CRUC: Cold-start Recommendations Using Collaborative Filtering in Internet of Things

The Internet of Things (IoT) aims at interconnecting everyday objects (including both things and users) and then using this connection information to provide customized user services. However, IoT does not work in its initial stages without adequate acquisition of user preferences. This is caused by cold-start problem that is a situation where only few users are interconnected. To this end, we propose CRUC scheme - Cold-start Recommendations Using Collaborative Filtering in IoT, involving formulation, filtering and prediction steps. Extensive experiments over real cases and simulation have been performed to evaluate the performance of CRUC scheme. Experimental results show that CRUC efficiently solves the cold-start problem in IoT.Comment: Elsevier ESEP 2011: 9-10 December 2011, Singapore, Elsevier Energy Procedia, http://www.elsevier.com/locate/procedia/, 201

Analisis dan Implementasi Collaborative Filtering menggunakan Strategi Smoothing dan Fusing pada Recommender System

Collaborative Filtering (CF) adalah salah satu pendekatan yang populer untuk membangun Recommender System dengan memanfaatkan informasi dan preferensi dari user lain untuk memberikan rekomendasi item. Salah satu permasalahan mendasar dalam CF adalah data rating yang sangat sedikit (data sparsity) yang mampu mempengaruhi hasil rekomendasi. Secara umum terdapat dua tipe algoritma pada CF, yaitu memory-based dan model-based yang memiliki kelebihan dan kekurangan masing-masing. Dalam tugas akhir ini, digunakan strategi smoothing dan fusing yang merupakan pendekatan hybrid dari memory-based dan model-based untuk menangani permasalahan data sparsity. Berdasarkan hasil pengujian, strategi smoothing dan fusing mampu menurunkan error sistem yang diukur menggunakan MAE dari 2,277 menjadi 0,746 atau menurun sebesar 50.624% dibandingkan tanpa menggunakan strategi smoothing dan fusing. Selain itu, akurasi sistem juga dipengaruhi oleh level sparsity dari data rating. Semakin sparse data rating yang dimiliki, maka akurasi yang dihasilkan semakin buruk

プロファイルと履歴を組織間で統合利用するプライバシ保護推薦システム

電気通信大学201

An Efficient Collaborative Filtering Approach Using Smoothing and Fusing

38th International Conference on Parallel Processing, ICPP-2009, Vienna, 22-25 September 2009Collaborative Filtering (CF) has achieved widespread success in recommender systems such as Amazon and Yahoo! music. However, CF usually suffers from two fundamental problems- data sparsity and limited scalability. Among the two broad classes of CF approaches, namely, memory-based and model-based, the former usually falls short of the system scalability demands, because these approaches predict user preferences over the entire item-user matrix. The latter often achieves unsatisfactory accuracy, because they cannot capture precisely the diversity in user rating styles. In this paper, we propose an efficient Collaborative Filtering approach using Smoothing and Fusing (CFSF) strategies. CFSF formulates the CF problem as a local prediction problem by mapping it from the entire large-scale item-user matrix to a locally reduced item-user matrix. Given an active item and a user, CFSF dynamically constructs a local item-user matrix as the basis of prediction. To alleviate data sparsity, CFSF presents a fusion strategy for the local item-user matrix that fuses ratings of the same user makes on similar items, and ratings of likeminded users make on the same and similar items. To eliminate diversity in user rating styles, CFSF uses a smoothing strategy that clusters users over the entire item-user matrix and then smoothes ratings within each user cluster. Empirical study shows that CFSF outperforms the state-of-the-art CF approaches in terms of both accuracy and scalability.Department of ComputingRefereed conference pape

Optimising data placement and traffic routing for energy saving in Backbone Networks

The energy consumption of network elements has become a big concern due to the exponential traffic growth and the rapid expansion of communication infrastructures. To deal with this problem, we propose a new approach called Backbone network Energy Saving based on Traffic engineering (BEST), which reduces the power consumption of network elements at the backbone level through jointly optimising data placement and traffic routing. Based on analysis on traffic characteristics, BEST firstly optimises the placement of data services such that the pairwise traffic demands can be better coordinated with the pairwise network costs, in order to minimise the traffic burden imposed on the network elements. Then, BEST optimises the routing of traffic flows and tries to find the minimum-power network subset that must remain active to fulfill the traffic requirements. Efficient heuristics are given by BEST to find an admissible solution when the problem size is very large. The simulation results illustrate the efficacy and efficiency of BEST in energy conservation on backbone networksThis work was supported in part by the National Science Foundation of China (grant nos. 61202430, 61303245 and 61103185), the Science and Technology Foundation of Beijing Jiaotong University (grant no. 2012RC040).Fang, W.; Wang, Z.; Lloret, J.; Zhang, D.; Yang, Z. (2014). Optimising data placement and traffic routing for energy saving in Backbone Networks. Transactions on Emerging Telecommunications Technologies. 25(9):914-925. https://doi.org/10.1002/ett.2774S914925259Zhang MG Yi C Liu B Zhang BC GreenTE: power-aware traffic engineering Proceedings of the 18 th IEEE International Conference on Network Protocols (ICNP 2010) 2010 21 30Cianfrani, A., Eramo, V., Listanti, M., Polverini, M., & Vasilakos, A. V. (2012). An OSPF-Integrated Routing Strategy for QoS-Aware Energy Saving in IP Backbone Networks. 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International Journal of Network Management, 15(5), 297-310. doi:10.1002/nem.565Morosi, S., Piunti, P., & Re, E. D. (2013). Sleep mode management in cellular networks: a traffic based technique enabling energy saving. Transactions on Emerging Telecommunications Technologies, 24(3), 331-341. doi:10.1002/ett.2621Bolla, R., Bruschi, R., Davoli, F., & Cucchietti, F. (2011). Energy Efficiency in the Future Internet: A Survey of Existing Approaches and Trends in Energy-Aware Fixed Network Infrastructures. IEEE Communications Surveys & Tutorials, 13(2), 223-244. doi:10.1109/surv.2011.071410.00073Yaacoub, E., AlKanj, L., Dawy, Z., Sharafeddine, S., Filali, F., & Abu-Dayya, A. (2012). A utility minimization approach for energy-aware cooperative content distribution with fairness constraints. Transactions on Emerging Telecommunications Technologies, 23(4), 378-392. doi:10.1002/ett.1546Al-Kanj, L., & Dawy, Z. (2012). Impact of network parameters on the design of energy-aware cooperative content distribution protocols. Transactions on Emerging Telecommunications Technologies, 24(3), 317-330. doi:10.1002/ett.2552Emilio G Sebastián AM Eduardo RS Jaime L Energy consumption study of network access switches to enhance energy distribution Proceedings of the 2012 IEEE Global Communications Conference (Globecom 2012) 2012 1496 1501Sebastián AM Eduardo RS Sandra S Jaime L Router power consumption analysis: towards green communications Proceedings of the 2 nd ICST International Conference on Green Commuincations and Networking (GreeNets 2012) 2012 28 37Sebastián AM Eduardo RS Emilio G Jaime L Energy consumption of wireless network access points Proceedings of the 2 nd ICST International Conference on Green Commuincations and Networking (GreeNets 2012) 2012 81 91Amaldi E Capone A Gianoli L Mascetti LA MILP-based heuristic for energy-aware traffic engineering with shortest path routing Proceedings of the 2011 International Network Optimization Conference (INOC 2011) 2011 464 477Chiaraviglio, L., Mellia, M., & Neri, F. (2012). Minimizing ISP Network Energy Cost: Formulation and Solutions. IEEE/ACM Transactions on Networking, 20(2), 463-476. doi:10.1109/tnet.2011.2161487Cuomo, F., Cianfrani, A., Polverini, M., & Mangione, D. (2012). Network pruning for energy saving in the Internet. Computer Networks, 56(10), 2355-2367. doi:10.1016/j.comnet.2012.03.009Heller B Seetharaman S Mahadevan P Yiakoumis Y Sharma P Banerjee S Mckeown N Elastictree: saving energy in data center networks Proceedings of the 7 th USENIX Symposium on Networked Systems Design and Implementation (NSDI 2010) 2010 17 17Fang, W., Liang, X., Li, S., Chiaraviglio, L., & Xiong, N. (2013). VMPlanner: Optimizing virtual machine placement and traffic flow routing to reduce network power costs in cloud data centers. Computer Networks, 57(1), 179-196. doi:10.1016/j.comnet.2012.09.008Agarwal S Dunagan J Jain N Saroiu S Wolman A Bhogan H Volley: automated data placement for geo-distributed cloud services Proceedings of the 7 th USENIX Symposium on Networked Systems Design and Implementation (NSDI 2010) 2010 17 32Meng X Pappas V Zhang L Improving the scalability of data center networks with traffic-aware virtual machine placement Proceedings of the 29 th Conference on Computer Communications (INFOCOM 2010) 2010 1 9Famaey, J., Wauters, T., De Turck, F., Dhoedt, B., & Demeester, P. (2011). Network-aware service placement and selection algorithms on large-scale overlay networks. Computer Communications, 34(15), 1777-1787. doi:10.1016/j.comcom.2011.03.017Steiner M Gaglianello B Gurbani V Hilt V Roome W Scharf M Voith T Network-aware service placement in a distributed cloud environment Proceedings of the 2012 ACM Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications (SIGCOMM 2012) 2012 73 74Zhang, Y., Chowdhury, P., Tornatore, M., & Mukherjee, B. (2010). Energy Efficiency in Telecom Optical Networks. IEEE Communications Surveys & Tutorials, 12(4), 441-458. doi:10.1109/surv.2011.062410.00034Bianzino, A. P., Chaudet, C., Rossi, D., & Rougier, J.-L. (2012). A Survey of Green Networking Research. 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Computer Networks, 57(4), 944-953. doi:10.1016/j.comnet.2012.11.010Mohsin, A. H., Abu Bakar, K., Adekiigbe, A., & Ghafoor, K. Z. (2012). A Survey of Energy-aware Routing protocols in Mobile Ad-hoc Networks: Trends and Challenges. Network Protocols and Algorithms, 4(2). doi:10.5296/npa.v4i2.1154Zhang, D., Chen, M., Huang, H., & Guo, M. (2011). Decentralized checking of context inconsistency in pervasive computing environments. The Journal of Supercomputing, 64(2), 256-273. doi:10.1007/s11227-011-0661-xZhang DQ Cao JN Guo MY Zhou J Raychoudhury V An efficient collaborative filtering approach using smoothing and fusing Proceedings of the 38 th International Conference on Parallel Processing (ICPP 2009) 2009 558 56