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

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

Enhanced PMF Model to Predict User Interest for Web API Recommendation

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.

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

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.

Using Unified Probabilistic Matrix Factorization for Contextual Advertisement Recommendation

2014 ◽  
Vol 24 (3) ◽  
pp. 454-464 ◽  
Author(s):  
Dan-Dan TU ◽  
Cheng-Chun SHU ◽  
Hai-Yan YU
Keyword(s):  
Matrix Factorization ◽  
Probabilistic Matrix Factorization

scholarly journals Leveraging Latent Sentiment Constraint in Probabilistic Matrix Factorization for Cross-domain Sentiment Classification

2016 ◽  
Vol 80 ◽  
pp. 366-375 ◽  
Author(s):  
Jiguang Liang ◽  
Kai Zhang ◽  
Xiaofei Zhou ◽  
Yue Hu ◽  
Jianlong Tan ◽  
...  
Keyword(s):  
Matrix Factorization ◽  
Sentiment Classification ◽  
Cross Domain ◽  
Probabilistic Matrix Factorization
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