A Collaborative Filtering Algorithm Based on SVD and Trust Factor

The advent of the information age has led tothe availability of overwhelming choices of products to userswhich create need of various Recommendation Systems (RS). Recommendation System belongs to the methods of InformationRetrieval, Data Mining and Machine Learning algorithms. Multifaceted Recommendation System Engine (MF-RISE) helpthe users to get personalized recommendations, helps users to select correct product from wide range of products using userfeedback, ratings and reviews provided by users. In real-world scenarios, recommenders have many non-functionalrequirements of technical nature and must handle huge amountdata. Evaluation of Multifaceted Recommendation System Enginemust take these issues into account in order to be producemaximum useful recommendations. The many researchers have proposed a wide range of recommendationsystems algorithms. This study investigates there are threepopular existing types of recommendation systems algorithms, Collaborative Filtering (CF), Content-Based Filtering (CB), andHybrid Recommendation System. The MovieLens dataset with its3 variants was utilized for the purpose of this study. The studiedevaluation methods consider both quantitative and qualitativeaspects of algorithm with many evaluation parameters like meansquared error (MSE), root mean squared error (RMSE), Test Timeand Fit Time are calculated for each recommender algorithmimplementation. The study identifies the gaps and challengesfaced by every recommender algorithm. This study will alsohelp researchers to propose new recommendation algorithms by overcoming identified research gaps and challenges of existing algorithms.

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“…The singular value decomposition [15] is a method of decomposing a matrix into three other matrices.…”

Section: Matrix Factorization Algorithm [16]mentioning

confidence: 99%

MF-RISE : Benchmarking for Multifaceted Recommender System Engine

Mali

Mishra

Vijayalakshmi

2023

Preprint

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“…Such methods usually employ dimensionality reduction to extract latent features of users and items. For example, Wang et al [15] proposed an algorithm that combines the Singular Value Decomposition (SVD) technology with the trust model, which reduces the dimension of a high-dimensional sparse matrix and then improves the prediction accuracy by introducing a trust factor. However, SVD is too slow when decomposing data with dimensions above 1000, and it has certain limitations in the calculation of up to tens of millions of dimensions in real systems.…”

Section: Model-based Collaborativementioning

confidence: 99%

Deep Collaborative Filtering: A Recommendation Method for Crowdfunding Project Based on the Integration of Deep Neural Network and Collaborative Filtering

Yin

Wang

Zhao

et al. 2022

Mathematical Problems in Engineering

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In real recommendation systems, implicit feedback data is more common and easier to obtain, and recommendation algorithms based on such data will be more applicable. However, implicit feedback data cannot directly express user preferences. Meanwhile, data sparsity caused by massive data is still an urgent problem to be solved in recommendation system. In response to this phenomenon, this paper proposes a deep collaborative filtering algorithm. In the perspective of implicit feedback, this method uses the advantages of convolutional neural network for effective learning of the nonlinear interaction of users and items and the characteristics of collaborative filtering algorithm for modeling the linear interaction of users and items and combines the two methods for recommendation. Finally, the baseline method is set up and the comparative experiment and parameter adjustment is carried out. The experimental results show that the proposed algorithm has significantly improved the recommendation accuracy on public dataset (Yahoo! Movie). The parameter adjustment results show that, under the condition of uniformly collecting negative feedback data and setting a certain number of convolution layers, the sparser the data is, the better the recommendation performs. As a result, this paper has made some progress in solving the problem of data sparsity and enriching the research of recommendation system.

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“…Zhang et al [8] propose a new collaborative filtering recommendation algorithm that combines the technique of time window and rating prediction to estimate the preferences of the users without any rating items. Besides, the singular value and the trust factor are also considered in the calculation of the collaborative filtering algorithm [9], [10]. To meet the individual needs of users, the personalized recommendation algorithm combining probabilistic semantic clustering analysis and collaborative filtering is used to recommend the most relevant items to users [11].…”

Section: Introductionmentioning

confidence: 99%