A novel task recommendation model for mobile crowdsourcing systems

With the developments of sensors in mobile devices, mobile crowdsourcing systems are attracting more and more attention. How to recommend user-preferred and trustful tasks for users is an important issue to improve efficiency of mobile crowdsourcing systems. This paper proposes a novel task recommendation model for mobile crowdsourcing systems. Considering both user similarity and task similarity, the recommendation probabilities of tasks are derived. Based on dwell-time, the latent recommendation probability of tasks can be predicted. In addition, trust of tasks is obtained based on their reputations and participation frequencies. Finally, we perform comprehensive experiments towards the Amazon metadata and YOOCHOOSE data sets to verify the effectiveness of the proposed recommendation model.

[1]  Yung-Ming Li,et al.  Recommending social network applications via social filtering mechanisms , 2013, Inf. Sci..

[2]  Yang Guo,et al.  Bayesian-Inference-Based Recommendation in Online Social Networks , 2011, IEEE Transactions on Parallel and Distributed Systems.

[3]  Jianzhong Li,et al.  An Application-Aware Scheduling Policy for Real-Time Traffic , 2015, 2015 IEEE 35th International Conference on Distributed Computing Systems.

[4]  Douglas B. Terry,et al.  Using collaborative filtering to weave an information tapestry , 1992, CACM.

[5]  Guo Le.  Incorporating Item Relations for Social Recommendation , 2014 .

[6]  Hsinchun Chen,et al.  A graph model for E-commerce recommender systems , 2004, J. Assoc. Inf. Sci. Technol..

[7]  Yingshu Li,et al.  Using crowdsourced data in location-based social networks to explore influence maximization , 2016, IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications.

[8]  Zhipeng Cai,et al.  Modeling Propagation Dynamics and Developing Optimized Countermeasures for Rumor Spreading in Online Social Networks , 2015, 2015 IEEE 35th International Conference on Distributed Computing Systems.

[9]  Mohammad Salehan,et al.  Social networking on smartphones: When mobile phones become addictive , 2013, Comput. Hum. Behav..

[10]  Arbee L. P. Chen,et al.  A music recommendation system based on music data grouping and user interests , 2001, CIKM '01.

[11]  Guisheng Yin,et al.  A trust-based probabilistic recommendation model for social networks , 2015, J. Netw. Comput. Appl..

[12]  Shuhui Yang,et al.  Poster: crowdsourcing to smartphones: social network based human collaboration , 2014, MobiHoc '14.

[13]  Andrew Zisserman,et al.  Near Duplicate Image Detection: min-Hash and tf-idf Weighting , 2008, BMVC.

[14]  Yingshu Li,et al.  An exploration of broader influence maximization in timeliness networks with opportunistic selection , 2016, J. Netw. Comput. Appl..

[15]  John Riedl,et al.  GroupLens: an open architecture for collaborative filtering of netnews , 1994, CSCW '94.

[16]  Jianzhong Li,et al.  Extracting Kernel Dataset from Big Sensory Data in Wireless Sensor Networks , 2017, IEEE Transactions on Knowledge and Data Engineering.

[17]  Xu Yang,et al.  Transaction rating credibility based on user group preference , 2015 .

[18]  Yang Xiao,et al.  An Accountable Framework for Sensing-Oriented Mobile Cloud Computing , 2014 .

[19]  Jiguo Yu,et al.  Cost-Efficient Strategies for Restraining Rumor Spreading in Mobile Social Networks , 2017, IEEE Transactions on Vehicular Technology.

[20]  Nuanwan Soonthornphisaj,et al.  Hybrid Recommendation: Combining Content-Based Prediction and Collaborative Filtering , 2003, IDEAL.

[21]  Yingshu Li,et al.  Collective Data-Sanitization for Preventing Sensitive Information Inference Attacks in Social Networks , 2018, IEEE Transactions on Dependable and Secure Computing.

[22]  Xin Jin,et al.  A maximum entropy web recommendation system: combining collaborative and content features , 2005, KDD '05.

[23]  Yuqiang Sun,et al.  Collaborative filtering recommender method based on trust , 2015 .

[24]  Yingshu Li,et al.  Truthful Incentive Mechanisms for Social Cost Minimization in Mobile Crowdsourcing Systems , 2016, Sensors.

[25]  Jiguo Yu,et al.  Influence maximization by probing partial communities in dynamic online social networks , 2017, Trans. Emerg. Telecommun. Technol..

[26]  Jianzhong Li,et al.  Location-privacy-aware review publication mechanism for local business service systems , 2017, IEEE INFOCOM 2017 - IEEE Conference on Computer Communications.

[27]  Yang Gao,et al.  A Task Recommendation Model for Mobile Crowdsourcing Systems Based on Dwell-Time , 2016, 2016 IEEE International Conferences on Big Data and Cloud Computing (BDCloud), Social Computing and Networking (SocialCom), Sustainable Computing and Communications (SustainCom) (BDCloud-SocialCom-SustainCom).

[28]  Yu Zhou,et al.  An Efficient Tree-Based Self-Organizing Protocol for Internet of Things , 2016, IEEE Access.

[29]  Feng Xia,et al.  ERGID: An efficient routing protocol for emergency response Internet of Things , 2016, J. Netw. Comput. Appl..

[30]  Wei Chen,et al.  Making recommendations from multiple domains , 2013, KDD.

[31]  Shao Kun,et al.  Normal Distribution Based Dynamical Recommendation Trust Model , 2012 .

[32]  Tie Qiu,et al.  A task-efficient sink node based on embedded multi-core SoC for Internet of Things , 2016, Future Gener. Comput. Syst..

[33]  Peifeng Yin,et al.  Silence is also evidence: interpreting dwell time for recommendation from psychological perspective , 2013, KDD.

[34]  Yang Gao,et al.  An incentive mechanism with privacy protection in mobile crowdsourcing systems , 2016, Comput. Networks.

[35]  Ioannis Konstas,et al.  On social networks and collaborative recommendation , 2009, SIGIR.

[36]  Kenneth Y. Goldberg,et al.  Eigentaste: A Constant Time Collaborative Filtering Algorithm , 2001, Information Retrieval.

[37]  Yi-Cheng Zhang,et al.  Bipartite network projection and personal recommendation. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[38]  Demetrios Zeinalipour-Yazti,et al.  Crowdsourcing with Smartphones , 2012, IEEE Internet Computing.

[39]  Jianzhong Li,et al.  A Study on Application-Aware Scheduling in Wireless Networks , 2017, IEEE Transactions on Mobile Computing.