A Probabilistic Matrix Factorization Method for Link Sign Prediction in Social Networks

Performance Improvement of Stream-Centered Probabilistic Matrix Factorization Method Using Weighted Reservoir Sampling and Parallel Computing

International Journal on Electrical Engineering and Informatics ◽

10.15676/ijeei.2021.13.3.8 ◽

2021 ◽

Vol 13 (3) ◽

pp. 620-637

Author(s):

Berman Danyel Sinaga ◽

◽

Saiful Akbar ◽

Keyword(s):

Parallel Computing ◽

Performance Improvement ◽

Matrix Factorization ◽

Factorization Method ◽

Reservoir Sampling ◽

Probabilistic Matrix Factorization

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Recommendation system in social networks with topical attention and probabilistic matrix factorization

PLoS ONE ◽

10.1371/journal.pone.0223967 ◽

2019 ◽

Vol 14 (10) ◽

pp. e0223967 ◽

Cited By ~ 1

Author(s):

Weiwei Zhang ◽

Fangai Liu ◽

Daomeng Xu ◽

Lu Jiang

Keyword(s):

Social Networks ◽

Matrix Factorization ◽

Recommendation System ◽

Probabilistic Matrix Factorization

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Enhanced PMF Model to Predict User Interest for Web API Recommendation

10.4018/978-1-7998-7685-4.ch009 ◽

2022 ◽

pp. 131-139

Author(s):

T. Ramathulasi ◽

M. Rajasekhar Babu

Keyword(s):

Matrix Factorization ◽

Short Term Memory ◽

Contextual Information ◽

Factorization Method ◽

Experimental Results ◽

User Interest ◽

Pmf Model ◽

Probabilistic Matrix Factorization ◽

Memory Network ◽

Latent Attribute

Many methods focus solely on the relationship between the API and the user and fail to capture their contextual value. Because of this, they could not get better accuracy. The accuracy of the API recommendation can be improved by considering the effect of API contextual information on their latent attribute and the effect of the user time factor on the latent attribute of the user through the deep learning-based matrix factorization method (DL-PMF). In this chapter, a CNN (convolutional neural network) with an attention mechanism for the hidden features of web API elements and an LSTM (long-term and short-term memory) network is introduced to find the hidden features of service users. Finally, the authors combined PMF (probabilistic matrix factorization) to estimate the value of the recommended results. Experimental results obtained by the DL-PMF method show better than the experimental results obtained by the PMF and the ConvMF (convolutional matrix factorization) method in the recommended accuracy.

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Two-sided regularization model based on probabilistic matrix factorization and quantum similarity for recommender systems

International Journal of Modeling Simulation and Scientific Computing ◽

10.1142/s1793962320500567 ◽

2020 ◽

pp. 2050056

Author(s):

Waleed Reafee ◽

Marwa Alhazmi ◽

Naomie Salim

Keyword(s):

Matrix Factorization ◽

Side Information ◽

Cold Start ◽

Factorization Method ◽

Quantum Similarity ◽

Sources Of Information ◽

Social Recommendation ◽

Network Information ◽

Proposed Model ◽

Probabilistic Matrix Factorization

Nowadays, with the advent of the age of Web 2.0, several social recommendation methods that use social network information have been proposed and achieved distinct developments. However, the most critical challenges for the existing majority of these methods are: (1) They tend to utilize only the available social relation between users and deal just with the cold-start user issue. (2) Besides, these methods are suffering from the lack of exploitation of content information such as social tagging, which can provide various sources to extract the item information to overcome the cold-start item and improve the recommendation quality. In this paper, we investigated the efficiency of data fusion by integrating multi-source of information. First, two essential factors, user-side information, and item-side information, are identified. Second, we developed a novel social recommendation model called Two-Sided Regularization (TSR), which is based on the probabilistic matrix factorization method. Finally, the effective quantum-based similarity method is adapted to measure the similarity between users and between items into the proposed model. Experimental results on the real dataset show that our proposed model TSR addresses both of cold-start user and item issues and outperforms state-of-the-art recommendation methods. These results indicate the importance of incorporating various sources of information in the recommendation process.

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