The Impact of Basic Matrix Factorization Refinements on Recommendation Accuracy

2014 ◽  
Author(s):  
Parisa Lak ◽  
Bora Caglayan ◽  
Ayse Basar Bener
Keyword(s):  
Matrix Factorization ◽  
Basic Matrix ◽  
Recommendation Accuracy ◽  
The Impact

scholarly journals Retweet Prediction Based on User Behavior

2021 ◽  
Author(s):  
Syeda Nadia Firdaus
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Network ◽  
Prediction Model ◽  
Matrix Factorization ◽  
Information Diffusion ◽  
User Behavior ◽  
Past Research ◽  
The Difference ◽  
The Impact

Social network is a hot topic of interest for researchers in the field of computer science in recent years. These social networks such as Facebook, Twitter, Instagram play an important role in information diffusion. Social network data are created by its users. Users’ online activities and behavior have been studied in various past research efforts in order to get a better understanding on how information is diffused on social networks. In this study, we focus on Twitter and we explore the impact of user behavior on their retweet activity. To represent a user’s behavior for predicting their retweet decision, we introduce 10-dimentional emotion and 35-dimensional personality related features. We consider the difference of a user being an author and a retweeter in terms of their behaviors, and propose a machine learning based retweet prediction model considering this difference. We also propose two approaches for matrix factorization retweet prediction model which learns the latent relation between users and tweets to predict the user’s retweet decision. In the experiment, we have tested our proposed models. We find that models based on user behavior related features provide good improvement (3% - 6% in terms of F1- score) over baseline models. By only considering user’s behavior as a retweeter, the data processing time is reduced while the prediction accuracy is comparable to the case when both retweeting and posting behaviors are considered. In the proposed matrix factorization models, we include tweet features into the basic factorization model through newly defined regularization terms and improve the performance by 3% - 4% in terms of F1-score. Finally, we compare the performance of machine learning and matrix factorization models for retweet prediction and find that none of the models is superior to the other in all occasions. Therefore, different models should be used depending on how prediction results will be used. Machine learning model is preferable when a model’s performance quality is important such as for tweet re-ranking and tweet recommendation. Matrix factorization is a preferred option when model’s positive retweet prediction capability is more important such as for marketing campaign and finding potential retweeters.

scholarly journals Retweet Prediction Based on User Behavior

2021 ◽  
Author(s):  
Syeda Nadia Firdaus
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Network ◽  
Prediction Model ◽  
Matrix Factorization ◽  
Information Diffusion ◽  
User Behavior ◽  
Past Research ◽  
The Difference ◽  
The Impact

Social network is a hot topic of interest for researchers in the field of computer science in recent years. These social networks such as Facebook, Twitter, Instagram play an important role in information diffusion. Social network data are created by its users. Users’ online activities and behavior have been studied in various past research efforts in order to get a better understanding on how information is diffused on social networks. In this study, we focus on Twitter and we explore the impact of user behavior on their retweet activity. To represent a user’s behavior for predicting their retweet decision, we introduce 10-dimentional emotion and 35-dimensional personality related features. We consider the difference of a user being an author and a retweeter in terms of their behaviors, and propose a machine learning based retweet prediction model considering this difference. We also propose two approaches for matrix factorization retweet prediction model which learns the latent relation between users and tweets to predict the user’s retweet decision. In the experiment, we have tested our proposed models. We find that models based on user behavior related features provide good improvement (3% - 6% in terms of F1- score) over baseline models. By only considering user’s behavior as a retweeter, the data processing time is reduced while the prediction accuracy is comparable to the case when both retweeting and posting behaviors are considered. In the proposed matrix factorization models, we include tweet features into the basic factorization model through newly defined regularization terms and improve the performance by 3% - 4% in terms of F1-score. Finally, we compare the performance of machine learning and matrix factorization models for retweet prediction and find that none of the models is superior to the other in all occasions. Therefore, different models should be used depending on how prediction results will be used. Machine learning model is preferable when a model’s performance quality is important such as for tweet re-ranking and tweet recommendation. Matrix factorization is a preferred option when model’s positive retweet prediction capability is more important such as for marketing campaign and finding potential retweeters.

scholarly journals Learning User Dependencies for Recommendation

2017 ◽  
Author(s):  
Yong Liu ◽  
Peilin Zhao ◽  
Xin Liu ◽  
Min Wu ◽  
Lixin Duan ◽  
...  
Keyword(s):  
Social Relationships ◽  
Matrix Factorization ◽  
Optimization Problem ◽  
Specific Recommendation ◽  
Social Recommendation ◽  
Precision Matrix ◽  
Main Challenge ◽  
Social Recommender Systems ◽  
Latent Features ◽  
Recommendation Accuracy

Social recommender systems exploit users' social relationships to improve recommendation accuracy. Intuitively, a user tends to trust different people regarding with different scenarios. Therefore, one main challenge of social recommendation is to exploit the most appropriate dependencies between users for a given recommendation task. Previous social recommendation methods are usually developed based on pre-defined user dependencies. Thus, they may not be optimal for a specific recommendation task. In this paper, we propose a novel recommendation method, named probabilistic relational matrix factorization (PRMF), which can automatically learn the dependencies between users to improve recommendation accuracy. In PRMF, users' latent features are assumed to follow a matrix variate normal (MVN) distribution. Both positive and negative user dependencies can be modeled by the row precision matrix of the MVN distribution. Moreover, we also propose an alternating optimization algorithm to solve the optimization problem of PRMF. Extensive experiments on four real datasets have been performed to demonstrate the effectiveness of the proposed PRMF model.

Collaborative Filtering Recommendation Algorithm Integrated into Co-Rating Impact Factor

2014 ◽  
Vol 926-930 ◽  
pp. 3004-3007
Author(s):  
Xu Yang Wang ◽  
Heng Liu
Keyword(s):  
Impact Factor ◽  
Collaborative Filtering ◽  
Recommendation System ◽  
Nearest Neighbor ◽  
Recommendation Algorithm ◽  
Rating Data ◽  
Recommendation Accuracy ◽  
The Impact ◽  
Rating Information ◽  
Original Rating

The sparsity rating data is one of the main challenges of recommendation system. For this problem, we presented a collaborative filtering recommendation algorithm integrated into co-ratings impact factor. The method reduced the sparsity of rating matrix by filling the original rating matrix. It made the full use of rating information and took the impact on similarity of co-ratings between users into consideration when looking for the nearest neighbor so that the similarities were accurately computed. Experimental results showed that the proposed algorithm, to some extent, improved the recommendation accuracy.

scholarly journals Matrix Factorization Techniques for Context-Aware Collaborative Filtering Recommender Systems: A Survey

2018 ◽  
Vol 11 (2) ◽  
pp. 1 ◽  
Author(s):  
Mohamed Hussein Abdi ◽  
George Onyango Okeyo ◽  
Ronald Waweru Mwangi
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Matrix Factorization ◽  
Large Scale ◽  
User Preferences ◽  
Online Information ◽  
Context Aware ◽  
Preference Rating ◽  
Accessible Information ◽  
The Impact

Collaborative Filtering Recommender Systems predict user preferences for online information, products or services by learning from past user-item relationships. A predominant approach to Collaborative Filtering is Neighborhood-based, where a user-item preference rating is computed from ratings of similar items and/or users. This approach encounters data sparsity and scalability limitations as the volume of accessible information and the active users continue to grow leading to performance degradation, poor quality recommendations and inaccurate predictions. Despite these drawbacks, the problem of information overload has led to great interests in personalization techniques. The incorporation of context information and Matrix and Tensor Factorization techniques have proved to be a promising solution to some of these challenges. We conducted a focused review of literature in the areas of Context-aware Recommender Systems utilizing Matrix Factorization approaches. This survey paper presents a detailed literature review of Context-aware Recommender Systems and approaches to improving performance for large scale datasets and the impact of incorporating contextual information on the quality and accuracy of the recommendation. The results of this survey can be used as a basic reference for improving and optimizing existing Context-aware Collaborative Filtering based Recommender Systems. The main contribution of this paper is a survey of Matrix Factorization techniques for Context-aware Collaborative Filtering Recommender Systems. 

scholarly journals Analisis Karaterisasi Konsentrasi dan Komposisi Partikulat Udara (Studi Case : Surabaya)

2016 ◽  
Vol 2 (2) ◽  
pp. 97-103 ◽  
Author(s):  
Eka Fithriani Ahmad ◽  
Muhayatun Santoso
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Black Carbon ◽  
Matrix Factorization ◽  
Positive Matrix Factorization ◽  
Quality Standard ◽  
Receptor Modeling ◽  
Positive Matrix ◽  
Pm 2.5 ◽  
The Impact

Abstrak Pencemaran udara merupakan dampak yang sangat merugikan, tidak hanya bagi manusia tetapi juga akan berdampak buruk bagi ekosistem hewan dan tumbuhan. Pada penelitian ini akan mengkaji pencemaran udara dari Oktober 2012 hingga Februari 2014 melalui penelitian konsentrasi dan komposisi dari partikulat udara dengan ukuran PM 2.5. Penelitian ini bertujuan untuk menentuan sumber asal pencemaran di Surabaya sehingga dapat dijadikan referensi berbasis ilmiah sebagai langkah untuk membuat keputusan dan kebijakan yang tepat dalam menanggulangi dampak pencemaran. Metode pengolahan data dalam penelitian ini adalah dengan menggunakan analisis reseptor modeling yaitu Positif Matrix Factorization (PMF) untuk mengetahui sumber asal pencemaran. Hasil pengukuran yang diperoleh pada konsentrasi PM 2,5 adalah 15.05 μg/m3 sehingga telah melebihi baku mutu tahunan yang telah ditetapkan PP 41 tahun 1999, USEPA, maupun WHO. Dalam partikulat terdapat konsentrasi black carbon (BC) sebesar 3.20 μg/m3 dan unsur Pb dengan konsentrasi 0.28 μg/m3 yang telah melebihi nilai baku mutu USEPA. Sedangkan hasil analisis reseptor modeling di dapatkan sumber asal polutan berasal dari biomass, vehicle, soil, industri Pb, industri Zn dan indutri Fe. Kata kunci: Partikulat mater 2.5, black carbon, Pb, positive matrix factorization, Surabaya   Abstract Air pollution is a very adverse impact, not only for humans but also the ecosystem of plants and animals. This research examine air pollution from October 2012 until February 2014 through the research of concentration and composition of airborne particulates with a size of PM 2.5 μm. This study aims to determine the origin and location of pollution sources in Surabaya so that it can be used as scientific reference as a step to make the right decisions and policies in tackling the impact of pollution. Data processing method in this research used analysis of receptor modeling that is Positive Matrix Factorization (PMF) to determine the source of the pollution. Results obtained at a concentration of PM 2.5 was 15.05 μg/m3 so PM 2.5 has exceeded the quality standard yearly, based on PP 41 1999, USEPA and WHO. There are 3.20 μg/m3 concentration of black carbon (BC), element Pb in particulate matter with a concentration of 0.28 μg/m3 which has exceeded the value of the quality standard USEPA. The source of the pollutants come from biomass, vehicle, soil, industrial Pb, Zn and industries Fe industry.   Keywords: Particulate matter 2.5, black carbon, Pb, positive matrix factorization, Surabaya DOI: http://dx.doi.org/10.15408/jkv.v0i0.3602

scholarly journals A Review on Initialization Methods for Nonnegative Matrix Factorization: Towards Omics Data Experiments

Mathematics ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1006
Author(s):  
Flavia Esposito
Keyword(s):  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Biological Information ◽  
Relevant Role ◽  
Initialization Scheme ◽  
The Cost ◽  
The Impact ◽  
Omic Data

Nonnegative Matrix Factorization (NMF) has acquired a relevant role in the panorama of knowledge extraction, thanks to the peculiarity that non-negativity applies to both bases and weights, which allows meaningful interpretations and is consistent with the natural human part-based learning process. Nevertheless, most NMF algorithms are iterative, so initialization methods affect convergence behaviour, the quality of the final solution, and NMF performance in terms of the residual of the cost function. Studies on the impact of NMF initialization techniques have been conducted for text or image datasets, but very few considerations can be found in the literature when biological datasets are studied, even though NMFs have largely demonstrated their usefulness in better understanding biological mechanisms with omic datasets. This paper aims to present the state-of-the-art on NMF initialization schemes along with some initial considerations on the impact of initialization methods when microarrays (a simple instance of omic data) are evaluated with NMF mechanisms. Using a series of measures to qualitatively examine the biological information extracted by a given NMF scheme, it preliminary appears that some information (e.g., represented by genes) can be extracted regardless of the initialization scheme used.

scholarly journals From Reputation Perspective: A Hybrid Matrix Factorization for QoS Prediction in Location-Aware Mobile Service Recommendation System

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Shun Li ◽  
Junhao Wen ◽  
Xibin Wang
Keyword(s):  
Matrix Factorization ◽  
Recommendation System ◽  
Mobile Services ◽  
End Users ◽  
Mobile Service ◽  
Service Recommendation ◽  
Qos Prediction ◽  
Hybrid Matrix ◽  
Location Aware ◽  
The Impact

With the great development of mobile services, the Quality of Services (QoS) becomes an essential factor to meet end users’ personalized requirement on the nonfunctional performance of mobile services. However, most of the QoS values in real cases are unattainable because a service user would only invoke some specific mobile services. Therefore, how to predict the missing QoS values and recommend high-quality services to end users becomes a significant challenge in mobile service recommendation research. Previous QoS prediction researches demonstrate that the nonfunctional performance of mobile services is closely related to users’ location information. However, most location-aware QoS prediction methods ignore the premise that the obtainable QoS values observed by different users in same location region would probably be untrustworthy, which will lead to inaccurate and unreliable prediction results. To make credible location-aware QoS prediction, we propose a hybrid matrix factorization method integrated location and reputation information (LRMF) to predict the unattainable QoS values. Our approach firstly cluster users into different locational region based on their geographical distribution, and then we compute users’ reputation to identify untrustworthy users in every locational region. Finally, the unknown QoS values can be predicted by integrating locational cluster information and users’ reputation into a hybrid matrix factorization model. Comprehensive experiments are conducted on a public QoS dataset which contains sufficient real-world service invocation records. The evaluation results indicate that our LRMF method can effectively reduce the impact of unreliable users on QoS prediction and make credible mobile service recommendation.

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