A New Approach for Matrix Completion Using Arrow Relation of FCA in Recommender Systems

2020 ◽  
pp. 839-846
Author(s):  
G. Chemmalar Selvi ◽  
G. G. Lakshmi Priya
Keyword(s):  
Recommender Systems ◽  
Matrix Completion ◽  
New Approach

Neighborhood Evaluation in Recommender Systems Using the Realization Based Entropy Approach

2014 ◽  
Vol 1 (4) ◽  
pp. 34-50
Author(s):  
Roee Anuar ◽  
Yossi Bukchin ◽  
Oded Maimon ◽  
Lior Rokach
Keyword(s):  
Recommender Systems ◽  
Entropy Measure ◽  
Continuous Data ◽  
System Evaluation ◽  
Rating Data ◽  
New Approach ◽  
Selection Of ◽  
Classical Entropy ◽  
Better Than

The task of a recommender system evaluation has often been addressed in the literature, however there exists no consensus regarding the best metrics to assess its performance. This research deals with collaborative filtering recommendation systems, and proposes a new approach for evaluating the quality of neighbor selection. It theorizes that good recommendations emerge from good selection of neighbors. Hence, measuring the quality of the neighborhood may be used to predict the recommendation success. Since user neighborhoods in recommender systems are often sparse and differ in their rating range, this paper designs a novel measure to asses a neighborhood quality. First it builds the realization based entropy (RBE), which presents the classical entropy measure from a different angle. Next it modifies the RBE and propose the realization based distance entropy (RBDE), which considers also continuous data. Using the RBDE, it finally develops the consent entropy, which takes into account the absence of rating data. The paper compares the proposed approach with common approaches from the literature, using several recommendation evaluation metrics. It presents offline experiments using the Netflix database. The experimental results confirm that consent entropy performs better than commonly used metrics, particularly with high sparsity neighborhoods. This research is supported by The Israel Science Foundation, Grant #1362/10. This research is supported by NHECD EC, Grant #218639.

scholarly journals From Community Detection to Mentor Selection in Rating-Free Collaborative Filtering

2011 ◽  
Vol 2011 ◽  
pp. 1-19
Author(s):  
Armelle Brun ◽  
Sylvain Castagnos ◽  
Anne Boyer
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Community Detection ◽  
Local Community ◽  
State Of The Art ◽  
New Approach ◽  
Detection Algorithms ◽  
Local Community Detection ◽  
The Web ◽  
Filtering Approach

The number of items that users can now access when navigating on the Web is so huge that these might feel lost. Recommender systems are a way to cope with this profusion of data by suggesting items that fit the users needs. One of the most popular techniques for recommender systems is the collaborative filtering approach that relies on the preferences of items expressed by users, usually under the form of ratings. In the absence of ratings, classical collaborative filtering techniques cannot be applied. Fortunately, the behavior of users, such as their consultations, can be collected. In this paper, we present a new approach to perform collaborative filtering when no rating is available but when user consultations are known. We propose to take inspiration from local community detection algorithms to form communities of users and deduce the set of mentors of a given user. We adapt one state-of-the-art algorithm so as to fit the characteristics of collaborative filtering. Experiments conducted show that the precision achieved is higher then the baseline that does not perform any mentor selection. In addition, our model almost offsets the absence of ratings by exploiting a reduced set of mentors.

scholarly journals Towards Cognitive Recommender Systems

Algorithms ◽  
2020 ◽  
Vol 13 (8) ◽  
pp. 176
Author(s):  
Amin Beheshti ◽  
Shahpar Yakhchi ◽  
Salman Mousaeirad ◽  
Seyed Mohssen Ghafari ◽  
Srinivasa Reddy Goluguri ◽  
...  
Keyword(s):  
Recommender Systems ◽  
State Of The Art ◽  
User Preferences ◽  
Data Driven ◽  
New Approach ◽  
Domain Experts ◽  
Adaptive Mechanisms ◽  
New Type ◽  
Cognitive Activities ◽  
Time Aware

Intelligence is the ability to learn from experience and use domain experts’ knowledge to adapt to new situations. In this context, an intelligent Recommender System should be able to learn from domain experts’ knowledge and experience, as it is vital to know the domain that the items will be recommended. Traditionally, Recommender Systems have been recognized as playlist generators for video/music services (e.g., Netflix and Spotify), e-commerce product recommenders (e.g., Amazon and eBay), or social content recommenders (e.g., Facebook and Twitter). However, Recommender Systems in modern enterprises are highly data-/knowledge-driven and may rely on users’ cognitive aspects such as personality, behavior, and attitude. In this paper, we survey and summarize previously published studies on Recommender Systems to help readers understand our method’s contributions to the field in this context. We discuss the current limitations of the state of the art approaches in Recommender Systems and the need for our new approach: A vision and a general framework for a new type of data-driven, knowledge-driven, and cognition-driven Recommender Systems, namely, Cognitive Recommender Systems. Cognitive Recommender Systems will be the new type of intelligent Recommender Systems that understand the user’s preferences, detect changes in user preferences over time, predict user’s unknown favorites, and explore adaptive mechanisms to enable intelligent actions within the compound and changing environments. We present a motivating scenario in banking and argue that existing Recommender Systems: (i) do not use domain experts’ knowledge to adapt to new situations; (ii) may not be able to predict the ratings or preferences a customer would give to a product (e.g., loan, deposit, or trust service); and (iii) do not support data capture and analytics around customers’ cognitive activities and use it to provide intelligent and time-aware recommendations.

Three-way formal concept clustering technique for matrix completion in recommender system

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Chemmalar Selvi G. ◽  
Lakshmi Priya G.G.
Keyword(s):  
Recommender Systems ◽  
Error Rate ◽  
Prediction Accuracy ◽  
Matrix Completion ◽  
Sparse Data ◽  
Data Matrix ◽  
Formal Concept ◽  
Data Loss ◽  
Content Type ◽  
The Matrix

Purpose In today’s world, the recommender systems are very valuable systems for the online users, as the World Wide Web is loaded with plenty of available information causing the online users to spend more time and money. The recommender systems suggest some possible and relevant recommendation to the online users by applying the recommendation filtering techniques to the available source of information. The recommendation filtering techniques take the input data denoted as the matrix representation which is generally very sparse and high dimensional data in nature. Hence, the sparse data matrix is completed by filling the unknown or missing entries by using many matrix completion techniques. One of the most popular techniques used is the matrix factorization (MF) which aims to decompose the sparse data matrix into two new and small dimensional data matrix and whose dot product completes the matrix by filling the logical values. However, the MF technique failed to retain the loss of original information when it tried to decompose the matrix, and the error rate is relatively high which clearly shows the loss of such valuable information. Design/methodology/approach To alleviate the problem of data loss and data sparsity, the new algorithm from formal concept analysis (FCA), a mathematical model, is proposed for matrix completion which aims at filling the unknown or missing entries without loss of valuable information to a greater extent. The proposed matrix completion algorithm uses the clustering technique where the users who have commonly rated the items and have not commonly rated the items are captured into two classes. The matrix completion algorithm fills the mean cluster value of the unknown entries which well completes the matrix without actually decomposing the matrix. Findings The experiment was conducted on the available public data set, MovieLens, whose result shows the prediction error rate is minimal, and the comparison with the existing algorithms is also studied. Thus, the application of FCA in recommender systems proves minimum or no data loss and improvement in the prediction accuracy of rating score. Social implications The proposed matrix completion algorithm using FCA performs good recommendation which will be more useful for today’s online users in making decision with regard to the online purchasing of products. Originality/value This paper presents the new technique of matrix completion adopting the vital properties from FCA which is applied in the recommender systems. Hence, the proposed algorithm performs well when compared to other existing algorithms in terms of prediction accuracy.

scholarly journals A Survey of Latent Factor Models for Recommender Systems and Personalization

2021 ◽  
Author(s):  
Shalin Shah
Keyword(s):  
Recommender Systems ◽  
Matrix Factorization ◽  
Matrix Completion ◽  
Factor Models ◽  
Singular Value ◽  
Bayesian Personalized Ranking ◽  
Latent Factor Models ◽  
Value Decomposition ◽  
Personalized Ranking ◽  
Non Negative Matrix Factorization

<p>Recommender systems aim to personalize the experience of a user and are critical for businesses like retail portals, e-commerce websites, book sellers, streaming movie websites and so on. The earliest personalized algorithms use matrix factorization or matrix completion using algorithms like the singular value decomposition (SVD). There are other more advanced algorithms, like factorization machines, Bayesian personalized ranking (BPR), and a more recent Hebbian graph embeddings (HGE) algorithm. In this work, we implement BPR and HGE and compare our results with SVD, Non-negative matrix factorization (NMF) using the MovieLens dataset.</p>

scholarly journals STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender Systems

2019 ◽  
Author(s):  
Jiani Zhang ◽  
Xingjian Shi ◽  
Shenglin Zhao ◽  
Irwin King
Keyword(s):  
Recommender Systems ◽  
Matrix Completion ◽  
Model Space ◽  
Cold Start ◽  
Training Strategy ◽  
Convolutional Networks ◽  
Real World Datasets ◽  
Low Dimensional ◽  
Reconstructed Graph ◽  
Node Embeddings

We propose a new STAcked and Reconstructed Graph Convolutional Networks (STAR-GCN) architecture to learn node representations for boosting the performance in recommender systems, especially in the cold start scenario. STAR-GCN employs a stack of GCN encoder-decoders combined with intermediate supervision to improve the final prediction performance. Unlike the graph convolutional matrix completion model with one-hot encoding node inputs, our STAR-GCN learns low-dimensional user and item latent factors as the input to restrain the model space complexity. Moreover, our STAR-GCN can produce node embeddings for new nodes by reconstructing masked input node embeddings, which essentially tackles the cold start problem. Furthermore, we discover a label leakage issue when training GCN-based models for link prediction tasks and propose a training strategy to avoid the issue. Empirical results on multiple rating prediction benchmarks demonstrate our model achieves state-of-the-art performance in four out of five real-world datasets and significant improvements in predicting ratings in the cold start scenario. The code implementation is available in https://github.com/jennyzhang0215/STAR-GCN.

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