Research on comprehensive point of interest (POI) recommendation based on spark

He, Fangcheng; Wei, Pengcheng

doi:10.1007/s10586-018-2061-y

Cited by 8 publications

(7 citation statements)

References 10 publications

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“…13,14 Over the last few years, applying the deep learning methods has been a constant rise in artificial intelligence tasks like natural language processing, computer vision, and POI recommendation, where main traits can be exploited deeply and successfully. [15][16][17][18][19] Deep learning outlines a representation-learning algorithm that is reliable to learn data representations with multiple simple components. Each component investigates high-level representations of input from the former module (from low-level feature extractor module).…”

Section: Introductionmentioning

confidence: 99%

“…Doan et al 6 have employed a novel demonstrating location visit behavior (user check‐in) using emphasizing area attraction and neighborhood competition. He et al 15 have designed a POI recommendation structure to integrate factors, time factors, geographical and social factors. Their strategy is used the linear weighting and cascading combination.…”

Section: Introductionmentioning

confidence: 99%

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DeePOF: A hybrid approach of deep convolutional neural network and friendship to Point‐of‐Interest (POI) recommendation system in location‐based social networks

Safavi

Jalali

2022

Concurrency and Computation

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Today, millions of active users spend a percentage of their time on location-based social networks like Yelp and Gowalla and share their rich information. They can easily learn about their friends' behaviors and where they are visiting and be influenced by their style. As a result, the existence of personalized recommendations and the investigation of meaningful features of users and Point of Interests (POIs), given the challenges of rich contents and data sparsity, is a substantial task to accurately recommend the POIs and interests of users in location-based social networks (LBSNs). This work proposes a novel pipeline of POI recommendations named DeePOF based on deep learning and the convolutional neural network. This approach only takes into consideration the influence of the most similar pattern of friendship instead of the friendship of all users. The mean-shift clustering technique is used to detect similarity. The most similar friends' spatial and temporal features are fed into our deep CNN technique. The output of several proposed layers can predict latitude and longitude and the ID of subsequent appropriate places, and then using the friendship interval of a similar pattern, the lowest distance venues are chosen. This combination method is estimated on two popular datasets of LBSNs. Experimental results demonstrate that analyzing similar friendships could make recommendations more accurate and the suggested model for recommending a sequence of top-k POIs outperforms state-of-the-art approaches.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

DeePOF: A hybrid approach of deep convolutional neural network and friendship to Point‐of‐Interest (POI) recommendation system in location‐based social networks

Safavi

Jalali

2022

Concurrency and Computation

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“…As a relatively easy-to-access emerging open source data, points of interest (POIs) [24][25][26] can comprehensively, accurately, and meticulously reflect the spatial distribution of various geographic entities in the city and can be obtained relatively easily and objectively from map providers. Thus, they provide a new data source for the spatial characteristics of urban tourism resources.…”

Section: Introductionmentioning

confidence: 99%

A Novel Framework for Exploring the Spatial Characteristics of Leisure Tourism Using Multisource Data: A Case Study of Qingdao, China

Shang

Wen

Bai

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Spatial characteristics of leisure tourism resources is essential for human life, the urban economy, and tourism planning. This paper presents a novel framework to explore these characteristics based on multi-source data, such as points of interest (POIs), OpenStreetMap roads, Sentinel-2 Multispectral Instrument (MSI) images, and other data, and proposes a new tourism area identification method by integrating attractiveness of attractions with term frequency-inverse document frequency. The roles of the influencing factors were measured by using the geodetector and related statistical analyses. The results showed that the resources were centered on Jiaozhou Bay, and their axial direction was "northeast to southwest." The distribution of the overall resources was characterized by "one cluster with multiple core points," and different types of resources had different aggregation distributions. The Recreational Recreation (RR) and Cultural Leisure (CL) zones were more likely to be distributed in and near the center of each district, and their numbers were high, while the Shopping Leisure (SL) and Natural Recreation (NR) zones were the opposite. The distribution of each type of resource was the result of a combination of factors working together, except for NR resources, which were mainly influenced by natural factors, while others were mainly affected by socioeconomic factors. The study findings are instructive for tourism planning.

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“…The recommendation system tracks the interaction information between users and their selected items and then uses this information to process into a user model through recommendation algorithm, which is used to filter out the items that users are interested in and recommend the results to users in the form of personalized list [3]. According to user's needs, interests, etc., create a list of items that users are interested in, without a lot of interaction with users [4]. Recommendation system helps users to solve the problem of too many products and difficult to choose and provides them with personalized services.…”

Section: Introductionmentioning

confidence: 99%

Research on Product Design Strategy Based on User Preference and Machine Learning Intelligent Recommendation

2022

Wireless Communications and Mobile Computing

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In the machine learning model, intelligent recommendation system can select valuable information from a lot of data to help users find the products or services they need, which has been more and more widely used in recent years. However, there are still many problems in machine learning recommender systems, such as data sparsity, natural noise, and cold start, which leads to the fact that machine learning recommender systems cannot obtain accurate user preferences. When a project is rated, the quality of the recommendation is greatly affected. In order to solve the problem that the existing recommendation algorithms have poor recommendation results in sparse data sets, this paper proposes a machine learning method for recommendation rating prediction based on user interest concept lattice. Firstly, the nearest neighbors are divided into direct nearest neighbors and indirect nearest neighbors by user interest concept lattice. Then, different methods are used to calculate the similarity between the direct “nearest neighbor” and the target user, and the similarity between the indirect “nearest neighbor” and the target user. Finally, the invisible item score of the target user is calculated by the similarity value. Experiments are carried out on real data sets, and the experimental results show that the CFCNN-CL algorithm and RRP-UI CL algorithm proposed in this paper have high recommendation accuracy and still have good performance in the case of sparse data.

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Research on comprehensive point of interest (POI) recommendation based on spark

Cited by 8 publications

References 10 publications

DeePOF: A hybrid approach of deep convolutional neural network and friendship to Point‐of‐Interest (POI) recommendation system in location‐based social networks

DeePOF: A hybrid approach of deep convolutional neural network and friendship to Point‐of‐Interest (POI) recommendation system in location‐based social networks

A Novel Framework for Exploring the Spatial Characteristics of Leisure Tourism Using Multisource Data: A Case Study of Qingdao, China

Research on Product Design Strategy Based on User Preference and Machine Learning Intelligent Recommendation

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