temporal. To show the effectiveness of our proposed method, which we refer to as STACP, we conduct experiments on two well-known real-world datasets acquired from Gowalla and Foursquare LBSNs. Experimental results show that the STACP model achieves a statistically significant performance improvement, compared to the state-of-the-art techniques. Also, we demonstrate the effectiveness of capturing geographical and temporal information for modeling users' activity centers and the importance of modeling them jointly.Comment: To be appear in ECIR 202

Joint Geo-Spatial Preference and Pairwise Ranking for Point-of-Interest Recommendation

Recommending users with preferred point-of-interests (POIs) has become an important task for location-based social networks, which facilitates users' urban exploration by helping them filter out unattractive locations. Although the influence of geographical neighborhood has been studied in the rating prediction task (i.e. regression), few work have exploited it to develop a ranking-oriented objective function to improve top-N item recommendations. To solve this task, we conduct a manual inspection on real-world datasets, and find that each individual's traits are likely to cluster around multiple centers. Hence, we propose a co-pairwise ranking model based on the assumption that users prefer to assign higher ranks to the POIs near previously rated ones. The proposed method can learn preference ordering from non-observed rating pairs, and thus can alleviate the sparsity problem of matrix factorization. Evaluation on two publicly available datasets shows that our method performs significantly better than state-of-the-art techniques for the top-N item recommendation task

Accessible POI Recommendation Using Adaptive Aggregation of Binary Ratings

Everyone needs one or more forms of accessibility at some point in life due to age, medical conditions, accidents, etc. People with accessibility needs have the right to accessible services, as well as the right to information about accessibility at various places or Points of Interest (POI). While most popular POI recommendation services do not take accessibility into account, some of them only consider a few specific needs, such as ramp for wheelchair users. However, different users have different accessibility needs regarding the structure of the building, special aid devices, and facilities to be able to independently visit a place. The proposed system focuses on finding the personalized accessibility score for a (user, POI) pair. It can be used with other factors such as historical behavior, social influence, geographical conditions, etc. to recommend accessible places. It uses time decaying aggregate on the crowd-sourced binary rating data to find accurate approximation of current accessibility status for each accessibility criteria. Also, we propose a tunnel-based algorithm to detect the trend of binary stream data to update the rate of decay. This ensures that the calculated aggregate adapts to change in the accessibility status of the place