NEIGHBORHOOD-BASED APPROACH OF COLLABORATIVE FILTERING TECHNIQUES FOR BOOK RECOMMENDATION SYSTEM

Recommendation System or Recommender System help the user to predict the "rating" or "preference" a user would give to an item. Recommender systems in general helps the users to find content, products, or services (such as digital products, books, music, movie, TV programs, and web sites) by combining and analyzing suggestions from other users, which mean rating from various people, and users. These recommendation systems use analytic technology to calculate the results that a user is willing to purchase, and the users will receive recommendations to a product of their interest. The aim of the System is to provide a recommendation based on users likes or reviews or ratings. Recommendation system comprises of content based and collaborative based filtering techniques. In this paper, collaborative based filtering has been used to get the expected outcome. The expected outcome has been achieved through collaborative filtering with the help of correlation techniques which in turn comprises of Pearson correlation, cosine similarity, Kendall’ s Tau correlation, Jaccard similarity, Spearman Rank Correlation, Mean-squared distance, etc. This paper tells about which similarity metrics such us Pearson correlation (PC), constrained Pearson correlation (CPC), spearman rank correlation (SRC) which is good in the context of book recommendation system and then applied with neighborhood algorithm

NEIGHBORHOOD-BASED APPROACH OF COLLABORATIVE FILTERING TECHNIQUES FOR BOOK RECOMMENDATION SYSTEM

Recommendation System or Recommender System help the user to predict the "rating" or "preference" a user would give to an item. Recommender systems in general helps the users to find content, products, or services (such as digital products, books, music, movie, TV programs, and web sites) by combining and analyzing suggestions from other users, which mean rating from various people, and users. These recommendation systems use analytic technology to calculate the results that a user is willing to purchase, and the users will receive recommendations to a product of their interest. The aim of the System is to provide a recommendation based on users likes or reviews or ratings. Recommendation system comprises of content based and collaborative based filtering techniques. In this paper, collaborative based filtering has been used to get the expected outcome. The expected outcome has been achieved through collaborative filtering with the help of correlation techniques which in turn comprises of Pearson correlation, cosine similarity, Kendall’ s Tau correlation, Jaccard similarity, Spearman Rank Correlation, Mean-squared distance, etc. This paper tells about which similarity metrics such us Pearson correlation (PC), constrained Pearson correlation (CPC), spearman rank correlation (SRC) which is good in the context of book recommendation system and then applied with neighborhood algorithm

Carbon-efficient virtual machine placement based on dynamic voltage frequency scaling in geo-distributed cloud data centers

The tremendous growth of big data analysis and IoT (Internet of Things) has made cloud computing an integral part of society. The prominent problem associated with data centers is the growing energy consumption, which results in environmental pollution. Data centers can reduce their carbon emissions through efficient management of server power consumption for a given workload. Dynamic voltage frequency scaling (DVFS) can be applied to control the operating frequencies of the servers based on the workloads assigned to them, as this approach has a cubic increment relationship with power consumption. This research work proposes two DVFS-enabled host selection algorithms for virtual machine (VM) placement with a cluster selection strategy, namely the carbon and power-efficient optimal frequency (C-PEF) algorithm and the carbon-aware first-fit optimal frequency (C-FFF) algorithm. The main aims of the proposed algorithms are to balance the load among the servers and dynamically tune the cooling load based on the current workload. The cluster selection strategy is based on static and dynamic power usage effectiveness (PUE) values and the carbon footprint rate (CFR). The cluster selection is also extended to non-DVFS host selection policies, namely the carbon-and power-efficient (C-PE) algorithm, carbon-aware first-fit (C-FF) algorithm, and carbon-aware first-fit least-empty (C-FFLE) algorithm. The results show that C-FFF achieves 2% more power reduction than C-PEF and C-PE, and demonstrates itself as a power-efficient algorithm for CO2 reduction, retaining the same quality of service (QoS) as its counterparts with lower computational overheads

NEIGHBORHOOD-BASED APPROACH OF COLLABORATIVE FILTERING TECHNIQUES FOR BOOK RECOMMENDATION SYSTEM

International audienceRecommendation System or Recommender System help the user to predict the "rating" or "preference" a user would give to an item. Recommender systems in general helps the users to find content, products, or services (such as digital products, books, music, movie, TV programs, and web sites) by combining and analyzing suggestions from other users, which mean rating from various people, and users. These recommendation systems use analytic technology to calculate the results that a user is willing to purchase, and the users will receive recommendations to a product of their interest. The aim of the System is to provide a recommendation based on users likes or reviews or ratings. Recommendation system comprises of content based and collaborative based filtering techniques. In this paper, collaborative based filtering has been used to get the expected outcome. The expected outcome has been achieved through collaborative filtering with the help of correlation techniques which in turn comprises of Pearson correlation, cosine similarity, Kendall’ s Tau correlation, Jaccard similarity, Spearman Rank Correlation, Mean-squared distance, etc. This paper tells about which similarity metrics such us Pearson correlation (PC), constrained Pearson correlation (CPC), spearman rank correlation (SRC) which is good in the context of book recommendation system and then applied with neighborhood algorithm