scholarly journals Collaborative filtering recommendation algorithm based on user correlation and evolutionary clustering

2019 ◽  
Vol 6 (1) ◽  
pp. 147-156 ◽  
Author(s):  
Jianrui Chen ◽  
Chunxia Zhao ◽  
Uliji ◽  
Lifang Chen
Keyword(s):  
Dimension Reduction ◽  
Collaborative Filtering ◽  
Data Clustering ◽  
Real Life ◽  
Nearest Neighbors ◽  
User Group ◽  
Recommendation Algorithm ◽  
Evolutionary Clustering ◽  
The Relationship ◽  
Score Matrix

Abstract In recent years, application of recommendation algorithm in real life such as Amazon, Taobao is getting universal, but it is not perfect yet. A few problems need to be solved such as sparse data and low recommended accuracy. Collaborative filtering is a mature algorithm in the recommended systems, but there are still some problems. In this paper, a novel collaborative filtering recommendation algorithm based on user correlation and evolutionary clustering is presented. Firstly, score matrix is pre-processed with normalization and dimension reduction, to obtain denser score data. Based on these processed data, clustering principle is generated and dynamic evolutionary clustering is implemented. Secondly, the search for the nearest neighbors with highest similar interest is considered. A measurement about the relationship between users is proposed, called user correlation, which applies the satisfaction of users and the potential information. In each user group, user correlation is applied to choose the nearest neighbors to predict ratings. The proposed method is evaluated using the Movielens dataset. Diversity experimental results demonstrate that the proposed method has outstanding performance in predicted accuracy and recommended precision.

A Novel Collaborative Filtering Recommendation Approach Based on Field Authorities

2013 ◽  
Vol 765-767 ◽  
pp. 989-993
Author(s):  
Zhi Xue ◽  
Yao Xue Zhang ◽  
Yue Zhi Zhou ◽  
Wei Hu
Keyword(s):  
Collaborative Filtering ◽  
Prediction Accuracy ◽  
Word Of Mouth ◽  
Real Life ◽  
Recommendation Algorithm ◽  
Computational Overhead ◽  
External Data ◽  
Data Limitations ◽  
Accuracy Level ◽  
Filtering Approach

This paper presents a novel collaborative filtering recommendation algorithm based on field authorities which simulates the real life word of mouth recommendation mode. It uses the specialistic knowledge from field authorities of different genres, and successfully addresses data sparsity and noise problems existing in traditional collaborative filtering. Meanwhile it also improves prediction accuracy and saves computational overhead effectively. Experiments on MovieLens datasets show that the accuracy of our algorithm is significantly higher than collaborative filtering approach based on experts, and has larger scope because of no external data limitations. Meanwhile, compared to traditional k-NN collaborative filtering, our algorithm has a better performance both in MAE and precision experiments, and the computational overhead has a decrease of 19.2% while they provide the same accuracy level.

A Collaborative Filtering Recommendation Algorithm Based on User Clustering in E-Commerce Personalized Systems

2011 ◽  
Vol 267 ◽  
pp. 789-793 ◽  
Author(s):  
Guang Hua Cheng
Keyword(s):  
Collaborative Filtering ◽  
Nearest Neighbors ◽  
Recommendation Algorithm ◽  
Active User ◽  
User Clustering ◽  
Target User ◽  
Service Efficiency ◽  
Collaborative Filtering Algorithm ◽  
Two Stages ◽  
Successful Technology

Electronic commerce recommender systems are becoming increasingly popular with the evolution of the Internet, and collaborative filtering is the most successful technology for building recommendation systems. Unfortunately, the efficiency of this method declines linearly with the number of users and items. So, as the magnitudes of users and items grow rapidly, the result in the difficulty of the speed bottleneck of collaborative filtering systems. In order to raise service efficiency of the personalized systems, a collaborative filtering recommendation method based on clustering of users is presented. Users are clustered based on users ratings on items, then the nearest neighbors of target user can be found in the user clusters most similar to the target user. Based on the algorithm, the collaborative filtering algorithm should be divided into two stages, and it separates the procedure of recommendation into offline and online phases. In the offline phase, the basic users are clustered into centers; while in the online phase, the nearest neighbors of an active user are found according to the basic users’ cluster centers, and the recommendation to the active user is produced.

scholarly journals Fusion of Internal Similarity to Improve the Accuracy of Recommendation Algorithm

2022 ◽  
Vol 24 (1) ◽  
pp. 139-140
Author(s):  
Dr.S. Dhanabal ◽  
◽  
Dr.K. Baskar ◽  
R. Premkumar ◽  
◽  
...  
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Information Overload ◽  
Real Life ◽  
Similarity Matrix ◽  
Recommendation Algorithm ◽  
Simulation Experiments ◽  
Average Similarity ◽  
Recommendation Accuracy ◽  
Item Item

Collaborative filtering algorithms (CF) and mass diffusion (MD) algorithms have been successfully applied to recommender systems for years and can solve the problem of information overload. However, both algorithms suffer from data sparsity, and both tend to recommend popular products, which have poor diversity and are not suitable for real life. In this paper, we propose a user internal similarity-based recommendation algorithm (UISRC). UISRC first calculates the item-item similarity matrix and calculates the average similarity between items purchased by each user as the user’s internal similarity. The internal similarity of users is combined to modify the recommendation score to make score predictions and suggestions. Simulation experiments on RYM and Last.FM datasets, the results show that UISRC can obtain better recommendation accuracy and a variety of recommendations than traditional CF and MD algorithms.

scholarly journals Multi-dimensional Aggregation Recommendation Algorithm Based on Average Prediction

2018 ◽  
Vol 173 ◽  
pp. 03043
Author(s):  
Jiaxin Han ◽  
Wenjuan Wei ◽  
Haiyang Xia
Keyword(s):  
Collaborative Filtering ◽  
Aggregation Function ◽  
Recommendation Algorithm ◽  
Recommendation Accuracy ◽  
The Relationship

Traditional collaborative filtering recommendation algorithm uses single dimensional data to calculate the similarity between users or items, ignoring the user’s preference, thus affect the recommendation accuracy. To this end, an averaging forecasting based multi-dimensional aggregation recommendation algorithm was proposed in this paper, which constructs the relationship aggregation function by user’s total score and dimension scores firstly, then apply the aggregation function to the initial multi-dimensional score that calculated by the modified averaging forecasting algorithm. The experiment result shows that compared with the previous collaborative filtering based recommendation algorithm, it has higher recommendation accuracy.

scholarly journals An Improved Collaborative Filtering Recommendation Algorithm Based on Retroactive Inhibition Theory

2021 ◽  
Vol 11 (2) ◽  
pp. 843
Author(s):  
Nihong Yang ◽  
Lei Chen ◽  
Yuyu Yuan
Keyword(s):  
Collaborative Filtering ◽  
Proactive Inhibition ◽  
User Preferences ◽  
Retroactive Inhibition ◽  
Recommendation Algorithm ◽  
Collaborative Filtering Algorithm ◽  
Memory Inhibition ◽  
Real World Datasets ◽  
Sequential Models ◽  
The Relationship

Collaborative filtering (CF) is the most classical and widely used recommendation algorithm, which is mainly used to predict user preferences by mining the user’s historical data. CF algorithms can be divided into two main categories: user-based CF and item-based CF, which recommend items based on rating information from similar user profiles (user-based) or recommend items based on the similarity between items (item-based). However, since user’s preferences are not static, it is vital to take into account the changing preferences of users when making recommendations to achieve more accurate recommendations. In recent years, there have been studies using memory as a factor to measure changes in preference and exploring the retention of preference based on the relationship between the forgetting mechanism and time. Nevertheless, according to the theory of memory inhibition, the main factors that cause forgetting are retroactive inhibition and proactive inhibition, not mere evolutions over time. Therefore, our work proposed a method that combines the theory of retroactive inhibition and the traditional item-based CF algorithm (namely, RICF) to accurately explore the evolution of user preferences. Meanwhile, embedding training is introduced to represent the features better and alleviate the problem of data sparsity, and then the item embeddings are clustered to represent the preference points to measure the preference inhibition between different items. Moreover, we conducted experiments on real-world datasets to demonstrate the practicability of the proposed RICF. The experiments show that the RICF algorithm performs better and is more interpretable than the traditional item-based collaborative filtering algorithm, as well as the state-of-art sequential models such as LSTM and GRU.

Collaborative Filtering Recommendation Algorithm Applying Multi-Criteria Decision Making

2014 ◽  
Vol 926-930 ◽  
pp. 3379-3383
Author(s):  
Jiang Lu ◽  
Mu Qing Zhu ◽  
Wen Chao Wang
Keyword(s):  
Decision Making ◽  
Collaborative Filtering ◽  
Nearest Neighbors ◽  
Multi Criteria Decision Making ◽  
Recommendation Algorithm

In order to solve the problem of ignoring items’ attributes in single criteria collaborative filtering recommendation algorithm, a collaborative filtering recommendation algorithm based on multi-criteria decision making was proposed. Multi-criteria decision making was used during the computing of users’ similarity in the algorithm. By averaging all the similarities of sub evaluation with their weight, the algorithm chose the nearest neighbors. Finally, the algorithm used several nearest neighbors to get the recommendation results. Experiments show that the algorithm can find the nearest neighbors more similar after detailing evaluation, and make better predictions for the target users.

A Collaborative Filtering Recommendation Method Based on Item Category

2012 ◽  
Vol 605-607 ◽  
pp. 2430-2433
Author(s):  
Wei Bin Deng ◽  
Jin Liu
Keyword(s):  
Collaborative Filtering ◽  
Real Time ◽  
Recommender System ◽  
Nearest Neighbors ◽  
Search Space ◽  
User Preferences ◽  
Experimental Results ◽  
Recommendation Algorithm ◽  
Search Speed ◽  
Speed And Accuracy

Traditional collaborative filtering algorithms are facing severe challenges of sparse user rating and real-time recommendation. To solve the problems, the category structure of merchandise is analyzed deeply and a collaborative filtering recommendation algorithm based on item category is proposed. A smooth filling technique is used for rating matrix with user preferences and all users rating on the item to solve the sparse problem. A user has different interests on different category. For every item, the nearest neighbors are searched within the category of the item. Not only is the search space of the users’ neighbors reduced greatly, but also search speed and accuracy are promoted. The experimental results show that the method can efficiently improve the recommendation scalability and accuracy of the recommender system.

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