AN EFFICIENT PERFORMANCE ANALYSIS USING COLLABORATIVE RECOMMENDATION SYSTEM ON BIG DATA

2021 ◽  
Author(s):  
Deepak V. ◽  
M. Rajesh Khanna ◽  
K. Dhanasekaran ◽  
P. G. Om Prakash ◽  
D. Vijendra Babu
Keyword(s):  
Big Data ◽  
Performance Analysis ◽  
Recommendation System ◽  
Collaborative Recommendation ◽  
Efficient Performance

scholarly journals Application of Improved Recommendation System Based on Spark Platform in Big Data Analysis

2016 ◽  
Vol 16 (6) ◽  
pp. 245-255 ◽  
Author(s):  
Li Xie ◽  
Wenbo Zhou ◽  
Yaosen Li
Keyword(s):  
Big Data ◽  
Recommender System ◽  
Recommendation System ◽  
Parallel Implementation ◽  
Implementation Process ◽  
Recommendation Algorithm ◽  
Filtration Problem ◽  
Information Filtration ◽  
Similarity Information ◽  
Better Than

Abstract In the era of big data, people have to face information filtration problem. For those cases when users do not or cannot express their demands clearly, recommender system can analyse user’s information more proactive and intelligent to filter out something users want. This property makes recommender system play a very important role in the field of e-commerce, social network and so on. The collaborative filtering recommendation algorithm based on Alternating Least Squares (ALS) is one of common algorithms using matrix factorization technique of recommendation system. In this paper, we design the parallel implementation process of the recommendation algorithm based on Spark platform and the related technology research of recommendation systems. Because of the shortcomings of the recommendation algorithm based on ALS model, a new loss function is designed. Before the model is trained, the similarity information of users and items is fused. The experimental results show that the performance of the proposed algorithm is better than that of algorithm based on ALS.

scholarly journals Interactive Marketing E-Commerce Recommendation System Driven by Big Data Technology

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yi Fu ◽  
Min Yang ◽  
Di Han
Keyword(s):  
Big Data ◽  
Recommendation System ◽  
Reference Model ◽  
Relevant Literature ◽  
Force Model ◽  
Interactive Marketing ◽  
Marketing Innovation ◽  
Interactive Relationship ◽  
The Media ◽  
Big Data Technology

This study combs through relevant literature, adopts a combination of typical sampling and random sampling, collects three big data technology-driven interactive marketing e-commerce companies in a specific period of Sina Weibo sample data for research, obtains historical information and data, and constructs a model. Through relevant analysis to eliminate invalid variables, we creatively selected three variables of Internet hot words, activities, and microtopics as independent variables and used marketing effects as dependent variables to carry out empirical analysis and study the marketing innovation of three representative companies based on big data technology. We discussed the use of self-media in interactive marketing e-commerce and the situation of marketing innovation based on self-media, focusing on the interactive relationship between marketing innovation and Internet word-of-mouth (brand image). Through research, we have derived the three-force model, which is the biggest result of this research, and provided a reference model for interactive marketing e-commerce companies to carry out follow-up marketing innovation based on the media. Limited to the level of research and ability, there are some deficiencies in the research, such as barrage marketing, big data marketing, and emotional computing, that have not been analyzed in depth. This article fully considers the dependence of small and medium e-commerce companies on e-commerce platforms in the era of big data and conducted detailed market research on their precision marketing strategies in the era of big data. This will be a new field that does not come from media marketing. This article intends to summarize a series of experiences and laws from special to general, from individuality to generality, so as to give full play to the role of personalized marketing in increasing website traffic and order conversion, in order to personalize the use of data by other e-commerce companies with marketing provides some valuable experiences and methods for reference.

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