A Survey on Point-of-Interest Recommendation in Location-based Social Networks

Werneck, Heitor; Silva, Nícollas; Viana, Matheus Carvalho; Mourão, Fernando; Pereira, Adriano C. M.; Rocha, Leonardo

doi:10.1145/3428658.3430970

Cited by 28 publications

(8 citation statements)

References 74 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the other hand, from the scenario-oriented perspective, the rest of this section retrospects the utilization of user's social information [310] and location information [311] as side information in the recommendation, in which case recent years have pioneered the location-based social recommendation [312] thriving for a long period. The reason is that user's online information is usually collected from online social platforms allowing users to express their needs, desires, and attitudes towards items and events by such as tweeting or posting.…”

Section: Recommendation Involving Side Informationmentioning

confidence: 99%

Recommender systems based on graph embedding techniques: A comprehensive review

Deng

2021

Preprint

View full text Add to dashboard Cite

Recommender systems, a pivotal tool to alleviate the information overload problem, aim to predict user's preferred items from millions of candidates by analyzing observed user-item relations. As for tackling the sparsity and cold start problems encountered by recommender systems, uncovering hidden (indirect) user-item relations by employing side information and knowledge to enrich observed information for the recommendation has been proven promising recently; and its performance is largely determined by the scalability of recommendation models in the face of the high complexity and large scale of side information and knowledge. Making great strides towards efficiently utilizing complex and large-scale data, research into graph embedding techniques is a major topic. Equipping recommender systems with graph embedding techniques contributes to outperforming the conventional recommendation implementing directly based on graph topology analysis and has been widely studied these years. This article systematically retrospects graph embedding-based recommendation from embedding techniques for bipartite graphs, general graphs, and knowledge graphs, and proposes a general design pipeline of that. In addition, comparing several representative graph embedding-based recommendation models with the most common-used conventional recommendation models, on simulations, manifests that the conventional models overall outperform the graph embedding-based ones in predicting implicit user-item interactions, revealing the relative weakness of graph embedding-based recommendation in these tasks. To foster future research, this article proposes constructive suggestions on making a trade-off between graph embedding-based recommendation and the conventional recommendation in different tasks as well as some open questions.

show abstract

Section: Recommendation Involving Side Informationmentioning

confidence: 99%

Recommender systems based on graph embedding techniques: A comprehensive review

Deng

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Thus, personalized location-based social networks (LBSNs) and recommendation services emerge and have a rapid development, such as the famous review website Yelp, travel platform Mafengwo, etc. [1]. How to recommend yet-unvisited locations that meet the users' interests has become a popular research topic.…”

Section: Introductionmentioning

confidence: 99%

Resolving Data Sparsity via Aggregating Graph-Based User–App–Location Association for Location Recommendations

Chen

Lian

et al. 2022

Applied Sciences

View full text Add to dashboard Cite

Personalized location recommendations aim to recommend places that users want to visit, which can save their decision-making time in daily life. However, the recommending task faces a serious data sparsity problem because users have only visited a small part of total places in a city. This problem directly leads to the difficulty in learning latent representations of users and locations. In order to tackle the data sparsity problem and make better recommendations, users’ app usage records in different locations are introduced to compensated for both users’ interests and locations’ characteristics in this paper. An attributed graph-based representation model is proposed to dig out user–app–location associations with high-order features aggregated. Extensive experiments prove that better representations of users and locations are obtained by our proposed model, thus it greatly improves location recommendation performances compared with the state-of-art methods. For example, our model achieves 13.20%, 10.1%, and 9.44% higher performance than the state-of-art (SOTA) models in Top3Hitrate, Top3Accuracy, and nDCG3, respectively, in the Telecom dataset. In the TalkingData dataset, our model achieves 9.34%, 13.35%, and 8.56% better performance than the SOTA models in Top2Hitrate, Top2Accuracy, and nDCG2, respectively. Furthermore, numerical results demonstrate that our model can effectively alleviate the data sparsity problem in recommendation systems.

show abstract

“…Points‐of‐interest (POIs), that is, well‐localized landmarks including catering, hospitals, schools, road infrastructures, etc., play an important role in map generation and navigation services (Liu et al, 2020; Lu et al, 2020; Wang et al, 2018). The data quality of POIs in the map database, that is, the accuracy of their location, name, and status (i.e., opening or closure), directly affects the service quality of location‐based applications (Revaud et al, 2019; Werneck et al, 2020). For example, users may decide to visit a restaurant based on the query result of its status from the Internet map, thereby avoiding wasted time if the restaurant has been closed or disappeared.…”

Section: Introductionmentioning

confidence: 99%

Point‐of‐interest detection from Weibo data for map updating

Yang

Gao

Zheng

et al. 2022

Transactions in GIS

View full text Add to dashboard Cite

Points‐of‐interest (POIs) geographic information system data are increasingly important for supporting map generation and navigation services, although updating their semantic and location information still largely depends on manual labor. In this study, we propose a novel method to automatically detect the changes in POIs from Chinese text and check‐in position data provided by the Chinese social media platform, Weibo. The proposed method includes three steps: (1) POI name recognition; (2) location confirmation; (3) and change detection. First, we propose recognizing a POI's name from Weibo text using the improved conditional random field algorithm. Then, we detect the location of each named POI by integrating the text address with the check‐in position. The changes in the detected POIs are recognized by extracting the status words from Weibo text and a three‐level status word database. To verify the effectiveness of the proposed method, we examine Wuhan as a case and detect the changes in the commercial POI using real‐world Weibo data collected from January to September 2020. Based on the validation of three common map platforms, the data provided and the manual field investigation of 55 random samples, the identification accuracies for newly added POIs, the unchanged POIs, and expired POIs are approximately 100, 95.8, and 91.7%, respectively.

show abstract

A Survey on Point-of-Interest Recommendation in Location-based Social Networks

Cited by 28 publications

References 74 publications

Recommender systems based on graph embedding techniques: A comprehensive review

Recommender systems based on graph embedding techniques: A comprehensive review

Resolving Data Sparsity via Aggregating Graph-Based User–App–Location Association for Location Recommendations

Point‐of‐interest detection from Weibo data for map updating

Contact Info

Product

Resources

About