Personalized Recommendation Algorithm for Intelligent Travel Service Robot Based on Big Data

2022 ◽  
pp. 1149-1154
Author(s):  
Suyun Li ◽  
Liping Lai
Keyword(s):  
Big Data ◽  
Personalized Recommendation ◽  
Service Robot ◽  
Recommendation Algorithm

scholarly journals Construction of E-commerce Personalized Information Recommendation System in the Era of Big Data

2021 ◽  
Vol 2074 (1) ◽  
pp. 012085
Author(s):  
Yaosheng Wang
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Recommendation System ◽  
Personalized Recommendation ◽  
Service Recommendation ◽  
Actual System ◽  
Recommendation Algorithm ◽  
Dissimilarity Matrix ◽  
Logistics Service ◽  
Depth Study

Abstract With the continuous expansion of the scale of e-commerce, personalized recommendation technology has been widely used. However, the traditional recommendation system has been unable to meet the current needs of data processing, and good big data processing ability has become the basic requirement of the new personalized recommendation system. In addition, traditional recommendation systems are often limited to tangible goods recommendation, and pay less attention to e-commerce logistics service recommendation. In this paper, through the in-depth study of information personalized recommendation service in e-commerce environment, combined with the application background of big data: Taking the user dissimilarity matrix as the recommendation model, we propose IU usercf and UDB slope one recommendation algorithm. The two algorithms based on incremental update recommendation model have good scalability, can effectively deal with big data, and have high prediction accuracy. The proposed algorithm is applied to the actual system, taking e-commerce logistics service as the recommendation object and iu-usercf as the recommendation algorithm, the personalized recommendation system for e-commerce logistics service is constructed. The e-commerce logistics service recommendation system explores the application practice of recommendation algorithm under big data, and enriches the application scenarios of personalized recommendation technology.

scholarly journals The application of e-commerce recommendation system in smart cities based on big data and cloud computing

2021 ◽  
pp. 26-26
Author(s):  
Yiman Zhang
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Collaborative Filtering ◽  
Real Time ◽  
Evaluation System ◽  
Recommendation System ◽  
Smart Cities ◽  
Personalized Recommendation ◽  
Current Recommendation ◽  
Recommendation Algorithm

In the era of big data, the amount of Internet data is growing explosively. How to quickly obtain valuable information from massive data has become a challenging task. To effectively solve the problems faced by recommendation technology, such as data sparsity, scalability, and real-time recommendation, a personalized recommendation algorithm for e-commerce based on Hadoop is designed aiming at the problems in collaborative filtering recommendation algorithm. Hadoop cloud computing platform has powerful computing and storage capabilities, which are used to improve the collaborative filtering recommendation algorithm based on project, and establish a comprehensive evaluation system. The effectiveness of the proposed personalized recommendation algorithm is further verified through the analysis and comparison with some traditional collaborative filtering algorithms. The experimental results show that the e-commerce system based on cloud computing technology effectively improves the support of various recommendation algorithms in the system environment; the algorithm has good scalability and recommendation efficiency in the distributed cluster, and the recommendation accuracy is also improved, which can improve the sparsity, scalability and real-time problems in e-commerce personalized recommendation. This study greatly improves the recommendation performance of e-commerce, effectively solves the shortcomings of the current recommendation algorithm, and further promotes the personalized development of e-commerce.

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.

Optimized Collaborative Filtering Recommendation Based on User’s Social Relationships

2014 ◽  
Vol 1044-1045 ◽  
pp. 1484-1488
Author(s):  
Yue Kun Fan ◽  
Xin Ye Li ◽  
Meng Meng Cao
Keyword(s):  
Collaborative Filtering ◽  
Social Relationships ◽  
Digital Libraries ◽  
Social Relationship ◽  
Nearest Neighbor ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Personalized Recommendation ◽  
Recommendation Algorithm

Currently collaborative filtering is widely used in e-commerce, digital libraries and other areas of personalized recommendation service system. Nearest-neighbor algorithm is the earliest proposed and the main collaborative filtering recommendation algorithm, but the data sparsity and cold-start problems seriously affect the recommendation quality. To solve these problems, A collaborative filtering recommendation algorithm based on users' social relationships is proposed. 0n the basis of traditional filtering recommendation technology, it combines with the interested objects of user's social relationship and takes the advantage of the tags to projects marked by users and their interested objects to improve the methods of recommendation. The experimental results of MAE ((Mean Absolute Error)) verify that this method can get better quality of recommendation.

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