Multi-Context Integrated Deep Neural Network Model for Next Location Prediction
The prediction of next location for users in location-based social networks has become an increasing significant requirement since it can benefit both users and business. However, existing methods lack an integrated analysis of sequence context, input contexts, and user preferences in a unified way,...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2018-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8340154/ |
| _version_ | 1818558717679894528 |
|---|---|
| author | Jianxin Liao Tongcun Liu Meilian Liu Jingyu Wang Yulong Wang Haifeng Sun |
| author_facet | Jianxin Liao Tongcun Liu Meilian Liu Jingyu Wang Yulong Wang Haifeng Sun |
| author_sort | Jianxin Liao |
| collection | DOAJ |
| description | The prediction of next location for users in location-based social networks has become an increasing significant requirement since it can benefit both users and business. However, existing methods lack an integrated analysis of sequence context, input contexts, and user preferences in a unified way, and result in an unsatisfactory prediction. Moreover, the interaction between different kinds of input contexts has not been investigated. In this paper, we propose a multi-context integrated deep neural network model (MCI-DNN) to improve the accuracy of the next location prediction. In this model, we integrate sequence context, input contexts, and user preferences into a cohesive framework. First, we model sequence context and interaction of different kinds of input contexts jointly by extending the recurrent neural network to capture the semantic pattern of user behaviors from check-in records. After that, we design a feedforward neural network to capture high-level user preferences from check-in data and incorporate that into MCI-DNN. To deal with different kinds of input contexts in the form of multi-field categorical, we adopt embedding representation technology to automatically learn dense feature representations of input contexts. Experimental results on two typical real-world data sets show that the proposed model outperforms the current state-of-the-art approaches by about 57.12% for Foursquare and 76.4% for Gowalla on average regarding F1-score@5. |
| first_indexed | 2024-12-14T00:15:57Z |
| format | Article |
| id | doaj.art-917ff6a5e52d41fb88ec91a933b6f5e6 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T00:15:57Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-917ff6a5e52d41fb88ec91a933b6f5e62022-12-21T23:25:33ZengIEEEIEEE Access2169-35362018-01-016219802199010.1109/ACCESS.2018.28274228340154Multi-Context Integrated Deep Neural Network Model for Next Location PredictionJianxin Liao0Tongcun Liu1https://orcid.org/0000-0002-0520-7807Meilian Liu2Jingyu Wang3Yulong Wang4Haifeng Sun5State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, ChinaState Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, ChinaSchool of Business, Guilin University of Electronic Technology, Guilin, ChinaState Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, ChinaState Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, ChinaState Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, ChinaThe prediction of next location for users in location-based social networks has become an increasing significant requirement since it can benefit both users and business. However, existing methods lack an integrated analysis of sequence context, input contexts, and user preferences in a unified way, and result in an unsatisfactory prediction. Moreover, the interaction between different kinds of input contexts has not been investigated. In this paper, we propose a multi-context integrated deep neural network model (MCI-DNN) to improve the accuracy of the next location prediction. In this model, we integrate sequence context, input contexts, and user preferences into a cohesive framework. First, we model sequence context and interaction of different kinds of input contexts jointly by extending the recurrent neural network to capture the semantic pattern of user behaviors from check-in records. After that, we design a feedforward neural network to capture high-level user preferences from check-in data and incorporate that into MCI-DNN. To deal with different kinds of input contexts in the form of multi-field categorical, we adopt embedding representation technology to automatically learn dense feature representations of input contexts. Experimental results on two typical real-world data sets show that the proposed model outperforms the current state-of-the-art approaches by about 57.12% for Foursquare and 76.4% for Gowalla on average regarding F1-score@5.https://ieeexplore.ieee.org/document/8340154/Location-based social networksnext location predictiondeep neural networksequence predictionmulti-context |
| spellingShingle | Jianxin Liao Tongcun Liu Meilian Liu Jingyu Wang Yulong Wang Haifeng Sun Multi-Context Integrated Deep Neural Network Model for Next Location Prediction IEEE Access Location-based social networks next location prediction deep neural network sequence prediction multi-context |
| title | Multi-Context Integrated Deep Neural Network Model for Next Location Prediction |
| title_full | Multi-Context Integrated Deep Neural Network Model for Next Location Prediction |
| title_fullStr | Multi-Context Integrated Deep Neural Network Model for Next Location Prediction |
| title_full_unstemmed | Multi-Context Integrated Deep Neural Network Model for Next Location Prediction |
| title_short | Multi-Context Integrated Deep Neural Network Model for Next Location Prediction |
| title_sort | multi context integrated deep neural network model for next location prediction |
| topic | Location-based social networks next location prediction deep neural network sequence prediction multi-context |
| url | https://ieeexplore.ieee.org/document/8340154/ |
| work_keys_str_mv | AT jianxinliao multicontextintegrateddeepneuralnetworkmodelfornextlocationprediction AT tongcunliu multicontextintegrateddeepneuralnetworkmodelfornextlocationprediction AT meilianliu multicontextintegrateddeepneuralnetworkmodelfornextlocationprediction AT jingyuwang multicontextintegrateddeepneuralnetworkmodelfornextlocationprediction AT yulongwang multicontextintegrateddeepneuralnetworkmodelfornextlocationprediction AT haifengsun multicontextintegrateddeepneuralnetworkmodelfornextlocationprediction |