Efficient recommendation methods using category experts for a large dataset

Hwang, Won Seok; Lee, Ho Jong; Kim, Sang Wook; Won, Youngjoon; Lee, Min Soo

doi:10.1016/j.inffus.2015.07.005

Cited by 33 publications

(11 citation statements)

References 26 publications

(26 reference statements)

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“…This paper makes use of public dataset Ciao and FilmTrust with user trust relationships for experimental verification [5,50].…”

Section: Datasetmentioning

confidence: 99%

A Low-Rank Tensor Factorization Using Implicit Similarity in Trust Relationships

Ma¹,

Wang²,

Qin³

2020

Symmetry

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Low-rank tensor factorization can not only mine the implicit relationships between data but also fill in the missing data when working with complex data. Compared with the traditional collaborative filtering (CF) algorithm, the changes are essentially proposed, from traditional matrix analysis to three-dimensional spatial analysis. Based on low-rank tensor factorization, this paper proposes a recommendation model that comprehensively considers local information and global information, in other words, combining the similarity between trust users and low-rank tensor factorization. First, the similarity between trusted users is measured to capture local information between users by trusting similar preferences of users when selecting items. Then, the users’ similarity is integrated into the tensor, and the low-rank tensor factorization is used to better maintain and describe the internal structure of the data to obtain global information. Furthermore, based on the idea of the alternating least squares method, the conjugate gradient (CG) optimization algorithm for the model of this paper is designed. The local and global information is used to generate the optimal expected result in an iterative process. Finally, we conducted a large number of comparative experiments on the Ciao dataset and the FilmTrust dataset. Experimental results show that the algorithm has less precision loss under the data set with lower density. Thus, not only can a perfect compromise between accuracy and coverage be achieved, but also the computational complexity can be reduced to meet the need for real-time results.

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“…This paper makes use of public dataset Ciao and FilmTrust with user trust relationships for experimental verification [5,50].…”

Section: Datasetmentioning

confidence: 99%

A Low-Rank Tensor Factorization Using Implicit Similarity in Trust Relationships

Ma¹,

Wang²,

Qin³

2020

Symmetry

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“…In [29], Ranking SVM is applied to rank the experts by using pairwise approach to rank and predict the candidates. In [30] an evaluation of Learning to Rank algorithms is proposed for expert search on the DBLP database. In [31] a supervised learning approach is proposed to aggregate ranking and apply the same to search the experts and their blogs.…”

Section: Expert Based Recommender Systemsmentioning

confidence: 99%

Personalized Recommender by Exploiting Domain based Expert for Enhancing Collaborative Filtering Algorithm :PReC

Sridevi¹,

Rao²

2019

ijacsa

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The large amount of information available on the internet initiated various Recommender algorithms to act as an intermediate between number of choices and internet users. Collaborative filtering is one of the most traditional and intensively used recommendation approaches for many commercial services. Despite providing satisfying outcomes, it does have some issues that include source diversity, reliability, sparsity of data, scalability and cold start. Thus, there is a need for further improvement in the current generation of recommender system to achieve a more effective human decision support in a wide variety of applications and scenarios. Personalized Expert based collaborative filtering (PReC) approach is proposed to identify domain specific experts and the use of experts preference enhanced the performance of collaborative filtering recommender systems. A unified framework is proposed that integrates similar users rating data, experts rating and demographic data to reduce the number of pairwise computations from the search space to ensure scalability and enabled fine grained recommendations. The proposed method is evaluated using accuracy metrics MAE, RMSE on the data set collected from MovieLens datasets.

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“…In general, collaborative filtering methods are categorized into two types: memory-based and model-based ones [1]. First, memory-based methods [2][23] [7] predict the ratings of a user using the similarity of her neighborhoods, and recommend the items with high ratings.…”

Section: Related Workmentioning

confidence: 99%

Data imputation using a trust network for recommendation via matrix factorization

Hwang

Kim

et al. 2018

COMSIS J

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Existing recommendation methods suffer from the data sparsity problem which means that most of users have rated only a very small number of items, often resulting in low recommendation accuracy. In addition, for cold start users evaluating only few items, rating predictions with the methods also produce low accuracy. To address these problems, we propose a novel data imputation method that effectively substitutes missing ratings with probable values (i.e., imputed values). Our method successfully improves accuracy of recommendation methods from the following three aspects: (1) exploiting a trust network, (2) imputing only a part of missing ratings, and (3) applying them to any recommendation methods. Our method employs a bidirectional connection structure within a distance level for finding reliable users in exploiting a trust network as useful information. In addition, our method imputes only some missing ratings, called fillable ratings, whose imputed values are expected to be accurate with a sufficient level of confidence. Moreover, our imputation method is independent of, thus applicable to, any recommendation methods that may include application-specific ones and the most accurate one in each domain. We conduct experiments on three real-life datasets which arise from Epinions and Ciao. Our experimental results demonstrate that our method has recommendation accuracy better than existing recommendation methods equipped with imputation methods or trust networks, especially for cold start users.

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Efficient recommendation methods using category experts for a large dataset

Cited by 33 publications

References 26 publications

A Low-Rank Tensor Factorization Using Implicit Similarity in Trust Relationships

A Low-Rank Tensor Factorization Using Implicit Similarity in Trust Relationships

Personalized Recommender by Exploiting Domain based Expert for Enhancing Collaborative Filtering Algorithm :PReC

Data imputation using a trust network for recommendation via matrix factorization

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