Cloud Computing: 5th International Conference, CloudComp by Victor C.M. Leung, Roy Xiaorong Lai, Min Chen, Jiafu Wan

By Victor C.M. Leung, Roy Xiaorong Lai, Min Chen, Jiafu Wan

This e-book constitutes the completely refereed publish convention complaints of the fifth foreign convention on Cloud Computing, CloudComp 2014, held in Guilin, China, in October 2014. The 25 revised complete papers have been conscientiously reviewed and chosen from seventy two submissions and canopy issues akin to cellular cloud computing, companies, purposes, IoT on cloud, architectures and large info, cloud-assisted pervasive computing and providers, administration and virtualization for cloud, cloud security.

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Additional info for Cloud Computing: 5th International Conference, CloudComp 2014, Guilin, China, October 19-21, 2014, Revised Selected Papers

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116–120 (2013) 42 Z. Zhang et al. 18. : A trust-based top-k recommender system using social tagging network. In: Fuzzy Systems and Knowledge Discovery (FSKD 2012). pp. 1270–1274 (2012) 19. : Application of dimensionality reduction in recommender system-a case study. Technical report, DTIC Document (2000) 20. : Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. (TOIS) 22, 5–53 (2004) 21. : A comparative analysis of offline and online evaluations and discussion of research paper recommender system evaluation.

After, final recommendations provided to the user can be donated as: Rf (t) = Ro (t) ∪ Rn (t). (7) 38 5 Z. Zhang et al. Performance Evaluation Having described the design of our proposed recommender system, in this section we evaluate its correctness and effectiveness on a real data set. We first explain our experiment environment, and then show the results. e. , higher frequency users click an item, the more he/her likes it. Based on this, open source project Myrrix [27] is selected to build explicit ratings of user and item.

This indicates that he/she has the similar preference to new items and old ones. However, only using this parameter to decide whether a user likes new items or not is unreasonable, since he/she may change his/her preference after that. 15 from day 15 to day 32. These information indicates that this user prefers old items than new ones. From Fig. 2, it is clear to see that old items occupy a large portion of all viewing times, and thus, only the overall trend of the ratio between the number of new and old items can best reflect a user’s preference.

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