Personalized Check-in Prediction Model Based on User’s Dissimilarity and Regression
To solve the problem that the user check-in prediction model is difficult to provide personalized check-in services, this paper proposes a novel hybrid model, called personalized check-in prediction model based on user's dissimilarity and regression (UDR). The UDR is mainly composed of two sub-...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8737940/ |
| _version_ | 1818407551377604608 |
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| author | Chang Su Qiuli Zhou Xianzhong Xie Dezheng Wu |
| author_facet | Chang Su Qiuli Zhou Xianzhong Xie Dezheng Wu |
| author_sort | Chang Su |
| collection | DOAJ |
| description | To solve the problem that the user check-in prediction model is difficult to provide personalized check-in services, this paper proposes a novel hybrid model, called personalized check-in prediction model based on user's dissimilarity and regression (UDR). The UDR is mainly composed of two sub-models: user's regression location prediction model (UR) and user's dissimilarity location prediction model (UD). In UR, considering the personalization of user check-ins, we propose a hybrid weighted Markov model, which combines the whole check-in data and individual check-ins. Different from other methods, for the prediction of individual check-ins, we not only consider the preference of individual users, but also the influence of friend relationships. Meanwhile, the Hidden Markov model(HMM) is used to determine users' next check-in location by using time series feature (week-hour) and location sequence. In addition, by improving the kernel density estimation, we propose a multi-level hybrid kernel density estimation model, which is built based on the individual, city and region layers, and smoothes the over-fitting phenomenon caused by few check-ins. In UD, we take into account the weather factors that most existing methods did not consider. By defining the “cold and hot spot transference” and weather similarity features, we explore the influence of weather on user's check-ins and also propose a method used to calculate the similarity between user check-in weather preferences and location weathers. At the same time, the influence of social, time, and space factors are also considered. The experiments on two LBSN datasets demonstrate that the performance of UDR is superior to the state-of-the-art check-in prediction methods. |
| first_indexed | 2024-12-14T09:29:38Z |
| format | Article |
| id | doaj.art-39e3ab9e01d24e1f9d1343030e75290d |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T09:29:38Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-39e3ab9e01d24e1f9d1343030e75290d2022-12-21T23:08:06ZengIEEEIEEE Access2169-35362019-01-017794187943210.1109/ACCESS.2019.29234358737940Personalized Check-in Prediction Model Based on User’s Dissimilarity and RegressionChang Su0https://orcid.org/0000-0002-0498-9907Qiuli Zhou1Xianzhong Xie2Dezheng Wu3College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, ChinaCollege of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, ChinaChongqing Key Laboratory of Computer Network and Communication Technology, Chongqing University of Posts and Telecommunications, Chongqing, ChinaCollege of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, ChinaTo solve the problem that the user check-in prediction model is difficult to provide personalized check-in services, this paper proposes a novel hybrid model, called personalized check-in prediction model based on user's dissimilarity and regression (UDR). The UDR is mainly composed of two sub-models: user's regression location prediction model (UR) and user's dissimilarity location prediction model (UD). In UR, considering the personalization of user check-ins, we propose a hybrid weighted Markov model, which combines the whole check-in data and individual check-ins. Different from other methods, for the prediction of individual check-ins, we not only consider the preference of individual users, but also the influence of friend relationships. Meanwhile, the Hidden Markov model(HMM) is used to determine users' next check-in location by using time series feature (week-hour) and location sequence. In addition, by improving the kernel density estimation, we propose a multi-level hybrid kernel density estimation model, which is built based on the individual, city and region layers, and smoothes the over-fitting phenomenon caused by few check-ins. In UD, we take into account the weather factors that most existing methods did not consider. By defining the “cold and hot spot transference” and weather similarity features, we explore the influence of weather on user's check-ins and also propose a method used to calculate the similarity between user check-in weather preferences and location weathers. At the same time, the influence of social, time, and space factors are also considered. The experiments on two LBSN datasets demonstrate that the performance of UDR is superior to the state-of-the-art check-in prediction methods.https://ieeexplore.ieee.org/document/8737940/Location-based social networkscheck-in predictionhybrid Markov modelkernel density estimation |
| spellingShingle | Chang Su Qiuli Zhou Xianzhong Xie Dezheng Wu Personalized Check-in Prediction Model Based on User’s Dissimilarity and Regression IEEE Access Location-based social networks check-in prediction hybrid Markov model kernel density estimation |
| title | Personalized Check-in Prediction Model Based on User’s Dissimilarity and Regression |
| title_full | Personalized Check-in Prediction Model Based on User’s Dissimilarity and Regression |
| title_fullStr | Personalized Check-in Prediction Model Based on User’s Dissimilarity and Regression |
| title_full_unstemmed | Personalized Check-in Prediction Model Based on User’s Dissimilarity and Regression |
| title_short | Personalized Check-in Prediction Model Based on User’s Dissimilarity and Regression |
| title_sort | personalized check in prediction model based on user x2019 s dissimilarity and regression |
| topic | Location-based social networks check-in prediction hybrid Markov model kernel density estimation |
| url | https://ieeexplore.ieee.org/document/8737940/ |
| work_keys_str_mv | AT changsu personalizedcheckinpredictionmodelbasedonuserx2019sdissimilarityandregression AT qiulizhou personalizedcheckinpredictionmodelbasedonuserx2019sdissimilarityandregression AT xianzhongxie personalizedcheckinpredictionmodelbasedonuserx2019sdissimilarityandregression AT dezhengwu personalizedcheckinpredictionmodelbasedonuserx2019sdissimilarityandregression |