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.

scholarly journals Locus recommendation using probabilistic matrix factorization techniques

2021 ◽  
Vol 17 (1) ◽  
pp. 1-25
Author(s):  
Rachna Behl ◽  
Indu Kashyap
Keyword(s):  
Gamma Distribution ◽  
Gaussian Distribution ◽  
Real World ◽  
Matrix Factorization ◽  
Contextual Information ◽  
Experimental Results ◽  
International Institute ◽  
Rigorous Analysis ◽  
Location Recommendation ◽  
Probabilistic Matrix Factorization

Introduction: The present paper is the outcome of the research “Locus Recommendation using Probabilistic Matrix Factorization Techniques” carried out in Manav Rachna International Institute of Research and Studies, India in the year 2019-20.   Methodology: Matrix factorization is a model-based collaborative technique for recommending new items to the users.    Results: Experimental results on two real-world LBSNs showed that PFM consistently outperforms PMF. This is because the technique is based on gamma distribution to the model user and item matrix. Using gamma distribution is reasonable for check-in frequencies which are all positive in real datasets. However, PMF is based on Gaussian distribution that can allow negative frequency values as well.   Conclusion: The motive of the work is to identify the best technique for recommending locations with the highest accuracy and allow users to choose from a plethora of available locations; the best and interesting location based on the individual’s profile.   Originality: A rigorous analysis of Probabilistic Matrix Factorization techniques has been performed on popular LBSNs and the best technique for location recommendation has been identified by comparing the accuracy viz RMSE, Precision@N, Recall@N, F1@N of different models.   Limitations: User’s contextual information like demographics, social and geographical preferences have not been considered while evaluating the efficiency of probabilistic matrix factorization techniques for POI Recommendations.    

scholarly journals Recommendation Based on Trust Diffusion Model

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Jinfeng Yuan ◽  
Li Li
Keyword(s):  
Matrix Factorization ◽  
High Performance ◽  
Recommendation System ◽  
Online Information ◽  
Trust Network ◽  
Data Sparsity ◽  
Pmf Model ◽  
Probabilistic Matrix Factorization ◽  
Sparsity Problem ◽  
Relationship Of

Recommender system is emerging as a powerful and popular tool for online information relevant to a given user. The traditional recommendation system suffers from the cold start problem and the data sparsity problem. Many methods have been proposed to solve these problems, but few can achieve satisfactory efficiency. In this paper, we present a method which combines the trust diffusion (DiffTrust) algorithm and the probabilistic matrix factorization (PMF). DiffTrust is first used to study the possible diffusions of trust between various users. It is able to make use of the implicit relationship of the trust network, thus alleviating the data sparsity problem. The probabilistic matrix factorization (PMF) is then employed to combine the users' tastes with their trusted friends' interests. We evaluate the algorithm on Flixster, Moviedata, and Epinions datasets, respectively. The experimental results show that the recommendation based on our proposed DiffTrust + PMF model achieves high performance in terms of the root mean square error (RMSE), Recall, andFMeasure.

scholarly journals A Convolutional Neural Network and Matrix Factorization-Based Travel Location Recommendation Method Using Community-Contributed Geotagged Photos

2020 ◽  
Vol 9 (8) ◽  
pp. 464
Author(s):  
Thaair Ameen ◽  
Ling Chen ◽  
Zhenxing Xu ◽  
Dandan Lyu ◽  
Hongyu Shi
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Matrix Factorization ◽  
Factorization Method ◽  
Experimental Results ◽  
Latent Factor ◽  
Location Recommendation ◽  
Weighted Matrix ◽  
Geotagged Photos ◽  
Cold Start Problem

Travel location recommendation methods using community-contributed geotagged photos are based on past check-ins. Therefore, these methods cannot effectively work for new travel locations, i.e., they suffer from the travel location cold start problem. In this study, we propose a convolutional neural network and matrix factorization-based travel location recommendation method to address the problem. Specifically, a weighted matrix factorization method is used to obtain the latent factor representations of travel locations. The latent factor representation for a new travel location is estimated from its photos by using a convolutional neural network. Experimental results on a Flickr dataset demonstrate that the proposed method can provide better recommendations than existing methods.

Measurement and Analysis of the Functional Independence Measure Data by Using Nonnegative Matrix Factorization Method

2013 ◽  
Vol 718-720 ◽  
pp. 630-635 ◽  
Author(s):  
Naoki Yamamoto ◽  
Jun Murakami ◽  
Kei Fujii ◽  
Chiharu Okuma ◽  
Satoko Saito ◽  
...  
Keyword(s):  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Functional Independence Measure ◽  
Factorization Method ◽  
Functional Independence ◽  
Medical Data ◽  
Experimental Results ◽  
Measure Data ◽  
Measurement And Analysis

In this paper, we describe about a manner of adapting the nonnegative matrix factorization (NMF) method to the medical data, especially functional independence measure (FIM) data, and its experimental results. From the results which were obtained by applying the method to actually measured medical data in a hospital, we confirmed that the NMF method was effective to analyze the patients' characteristics related to disability and recovery tendency.

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.

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