Mining Business Opportunities from Location-based Social Networks

Zhao, Shenglin; King, Irwin; Lyu, Michael R.; Zeng, Jia; Yuan, Mingxuan

doi:10.1145/3077136.3080712

Cited by 10 publications

(3 citation statements)

References 6 publications

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“…The recommendation technique is employed in business category selection, store location selection, etc. Zhao et al [ 26 ] utilize the data from location-based social networks to recommend new business categories in a partitioned business district, which mines the business opportunities and guides the planners to open new commercial shops in certain categories in a specific district. Some researchers employ the business data to support the business owners in LBSNs, designing the zone recommendation system [ 27 ], business prediction system [ 28 ], and retail allocation system [ 29 ].…”

Section: Related Workmentioning

confidence: 99%

Pedestrian Flow Prediction and Route Recommendation with Business Events

Song

Zheng

et al. 2022

Sensors

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Due to the potential economic benefits, pedestrian flow is considered an essential indication of public spaces. Pedestrian flow prediction is designed to assist operators in making decisions (such as shopping center owners). Operators hold certain events, such as sales promotions, to attract surrounding pedestrians; we refer to this type of event as a business event. Business events attract pedestrian flows, which means business opportunities for the merchants. Moreover, their placement will affect the distributions of the pedestrian flows. However, deciding which route is chosen for a specified event is difficult. To the best of our knowledge, we are the first to consider business events when predicting pedestrian flow. In this paper, we investigate two problems: one is pedestrian flow prediction with business events, and the other is route recommendation for business events. First, we propose an Attraction-Based Matrix Factorization model (ABMF) to efficiently predict the pedestrian flow with business events, which introduces the attraction index of different categories to pedestrians in matrix factorization. Second, we leverage the Skip-gram mode to learn the latent representations and improve the pair-wise ranking loss to a flow-aware-based method (SG-FWARP), which aims to learn events’ latent representations for route recommendation. Compared with other state-of-the-art methods, the experimental results show ABMF can predict pedestrian flow matrix with a similarity of over 0.9 compared with the ground truth, and SG-FWARP can recommend routes for business events with high accuracy.

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Section: Related Workmentioning

confidence: 99%

Pedestrian Flow Prediction and Route Recommendation with Business Events

Song

Zheng

et al. 2022

Sensors

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“…The model used text analysis methods based on data collected from real estate listings located across the USA originally advertised by real estate agents. The problem of finding business opportunities by answering the question of what kind of business to open and where was investigated in [24] by mining data from a location-based social network called Yelp. The solution suggested partitioning a city into different business districts, and then recommending new business categories for specific business districts.…”

Section: B Urban Economic Computingmentioning

confidence: 99%

Venue-Popularity Prediction Using Social Data Participatory Sensing Systems and RNNs

2021

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“…As an information point in a geographic information system, a POI can be a landmark point such as a store, a scenic spot, or a site. In LBSN, users can sign-in on POI to indicate that they have reached this point [2]. Using the user's existing check-in records to recommend their favorite POI has become one of the research contents that has attracted much attention [3].…”

Section: Introductionmentioning

confidence: 99%

POI recommendation method using LSTM-attention in LBSN considering privacy protection

Wang

Lü

2021

Complex Intell. Syst.

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Aiming at the problems of traditional point of interest (POI), such as sparse data, lack of negative feedback, and dynamic and periodic changes of user preferences, a POI recommendation method using deep learning in location-based social networks (LBSN) considering privacy protection is proposed. First, the idea of Embedding is used to quantify the user information, friend relationship, POI information, and so on, so as to obtain the internal relationship of the location. Then, based on the user's history and current POI check-in sequence set, the long- and short-term attention mechanism (LSA) is constructed, and the quantified information is used as the input of LSA to better capture the user's long-term and short-term preferences. Finally, the social network information and semantic information are fitted in different input layers, and the time and geographical location information of user's historical behavior are used to recommend the next POI for users. Gowalla and Brightkite datasets are used to demonstrate the proposed method. The results show that the performance of the proposed method is better than other comparison methods under different sparsity, location sequence length, and embedding length. When the number of iterations is 500, the recommended method tends to be stable, and the accuracy is 0.27. Moreover, the recommendation time of the proposed method is less than 130 ms, which is better than other comparative deep learning methods.

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Mining Business Opportunities from Location-based Social Networks

Cited by 10 publications

References 6 publications

Pedestrian Flow Prediction and Route Recommendation with Business Events

Pedestrian Flow Prediction and Route Recommendation with Business Events

Venue-Popularity Prediction Using Social Data Participatory Sensing Systems and RNNs

POI recommendation method using LSTM-attention in LBSN considering privacy protection

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