scholarly journals Deep Matrix Factorization Approach for Collaborative Filtering Recommender Systems

2020 ◽  
Vol 10 (14) ◽  
pp. 4926 ◽  
Author(s):  
Raúl Lara-Cabrera ◽  
Ángel González-Prieto ◽  
Fernando Ortega
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Matrix Factorization ◽  
Sources Of Information ◽  
Factorization Approach ◽  
Filtering Method ◽  
Layered Architecture ◽  
Fundamental Part ◽  
Many Sources

Providing useful information to the users by recommending highly demanded products and services is a fundamental part of the business of many top tier companies. Recommender Systems make use of many sources of information to provide users with accurate predictions and novel recommendations of items. Here we propose, DeepMF, a novel collaborative filtering method that combines the Deep Learning paradigm with Matrix Factorization (MF) to improve the quality of both predictions and recommendations made to the user. Specifically, DeepMF performs successive refinements of a MF model with a layered architecture that uses the acquired knowledge in a layer as input for subsequent layers. Experimental results showed that the quality of both the predictions and recommendations of DeepMF overcome the baselines.

USING COLLABORATIVE FILTERING FOR DEALING WITH MISSING VALUES IN NUCLEAR SAFEGUARDS EVALUATION

2010 ◽  
Vol 18 (04) ◽  
pp. 431-449 ◽  
Author(s):  
ROSA M. RODRÍGUEZ ◽  
LUIS MARTÍNEZ ◽  
DA RUAN ◽  
JUN LIU
Keyword(s):  
Collaborative Filtering ◽  
Missing Values ◽  
Evaluation Process ◽  
Nuclear Safeguards ◽  
Sources Of Information ◽  
Energy Agency ◽  
Trust Measure ◽  
Many Sources ◽  
Linguistic Approach ◽  
Fuzzy Linguistic

Nuclear safeguards evaluation aims to verify that countries are not misusing nuclear programs for nuclear weapons purposes. Experts of the International Atomic Energy Agency (IAEA) carry out an evaluation process in which several hundreds of indicators are assessed according to the information obtained from different sources, such as State declarations, on-site inspections, IAEA non-safeguards databases and other open sources. These assessments are synthesized in a hierarchical way to obtain a global assessment. Much information and many sources of information related to nuclear safeguards are vague, imprecise and ill-defined. The use of the fuzzy linguistic approach has provided good results to deal with such uncertainties in this type of problems. However, a new challenge on nuclear safeguards evaluation has attracted the attention of researchers. Due to the complexity and vagueness of the sources of information obtained by IAEA experts and the huge number of indicators involved in the problem, it is common that they cannot assess all of them appearing missing values in the evaluation, which can bias the nuclear safeguards results. This paper proposes a model based on collaborative filtering (CF) techniques to impute missing values and provides a trust measure that indicates the reliability of the nuclear safeguards evaluation with the imputed values.

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.

scholarly journals Syn Rec: A Prediction Technique using Collaborative Filtering and Synergy Score

2019 ◽  
Vol 8 (5S3) ◽  
pp. 457-463
Keyword(s):  
Social Interactions ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Effective Technique ◽  
Novel Technique ◽  
Trust Relation ◽  
Prediction Technique ◽  
User Trust

Recommender systems are techniques designed to produce personalized recommendations. Data sparsity, scalability cold start and quality of prediction are some of the problems faced by a recommender system. Traditional recommender systems consider that all the users are independent and identical, its an assumption which leads to a total ignorance of social interactions and trust among user. Trust relation among users ease the work of recommender systems to produce better quality of recommendations. In this paper, an effective technique is proposed using trust factor extracted with help of ratings given so that quality can be improved and better predictions can be done. A novel-technique has been proposed for recommender system using film-trust dataset and its effectiveness has been justified with the help of experiments.

An Effective Recommender System Based on Clustering Technique for TED Talks

2020 ◽  
Vol 15 (1) ◽  
pp. 35-51 ◽  
Author(s):  
Faiz Maazouzi ◽  
Hafed Zarzour ◽  
Yaser Jararweh
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Information Overload ◽  
State Of The Art ◽  
Pearson Correlation ◽  
Enormous Amount ◽  
Powerful Mechanism ◽  
Ted Talks

With the enormous amount of information circulating on the Web, it is becoming increasingly difficult to find the necessary and useful information quickly and efficiently. However, with the emergence of recommender systems in the 1990s, reducing information overload became easy. In the last few years, many recommender systems employ the collaborative filtering technology, which has been proven to be one of the most successful techniques in recommender systems. Nowadays, the latest generation of collaborative filtering methods still requires further improvements to make the recommendations more efficient and accurate. Therefore, the objective of this article is to propose a new effective recommender system for TED talks that first groups users according to their preferences, and then provides a powerful mechanism to improve the quality of recommendations for users. In this context, the authors used the Pearson Correlation Coefficient (PCC) method and TED talks to create the TED user-user matrix. Then, they used the k-means clustering method to group the same users in clusters and create a predictive model. Finally, they used this model to make relevant recommendations to other users. The experimental results on real dataset show that their approach significantly outperforms the state-of-the-art methods in terms of RMSE, precision, recall, and F1 scores.

scholarly journals Enhancing Collaborative Filtering by User-User Covariance Matrix

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Yingyuan Xiao ◽  
Jingjing Shi ◽  
Wenguang Zheng ◽  
Hongya Wang ◽  
Ching-Hsien Hsu
Keyword(s):  
Collaborative Filtering ◽  
Covariance Matrix ◽  
Personalized Recommendation ◽  
Similarity Matrix ◽  
User Similarity ◽  
Filtering Method ◽  
The Past ◽  
Changing Trends ◽  
Over Time

The collaborative filtering (CF) approach is one of the most successful personalized recommendation methods so far, which is employed by the majority of personalized recommender systems to predict users’ preferences or interests. The basic idea of CF is that if users had the same interests in the past they will also have similar tastes in the future. In general, the traditional CF may suffer the following problems: (1) The recommendation quality of CF based system is greatly affected by the sparsity of data. (2) The traditional CF is relatively difficult to adapt the situation that users’ preferences always change over time. (3) CF based approaches are used to recommend similar items to a user ignoring the user’s demand for variety. In this paper, to solve the above problems we build a new user-user covariance matrix to replace the traditional CF’s user-user similarity matrix. Compared with the user-user similarity matrix, the user-user covariance matrix introduces the user-user covariance to finely describe the changing trends of users’ interests. Furthermore, we propose an enhancing collaborative filtering method based on the user-user covariance matrix. The experimental results show that the proposed method can significantly improve the diversity of recommendation results and ensure the good recommendation precision.

Mining intrinsic information by matrix factorization-based approaches for collaborative filtering in recommender systems

2017 ◽  
Vol 249 ◽  
pp. 48-63 ◽  
Author(s):  
Yangyang Li ◽  
Dong Wang ◽  
Haiyang He ◽  
Licheng Jiao ◽  
Yu Xue
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Matrix Factorization
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