Learning recency based comparative choice towards point-of-interest recommendation

Heterogeneous graph-based joint representation learning for users and POIs in location-based social network

As location-based social network (LBSN) services become increasingly popular, trip recommendation that recommends a sequence of points of interest (POIs) to visit for a user emerges as one of many important applications of LBSNs. Personalized trip recommendation tailors to users’ specific tastes by learning from past check-in behaviors of users and their peers. Finding the optimal trip that maximizes user’s experiences for a given time budget constraint is an NP-hard problem and previous solutions do not consider three practical and important constraints. One constraint is POI availability , where a POI may be only available during a certain time window. Another constraint is uncertain traveling time , where the traveling time between two POIs is uncertain. In addition, the diversity of the POIs included in the trip plays an important role in user’s final adoptions. This work presents efficient solutions to personalized trip recommendation by incorporating these constraints and leveraging them to prune the search space. We evaluated the efficiency and effectiveness of our solutions on real-life LBSN datasets.

With the development of Internet of Things technology, the will to make cities smarter is growing. In the context of smart cities, where people are surrounded by a tremendous number of points of interests (POIs), POI recommendation is of great significance. The massive amount of user check-in data collected by location-based social networks (LBSNs) can help users explore new places via POI recommendations. With rich auxiliary data becomeing available in LBSNs, purely exploiting users' check-in information for POI recommendation is not sufficient. Although several attempts have been done to employ geographical influence to enhance POI recommendation, they simply use a certain distribution function to measure the geographical influence between POIs, which may lead to biased results. To this end, this article proposes a geography-aware inductive matrix completion (GAIMC) approach for personalized POI recommendation. Specifically, the GAIMC model consists of two parts, including geographic feature extraction via a Gaussian mixture model (GMM) and inductive matrix completion for recommendation. The GAIMC model first captures the geographical influence among users and POIs by the GMM, which can mine clustering information with hierarchical structures based on the users' check-in data and POIs' location information. Then, a matrix-completion method called inductive matrix completion, which can incorporate geographical features with the user-POI association metric, is utilized to recommend POIs. The experimental results on two real-world LBSN data sets demonstrate that our proposed model can achieve the best recommendation performance in comparison with the state-of-the-art counterparts.

Point-of-interest (POI) recommendation, which helps mobile users explore new places, has become an important location-based service. Existing approaches for POI recommendation have been mainly focused on exploiting the information about user preferences, social influence, and geographical influence. However, these approaches cannot handle the scenario where users are expecting to have POI recommendation for a specific time period. To this end, in this paper, we propose a unified recommender system, named the 'Where and When to gO' (WWO) recommender system, to integrate the user interests and their evolving sequential preferences with temporal interval assessment. As a result, the WWO system can make recommendations dynamically for a specific time period and the traditional POI recommender system can be treated as the special case of the WWO system by setting this time period long enough. Specifically, to quantify users' sequential preferences, we consider the distributions of the temporal intervals between dependent POIs in the historical check-in sequences. Then, to estimate the distributions with only sparse observations, we develop the low-rank graph construction model, which identifies a set of bi-weighted graph bases so as to learn the static user preferences and the dynamic sequential preferences in a coherent way. Finally, we evaluate the proposed approach using real-world data sets from several location-based social networks (LBSNs). The experimental results show that our method outperforms the state-of-the-art approaches for POI recommendation in terms of various metrics, such as F-measure and NDCG, with a significant margin.

Leveraging graph neural networks for point-of-interest recommendations

Points-of-interest (POIs) recommendation plays a vital role in location-based social networks (LBSNs) by introducing unexplored POIs to consumers and has drawn extensive attention from academia and industry. Existing POI recommender systems usually learn fixed latent vectors to represent both consumers and POIs from historical check-ins and make recommendations under the spatio-temporal constraints. However, we argue that the existing works still suffer from the challenges of explaining consumers' complicated check-in actions. To this end, we first explore the interpretability of recommendations from the POI aspect, i.e., for a specific POI, its function usually changes over time, so representing a POI with a single fixed latent vector is not sufficient to describe the dynamic nature of POIs. Besides, check-in actions to a POI are also affected by the zone where it is located. In other words, the zone's embedding learned from POI distributions, road segments, and historical check-ins could be jointly utilized to enhance POI embeddings. Along this line, we propose a <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</b> ime-z <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</b> ne-space <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</b> OI embedding model ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ToP</b> ), which integrates multi-knowledge graphs and topic model to introduce not only spatio-temporal effects but also sentiment constraints into POI embeddings for strengthening interpretability of recommendation. Specifically, ToP learns multiple latent vectors for a POI in a different period with spatial constraints via knowledge graph learning. To add sentiment constraints, ToP jointly combines these vectors with the zone's representations learned by topic models to make explainable recommendations. ToP considers the time, space, and sentiment of POI in a unified embedding framework, which benefits the POI recommendations. Extensive experiments on real-world Changchun city datasets demonstrate that ToP achieves state-of-the-art performance in terms of common metrics and provides more insights for consumers' POI check-in actions.

Friend recommendation is an important real-world application in Location-based Social Networks (LBSN), helping users discover potential friends and enhance their overall happiness. LBSN mainly comprises two distinct data structures: spatio-temporal data for human mobility and graph data for social networks. These two data structures make it challenging to model the complex relationships between them, which are essential for comprehensively understanding users’ lives. Previous studies have either modeled user trajectories and social networks separately or used classical simple graph-based methods, where a simple edge links only two nodes, failing to capture the multiple relationships inherent in LBSN. Furthermore, most studies have relied on Euclidean space to train their graph models, which could result in significant distortion because of tree-like social network data structure. To address these limitations, we propose a novel heterogeneous LBSN hypergraph that represents user check-in records and continuous trajectories—comprising multiple Points of Interest (POI)—as hyperedges, enabling the representation of complex spatio-temporal relationships. This approach enables us to link multiple nodes of different types by hyperedges and use hyperbolic spaces to create more efficient graph representations. Additionally, we devise a new type-specific attention mechanism for our Heterogeneous Hyperbolic Hypergraph Neural Network (H 3 GNN), which is end-to-end trainable and employs supervised contrastive learning to learn hypergraph node embeddings for the subsequent friend recommendation task with the help of hyperbolic space. Finally, our model H 3 GNN achieves better results than existing methods on six real-world city datasets, and our ablation studies demonstrate the effectiveness of each component. Additionally, our experiments indicate that H 3 GNN requires less data storage and training time compared to previous methods.

New developments in mobile computing, wireless networking and social networking leverage opportunities to create location-based social networks (LBSNs) such as Foursquare, Gowalla, Facebook Place. Pointof-interest (POI) recommendation is a significant task in LBSNs, since it provides recommendations that respect personal preferences and capture social and environmental parameters. Moreover, employing Wireless Sensor Networks (WSNs) in POIs enables to improve recommendation accuracy. Most of existing POI recommendation methods deals with the only single domain, whereas the use of information available from other application domains potentially provides better POI recommendation results. This is particularly relevant for the newly launched LBSNs, where little check in information is available. In this paper, to the best of our knowledge, we are the first to propose a cross domain POI recommendation method in LBSNs and apply transfer learning for this purpose. To this end, we first incorporate both user and POI knowledge in auxiliary domains by discovering the principle coordinates. Next, we transfer them to the target domain and then make predictions using the transferred knowledge. The experimental results demonstrate high performance of our method and the merits of cross-domain POI recommendation in LBSNs.

SEM-PPA: A semantical pattern and preference-aware service mining method for personalized point of interest recommendation

The rising prosperity of Location-based Social Networks (LBSNs) witnessed an explosion in the availability of geo-tagged social media data, which enables tremendous location-aware online services, especially next point of interest (POI) recommendation. However, previous next POI recommendation studies usually adopt fix-length time windows for user check-in sequence modeling, leading to a limited capacity in capturing fine-grained user temporal preferences that easily change over time. Besides, existing methods often directly leverage multi-modal contexts as auxiliary to alleviate the data sparsity issue, which fails to fully exploit the sequential patterns of contextual information for inferring user interest drift. To address the above challenges, we propose a novel framework named iTourSPOT which extends traditional collaborative filtering methods with a context-aware POI embedding architecture. For enhancing temporal interests modeling capacity, we associate the context feature extraction with varying-length sessions and incorporate check-in frequencies of POIs as prior knowledge to instruct the session representation learning of our model. Moreover, a collaborative sequence transduction model is designed for joint context sequence modeling and session-based POI recommendation. Experimental results on a real-world geo-tagged photo dataset clearly demonstrate the effectiveness of the proposed framework when compared with state-of-the-art baseline methods, especially in both sparse and cold-start scenarios.

The point-of-interest (POI) recommender exploits check-in information from location-based social networks (LBSNs) to recommend POIs that match user preferences. The preferences of users vary with region/locality, consumption type, and co-consumers. For instance, the places preferred for friends may be different from the places preferred for family, and the places preferred in one locality may be different from the places in another locality. These dynamic preferences can be crucial for an efficient recommender system. A locality consists of different preference trends, such as “known for food and recreational sites.” In real-world, different sets of users might be attracted toward different preference trends, and some users' preferences might overlap across multiple preference sets. Hence, an efficient aggregation of locality preferences is essential for the recommender systems. Many existing studies simply group items by their category and use simple collaborative filtering (CF) for recommendations, however, such techniques cannot efficiently handle the aggregated locality preferences. We propose a hierarchical recommendation model termed Hierarchical Contextual Location Recommendation System (HiRecS) that formulates users' preferences as a hierarchical structure and models the locality trend using aggregated hierarchy. For a locality, the root of hierarchy contains preferred k items from a set of visitors, and the subsequent levels contain preference wise subsets of those items. We also present a hierarchy aggregation technique to aggregate the hierarchical preferences from a similar set of users. The aggregated hierarchy is then contextually exploited for POI sequence recommendation. The core contributions of this article are: 1) it formulates the locality trends as hierarchical structures, presents a hierarchy aggregation technique, and models the personalized POI preferences with the aggregated hierarchy; 2) it exploits the aggregated popular trends to generate contextual POI sequence recommendation; and 3) it extensively evaluates the proposed model with two real-world data sets and demonstrates a significant performance gain of 0.006-5.91 on diversity metrics, 0.0349-17.51 on displacement metrics, and 0.114-0.289 on normalized discounted cumulative gain (NDCG) metrics, when compared to several baselines and relevant studies.

The task of points-of-interest (POI) recommendations has become an essential feature in location-based social networks (LBSNs) with the significant growth of shared data on LBSNs. However it remains a challenging problem, because the decision process of a user choosing to visit a POI depends on numerous factors. The high level of sparsity of the data in LBSNs makes the POI recommendation problem even more challenging, especially for large geographical areas and worldwide datasets. Moreover, in this context the mobility behavior of the users is very heterogeneous, ranging from urban to worldwide mobility. In this paper, we explore the impact of spatial clustering on the recommendation quality. The proposed approach combines spatial clustering with users' influences. It is based on a Poisson factorization model built on an implicit social network, inferred from the geographical mobility patterns. We conduct a comprehensive performance evaluation of our approach on the YFCC dataset (a very large-scale real-world dataset). The experiments show that our approach achieves a significantly superior recommendation quality compared to other state-of-the-art recommendation techniques.

With the development of location-based social networks (LBSNs), users can check in Point-of-Interest (POIs) at any time. However, users do not check in all places they have visited, so the POI trajectory sequence generated through LBSNs is incomplete. An incomplete POI trajectory will have a negative impact on subsequent tasks such as POI recommendation and next POI prediction. Therefore, we complete the missing POI in the user trajectory sequence. Since the POI trajectory sequence is incomplete, it is a challenge to use the pre-order and post-order trajectory sequences with missing POIs. Therefore, we propose a masked POI trajectory model (MPTM) that uses the bidirectionality of BERT to complete the missing POIs in user’s behavior sequence. By masking the missing POIs, MPTM fully explores the relationship between the missing POIs and the known POIs to predict the missing POIs. In order to strengthen the relationship between POIs in the user trajectory sequence, we build a graph for each user’s incomplete POIs sequence to explore the user’s hidden behavior habits. Besides, we design experiments to explore the relationship between the continuity of the number of missing POIs and the predictive ability of the model. The experimental results demonstrate that our MPTM outperforms the state-of-the-art models for completion on missing POIs of user’s behavior sequence.

Location-based social networks (LBSNs), such as Gowalla, Facebook, Foursquare, Brightkite, and so on, have attracted millions of users to share their social friendship and their locations via check-ins in the past few years. Plenty of valuable information is accumulated based on the check-in behaviors, which makes it possible to learn users’ moving patterns as well as their preferences. In LBSNs, point-of-interest (POI) recommendation is one of the most significant tasks because it can help targeted users explore their surroundings as well as help third-party developers provide personalized services. Matrix factorization is a promising method for this task because it can capture users’ preferences to locations and is widely adopted in traditional recommender systems such as movie recommendation. However, the sparsity of the check-in data makes it difficult to capture users’ preferences accurately. Geographical influence can help alleviate this problem and have a large impact on the final recommendation result. By studying users’ moving patterns, we find that users tend to check in around several centers and different users have different numbers of centers. Based on this, we propose a Multi-center Gaussian Model (MGM) to capture this pattern via modeling the probability of a user’s check-in on a location. Moreover, users are usually more interested in the top 20 or even top 10 recommended POIs, which makes personalized ranking important in this task. From previous work, directly optimizing for pairwise ranking like Bayesian Personalized Ranking (BPR) achieves better performance in the top- k recommendation than directly using matrix matrix factorization that aims to minimize the point-wise rating error. To consider users’ preferences, geographical influence and personalized ranking, we propose a unified POI recommendation framework, which unifies all of them together. Specifically, we first fuse MGM with matrix factorization methods and further with BPR using two different approaches. We conduct experiments on Gowalla and Foursquare datasets, which are two large-scale real-world LBSN datasets publicly available online. The results on both datasets show that our unified POI recommendation framework can produce better performance.

In recent years, researches on recommendation of urban Points-Of-Interest (POI), such as restaurants, based on social information have attracted a lot of attention. Although a number of social-based recommendation techniques have been proposed in the literature, most of their concepts are only based on the individual or friends' check-in behaviors. It leads to that the recommended POIs list is usually constrained within the users' or friends' living area. Furthermore, since context-aware and environmental information changes quickly, especially in urban areas, how to extract appropriate features from such kind of heterogeneous data to facilitate the recommendation is also a critical and challenging issue. In this paper, we propose a novel approach named Urban POI-Mine (UPOI-Mine) that integrates location-based social networks (LBSNs) for recommending users urban POIs based on the user preferences and location properties simultaneously. The core idea of UPOI-Mine is to build a regression-tree-based predictor in the normalized check-in space, so as to support the prediction of interestingness of POI related to each user's preference. Based on the LBSN data, we extract the features of places in terms of i) Social Factor, ii) Individual Preference, and iii) POI Popularity for model building. To our best knowledge, this is the first work on urban POI recommendation that considers social factor, individual preference and POI popularity in LBSN data, simultaneously. Through comprehensive experimental evaluations on a real dataset from Gowalla, the proposed UPOI-Mine is shown to deliver excellent performance.