Research on Improved Collaborative Filtering Recommendation Algorithm on Hadoop

Tian, Baojun; Du, Xin; Hu, Peipei; Su, Yila

doi:10.14257/ijca.2016.9.12.33

Cited by 2 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“… Pearson coefficient correlation is shown in Equation (1). Let U ij be the common user set of items i and j, and then the similarity sim (i, j) between item i and item j is measured by the Pearson correlation coefficient [13].…”

Section: Collect User Behavior Informationmentioning

confidence: 99%

See 1 more Smart Citation

Collaborative Filtering Algorithm based on Data Mixing and Filtering

Cheng¹,

Feng²,

Gui³

2019

Int J Performability Eng

View full text Add to dashboard Cite

Personalized recommendation systems based on the collaborative filtering algorithm are faced with an excessive user rating data sparseness problem. In order to solve this problem, an improved collaborative filtering algorithm is proposed, which gathers a variety of single numerical filling methods and selects a more appropriate filling method according to the filling rules to fill the vacant positions in the user-item scoring matrix filling. The recommendations are then made on the populated user-item score matrix through a user-based collaborative filtering approach. The method of data mixed filling can effectively reduce the recommended error and numerical singularity caused by fixed filling values such as the mean and median. The improved collaborative filtering algorithm is tested on the Movie Lens data set. The results show that the method of data mixing is adopted to fill the empty positions in the scoring matrix, which effectively alleviates the data sparsity problem in the collaborative filtering algorithm and improves the accuracy of recommendation systems for target users.

show abstract

Section: Collect User Behavior Informationmentioning

confidence: 99%

“…In order to solve the problem of how to quickly and accurately recommend products of interest to users and solve information overload problems, personalized recommendation systems have emerged and are widely used in various industries as a convenient way to find interesting and valuable information bands for users [1][2].…”

Section: Introductionmentioning

confidence: 99%

Collaborative Filtering Algorithm based on Data Mixing and Filtering

Cheng¹,

Feng²,

Gui³

2019

Int J Performability Eng

View full text Add to dashboard Cite

show abstract

“…Presently, cloud computing has been adopted to explore collaborative filtering algorithm by many industries. Reference [8] proposed an improved collaborative filtering recommendation algorithm based on hadoop. Reference [9] designed a recommendation system for Ecommence based on hadoop.…”

mentioning

confidence: 99%

Personalized Recommendation Method of Power Information Operation and Maintenance Knowledge Based on Spark

Qu¹,

Xu²,

Ren³

2016

JCM

View full text Add to dashboard Cite

Power information operation and maintenance knowledge overload has become a pressing issue with the development of smart grid construction. The traditional personalized recommendation method cannot meet the demand of personalized recommendation of power information maintenance knowledge in big data environment. This paper proposes a method based on Spark which gives a personalized recommendation method of power information operation and maintenance knowledge. Firstly, an implicit rating mechanism is introduced, which can transform the learning behavior of users into implicit rating of power information operation and maintenance knowledge. Secondly, a personalized recommendation method combing knowledge features and user interests is designed. Finally, the personalized recommendation method, based on Spark, is applied to recommend power information operation and maintenance knowledge. The experimental results show that the method can effectively improve the accuracy and real-time of recommendation. Index Terms-Spark, power information operation and maintenance knowledge, personalized recommendation, implicit rating, collaborative filtering I. INTRODUCTION Power information operation and maintenance knowledge overload has become a pressing issue with the development of smart grid construction, which has made it difficult for users to find the knowledge that they really need from a great deal of power information operation and maintenance knowledge [1]. An effective way to enhance the power information operation and maintenance knowledge level of users is through personalized recommendation of power information operation and maintenance knowledge. This has great significance in ensuring the safe and stable operation of power enterprise information communication system. Collaborative filtering is widely used in e-commerce, social networking, video/music on demand and other fields, and is currently the most successful personalized recommendation method [2]. It recommends the

show abstract

Research on Improved Collaborative Filtering Recommendation Algorithm on Hadoop

Cited by 2 publications

References 0 publications

Collaborative Filtering Algorithm based on Data Mixing and Filtering

Collaborative Filtering Algorithm based on Data Mixing and Filtering

Personalized Recommendation Method of Power Information Operation and Maintenance Knowledge Based on Spark

Contact Info

Product

Resources

About