scholarly journals UARR: A Novel Similarity Measure for Collaborative Filtering Recommendation

2013 ◽  
Vol 13 (Special-Issue) ◽  
pp. 122-130
Author(s):  
Yue Huang ◽  
Xuedong Gao ◽  
Shujuan Gu
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Similarity Measure ◽  
Accurate Prediction ◽  
Similarity Measurement ◽  
Data Set ◽  
Rating Data ◽  
User Similarity ◽  
Level Difference

Abstract User similarity measurement plays a key role in collaborative filtering recommendation which is the most widely applied technique in recommender systems. Traditional user-based collaborative filtering recommendation methods focus on absolute rating difference of common rated items while neglecting the relative rating level difference to the same items. In order to overcome this drawback, we propose a novel user similarity measure which takes into account the degree of rating the level gap that users could accept. The results of collaborative filtering recommendation based on User Acceptable Rating Radius (UARR) on a real movie rating data set, the MovieLens data set, prove to generate more accurate prediction results compared to the traditional similarity methods.

scholarly journals Collaborative Filtering Recommendation Algorithm Based on TF-IDF and User Characteristics

2021 ◽  
Vol 11 (20) ◽  
pp. 9554
Author(s):  
Jianjun Ni ◽  
Yu Cai ◽  
Guangyi Tang ◽  
Yingjuan Xie
Keyword(s):  
Collaborative Filtering ◽  
User Characteristics ◽  
Recommendation Algorithm ◽  
Data Set ◽  
Rating Data ◽  
User Similarity ◽  
Public Data ◽  
Weighted Algorithm ◽  
Collaborative Filtering Algorithm ◽  
The Impact

The recommendation algorithm is a very important and challenging issue for a personal recommender system. The collaborative filtering recommendation algorithm is one of the most popular and effective recommendation algorithms. However, the traditional collaborative filtering recommendation algorithm does not fully consider the impact of popular items and user characteristics on the recommendation results. To solve these problems, an improved collaborative filtering algorithm is proposed, which is based on the Term Frequency-Inverse Document Frequency (TF-IDF) method and user characteristics. In the proposed algorithm, an improved TF-IDF method is used to calculate the user similarity on the basis of rating data first. Secondly, the multi-dimensional characteristics information of users is used to calculate the user similarity by a fuzzy membership method. Then, the above two user similarities are fused based on an adaptive weighted algorithm. Finally, some experiments are conducted on the movie public data set, and the experimental results show that the proposed method has better performance than that of the state of the art.

scholarly journals A New Similarity Measure for User-based Collaborative Filtering in Recommender Systems

2015 ◽  
Vol 14 (9) ◽  
pp. 6118-6128 ◽  
Author(s):  
T. Srikanth ◽  
M. Shashi
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Similarity Measure ◽  
Recommender System ◽  
Distance Measure ◽  
Current User ◽  
Challenging Problem ◽  
Popular Approach ◽  
Similarity Calculation

Collaborative filtering is a popular approach in recommender Systems that helps users in identifying the items they may like in a wagon of items. Finding similarity among users with the available item ratings so as to predict rating(s) for unseen item(s) based on the preferences of likeminded users for the current user is a challenging problem. Traditional measures like Cosine similarity and Pearson correlation’s correlation exhibit some drawbacks in similarity calculation. This paper presents a new similarity measure which improves the performance of Recommender System. Experimental results on MovieLens dataset show that our proposed distance measure improves the quality of prediction. We present clustering results as an extension to validate the effectiveness of our proposed method.

Detecting Anomalous Ratings in Collaborative Filtering Recommender Systems

2016 ◽  
Vol 8 (2) ◽  
pp. 16-26 ◽  
Author(s):  
Zhihai Yang ◽  
Zhongmin Cai
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Graph Mining ◽  
Scale Up ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Rating Systems ◽  
Rating Data ◽  
Online Rating ◽  
Two Stages

Online rating data is ubiquitous on existing popular E-commerce websites such as Amazon, Yelp etc., which influences deeply the following customer choices about products used by E-businessman. Collaborative filtering recommender systems (CFRSs) play crucial role in rating systems. Since CFRSs are highly vulnerable to “shilling” attacks, it is common occurrence that attackers contaminate the rating systems with malicious rates to achieve their attack intentions. Despite detection methods based on such attacks have received much attention, the problem of detection accuracy remains largely unsolved. Moreover, few can scale up to handle large networks. This paper proposes a fast and effective detection method which combines two stages to find out abnormal users. Firstly, the manuscript employs a graph mining method to spot automatically suspicious nodes in a constructed graph with millions of nodes. And then, this manuscript continue to determine abnormal users by exploiting suspected target items based on the result of first stage. Experiments evaluate the effectiveness of the method.

scholarly journals Cognitive Similarity-Based Collaborative Filtering Recommendation System

2020 ◽  
Vol 10 (12) ◽  
pp. 4183 ◽  
Author(s):  
Luong Vuong Nguyen ◽  
Min-Sung Hong ◽  
Jason J. Jung ◽  
Bong-Soo Sohn
Keyword(s):  
Performance Evaluation ◽  
Collaborative Filtering ◽  
Similarity Measure ◽  
Recommendation System ◽  
Pearson Correlation ◽  
Cognitive Network ◽  
Data Set ◽  
New Approach ◽  
Layered Architecture ◽  
F Measure

This paper provides a new approach that improves collaborative filtering results in recommendation systems. In particular, we aim to ensure the reliability of the data set collected which is to collect the cognition about the item similarity from the users. Hence, in this work, we collect the cognitive similarity of the user about similar movies. Besides, we introduce a three-layered architecture that consists of the network between the items (item layer), the network between the cognitive similarity of users (cognition layer) and the network between users occurring in their cognitive similarity (user layer). For instance, the similarity in the cognitive network can be extracted from a similarity measure on the item network. In order to evaluate our method, we conducted experiments in the movie domain. In addition, for better performance evaluation, we use the F-measure that is a combination of two criteria P r e c i s i o n and R e c a l l . Compared with the Pearson Correlation, our method more accurate and achieves improvement over the baseline 11.1% in the best case. The result shows that our method achieved consistent improvement of 1.8% to 3.2% for various neighborhood sizes in MAE calculation, and from 2.0% to 4.1% in RMSE calculation. This indicates that our method improves recommendation performance.

A Novel Similarity Measure Based on Weighted Bipartite Network for Collaborative Filtering Recommendation

2012 ◽  
Vol 263-266 ◽  
pp. 1834-1837 ◽  
Author(s):  
Jian Xun Xia ◽  
Fei Wu ◽  
Chang Sheng Xie
Keyword(s):  
Collaborative Filtering ◽  
Pearson Correlation ◽  
Bipartite Network ◽  
Similarity Matrix ◽  
Data Set ◽  
User Similarity ◽  
Novel Approach ◽  
Weighted Matrix ◽  
Recommendation Algorithms ◽  
Recommendation Accuracy

This paper presents a novel approach to compute user similarity based on weighted bipartite network and resource allocation principle for collaborative filtering recommendation. The key is to calculate the asymmetric user weighted matrix and translate it into a symmetric user similarity matrix. We carry out extensive experiments over Movielens data set and demonstrate that the proposed approach can yield better recommendation accuracy and can partly to alleviate the trouble of sparseness. Compare with traditional collaborative filtering recommendation algorithms based on Pearson correlation similarity and adjusted cosine similarity, the proposed method can improve the average predication accuracy by 6.7% and 0.6% respectively.

EXPERIMENTAL ANALYSIS OF DESIGN CHOICES IN MULTIATTRIBUTE UTILITY COLLABORATIVE FILTERING

2007 ◽  
Vol 21 (02) ◽  
pp. 311-331 ◽  
Author(s):  
NIKOS MANOUSELIS ◽  
CONSTANTINA COSTOPOULOU
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Experimental Analysis ◽  
Real Life ◽  
Multiattribute Utility ◽  
Numerous Application ◽  
Data Set ◽  
Recommendation Algorithms ◽  
Actual Operation ◽  
Design Options

Recommender systems have already been engaging multiple criteria for the production of recommendations. Such systems, referred to as multicriteria recommenders, demonstrated early the potential of applying Multi-Criteria Decision Making (MCDM) methods to facilitate recommendation in numerous application domains. On the other hand, systematic implementation and testing of multicriteria recommender systems in the context of real-life applications still remains rather limited. Previous studies dealing with the evaluation of recommender systems have outlined the importance of carrying out careful testing and parameterization of a recommender system, before it is actually deployed in a real setting. In this paper, the experimental analysis of several design options for three proposed multiattribute utility collaborative filtering algorithms is presented for a particular application context (recommendation of e-markets to online customers), under conditions similar to the ones expected during actual operation. The results of this study indicate that the performance of recommendation algorithms depends on the characteristics of the application context, as these are reflected on the properties of evaluations' data set. Therefore, it is judged important to experimentally analyze various design choices for multicriteria recommender systems, before their actual deployment.

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