A Group Recommendation System of Network Document Resource Based on Knowledge Graph and LSTM in Edge Computing

The Internet has become one of the important channels for users to obtain information and knowledge. It is crucial to work out how to acquire personalized requirement of users accurately and effectively from huge amount of network document resources. Group recommendation is an information system for group participation in common activities that meets the common interests of all members in the group. This paper proposes a group recommendation system for network document resource exploration using the knowledge graph and LSTM in edge computing, which can solve the problem of information overload and resource trek effectively. An extensive system test has been carried out in the field of big data application in packaging industry. The experimental results show that the proposed system recommends network document resource more accurately and further improves recommendation quality using the knowledge graph and LSTM in edge computing. Therefore, it can meet the user’s personalized resource need more effectively.

[1]  Yuan Tian,et al.  A Review of Techniques and Methods for IoT Applications in Collaborative Cloud-Fog Environment , 2020, Secur. Commun. Networks.

[2]  WuJie,et al.  Personalized Review Recommendation based on Users’ Aspect Sentiment , 2020 .

[3]  LiYang,et al.  Knowledge Graph Construction Techniques , 2016 .

[4]  Nikos Manouselis,et al.  marService: multiattribute utility recommendation for e-markets , 2008, Int. J. Comput. Appl. Technol..

[5]  Xing Zhang,et al.  Adaptive Computation Offloading With Edge for 5G-Envisioned Internet of Connected Vehicles , 2020, IEEE Transactions on Intelligent Transportation Systems.

[6]  Wazir Zada Khan,et al.  Edge computing: A survey , 2019, Future Gener. Comput. Syst..

[7]  Shuhong Chen,et al.  A Personalized Collaborative Filtering Recommendation System of Network Document Resource Based on Knowledge Graph , 2019 .

[8]  Toon De Pessemier,et al.  Hybrid group recommendations for a travel service , 2016, Multimedia Tools and Applications.

[9]  Anfeng Liu,et al.  A risk defense method based on microscopic state prediction with partial information observations in social networks , 2019, J. Parallel Distributed Comput..

[10]  Minchao Ye,et al.  Preference transfer model in collaborative filtering for implicit data , 2016, Frontiers of Information Technology & Electronic Engineering.

[11]  Hiroshi Yasuda,et al.  User behaviour modelling by abstracting low-level window transition logs , 2015, Int. J. Comput. Sci. Eng..

[12]  Jie Wu,et al.  Understanding Graph-Based Trust Evaluation in Online Social Networks , 2016, ACM Comput. Surv..

[13]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[14]  Gediminas Adomavicius,et al.  Incorporating contextual information in recommender systems using a multidimensional approach , 2005, TOIS.

[15]  Md Zakirul Alam Bhuiyan,et al.  Trust-Aware Service Offloading for Video Surveillance in Edge Computing Enabled Internet of Vehicles , 2021, IEEE Transactions on Intelligent Transportation Systems.

[16]  Erik Brynjolfsson,et al.  Big data: the management revolution. , 2012, Harvard business review.

[17]  Yujie Zhang,et al.  Mobile Recommender Systems and Their Applications: Mobile Recommender Systems and Their Applications , 2014 .

[18]  Cong Yu,et al.  Space efficiency in group recommendation , 2010, The VLDB Journal.

[19]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[20]  Laura Sebastia,et al.  Preference elicitation techniques for group recommender systems , 2012, Inf. Sci..

[21]  Chin-Hui Lai,et al.  Applying knowledge flow mining to group recommendation methods for task‐based groups , 2015, J. Assoc. Inf. Sci. Technol..

[22]  Arun Kumar Sangaiah,et al.  Edge-Computing-Based Trustworthy Data Collection Model in the Internet of Things , 2020, IEEE Internet of Things Journal.

[23]  Guy Shani,et al.  A Survey of Accuracy Evaluation Metrics of Recommendation Tasks , 2009, J. Mach. Learn. Res..

[24]  Qinghua Zheng,et al.  A behavioral sequence analyzing framework for grouping students in an e-learning system , 2016, Knowl. Based Syst..

[25]  Keyan Cao,et al.  An Overview on Edge Computing Research , 2020, IEEE Access.

[26]  Arun Kumar Sangaiah,et al.  Mobility Based Trust Evaluation for Heterogeneous Electric Vehicles Network in Smart Cities , 2021, IEEE Transactions on Intelligent Transportation Systems.

[27]  Michael J. Pazzani,et al.  A Framework for Collaborative, Content-Based and Demographic Filtering , 1999, Artificial Intelligence Review.

[28]  Xiaolong Li,et al.  Privacy-Enhanced Data Collection Based on Deep Learning for Internet of Vehicles , 2020, IEEE Transactions on Industrial Informatics.

[29]  Yanchun Zhang,et al.  Enhancing online video recommendation using social user interactions , 2017, The VLDB Journal.

[30]  Huaming Wu,et al.  Edge Server Quantification and Placement for Offloading Social Media Services in Industrial Cognitive IoV , 2021, IEEE Transactions on Industrial Informatics.