Recommender System using Distributed Improved Predictive Framework with Matrix Factorization and Random Forest

doi:10.22266/ijies2022.0831.40

Cited by 2 publications

(4 citation statements)

References 21 publications

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“…In this section, the DIDPMF model is executed in MATLAB 2017b to analyze its efficiency and compare to the existing models include DIPMF [8], DIPMI [8], DPMF [7], DPMI [7] and DPM [7] models. In this experiment, the products from Trip Advisor and Amazon datasets are used to reorganize and suggest the products to the clients depending on estimation of its rating characteristics.…”

Section: Resultsmentioning

confidence: 99%

“…As a result, a DIPMF model [8] has been developed which enhances the prediction efficiency by assessing the aspects of social context and their dynamic response of each individual for every product. The key purpose of DIPMF was to merge the information from the individual desires, choices and social context.…”

Section: International Journal On Recent and Innovationmentioning

confidence: 99%

“…The main purpose of DIDPMF is taking the advantages of DIPMI [8] with MF and fDNN models for improving the recommendation quality. First, all identified ratings 𝑟 𝑢,𝑐,𝑖 > 0 in 𝐷 𝑡𝑟𝑎𝑖𝑛 are defined as attributes and labels.…”

Section: B Didpmf: Distributed Improved Deep Prediction With Mf and Fdnnmentioning

confidence: 99%

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Distributed Improved Deep Prediction for Recommender System using an Ensemble Learning

Muruganantham

Shanmugasundaram²

2023

IJRITCC

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If online businesses possess valuable interest for suggesting their items by scoring them, then digital advertising gains their profits depending on their promotions or marketing task. Web users cannot be certain that the products handled via big-data recommendation are either advanced or interesting to their needs. In recent decades, recommender system models have been widely used to analyses large quantities of information. Amongst, a Distributed Improved Prediction with Matrix Factorization (MF) and Random Forest (RF) called DIPMF model exploits individual’s desires, choices and social context together for predicting the ratings of a particular item. But, the RF scheme needs high computation power and time for learning process. Also, its outcome was influenced by the training parameters. Hence this article proposes a Distributed Improved Deep Prediction with MF and ensemble learning (DIDPMF) model is proposed to decrease the computational difficulty of RF learning and increasing the efficiency of rating prediction. In this DIDPMF, a forest attribute extractor is ensemble with the Deep Neural Network (fDNN) for extracting the sparse attribute correlations from an extremely large attribute space. So, incorporating RF over DNN has the ability to provide prediction outcomes from all its base trainers instead of a single estimated possibility rate. This fDNN encompasses forest module and DNN module. The forest module is employed as an attribute extractor to extract the sparse representations from the given raw input data with the supervision of learning outcomes. First, independent decision trees are constructed and then ensemble those trees to obtain the forest. After, this forest is fed to the DNN module which acts as a learner to predict the individual’s ratings with the aid of novel attribute representations. Finally, the experimental results reveal that the DIDPMF outperforms than the other conventional recommender systems.

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Section: Resultsmentioning

confidence: 99%

Section: International Journal On Recent and Innovationmentioning

confidence: 99%

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Distributed Improved Deep Prediction for Recommender System using an Ensemble Learning

Muruganantham

Shanmugasundaram²

2023

IJRITCC

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“…The rankingbased system aims to provide users with Top-K personalized and sorted items according to their interests and preferences. Several state-of-the-art methods leverage matrix factorization (MF) techniques, including singular value decomposition (SVD), to learn efficient ranked lists of items for users [1,4]. However, in real-world applications, the user-item interaction matrices are sparse, with only a few observed rating values given by users on items [2,3]; hence using an MF technique to linearly model interaction data directly on a sparse rating matrix cannot deal with the non-linear and complex intrinsic structure of user and item latent features [2].…”

Section: Introductionmentioning

confidence: 99%

“Guess Why I didn't rate it”: A New Preference-based Model for Enhanced TopK Recommendation

2023

IJIES

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In existing matrix factorization (MF)-based recommender systems, the user-item interaction matrix is factorized linearly into two low-ranked feature matrices to generate predictions or provide the users with personalized rankings of items. However, when MF methods are directly applied to sparse rating matrices, they cannot cope with the inherent structure of real-world user and item latent features. The efficiency of MF-based systems crucially depends on their capability to address sparsity issues. To this end, we propose, in this paper, a novel preference-based data imputation approach for effective MF-based Top-K recommendation. We apply MF on an imputed and denser rating matrix with only interesting items to users. We obtain these items by inferring the prior preferences of users, considering some biases that may impact their choices for items they interact with, and leveraging a powerful latent and non-linear feature extraction using a deep generative model. Experimental results on two real-world sparse datasets reveal that the proposed model significantly enhances the performance of Top-K recommendations and outperforms baselines with an average improvement margin of 6.45% and 3.91% in hit rate (HR) and normalized discounted cumulative gain (NDCG) evaluation metrics, respectively, averaging on all employed datasets.

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Recommender System using Distributed Improved Predictive Framework with Matrix Factorization and Random Forest

Cited by 2 publications

References 21 publications

Distributed Improved Deep Prediction for Recommender System using an Ensemble Learning

Distributed Improved Deep Prediction for Recommender System using an Ensemble Learning

“Guess Why I didn't rate it”: A New Preference-based Model for Enhanced TopK Recommendation

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