Tensor-based item recommendation using probabilistic ranking in social tagging systems

Ifada, Noor; Nayak, Richi

doi:10.1145/2567948.2579243

Cited by 20 publications

(15 citation statements)

References 18 publications

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“…In their research, the user's social background was not accounted for. Ifada and Nayak [34] proposed a scalable tensor recommendation based on probability ordering and block parallel matrix multiplication. When new users and new items are added to the system, the system can generate an approximate tensor.…”

Section: Low-rank Tensor Factorizationmentioning

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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Section: Low-rank Tensor Factorizationmentioning

confidence: 99%

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

Ma¹,

Wang²,

Qin³

2020

Symmetry

View full text Add to dashboard Cite

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“…They split a tensor into several sub-tensors for mitigating the sparsity problem. In the study of [9], the ranking scheme of the recommendation cantidates and the scalability in the tensor reconstruction are investigated. They argued that the result of the tensor reconstruction disregards the user's past behaviors.…”

Section: Related Workmentioning

confidence: 99%

“…Then, the top k items among the candidates are recommended to the user. However, the research of Ifada et al [9] argued that the previous tagging behaviors of the users are not considered in this approach. The fundamental idea under the latent factor model is that the item selection of a user is controlled by a few features [39], while the preferences of the user are explained by characterizing the user profiles and user's item consuming patterns such as gender, actors, or genres.…”

Section: Bm25 Based Candidate Rankingmentioning

confidence: 99%

“…Tensor modeling is well-known method for solving this problem. The user's tag assignment can be modeled as a tensor, which is an n-dimensional array, and the latent relationships can be derived using the tensor factorization [8], [9].…”

Section: Introductionmentioning

confidence: 99%

“…Second, after the process of the tensor factorization and reconstruction, the value in the tensor can be assumed as the preference of the user for the item, and the items are ranked using the value [8], [11], [12]. However, these approaches do not consider the user's past activities [9]. In this paper, we suggest a recommendation method for mitigating two limitations and improving the efficiency of the recommendation based on our previous study.…”

Section: Introductionmentioning

confidence: 99%

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Tensor-based tag emotion aware recommendation with probabilistic ranking

Lim¹,

Kim²

2019

KSII TIIS

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In our previous research, we proposed a tag emotion-based item recommendation scheme. The ternary associations among users, items, and tags are described as a three-order tensor in order to capture the emotions in tags. The candidates for recommendation are created based on the latent semantics derived by a high-order singular value decomposition technique (HOSVD). However, the tensor is very sparse because the number of tagged items is smaller than the amount of all items. The previous research do not consider the previous behaviors of users and items. To mitigate the problems, in this paper, the item-based collaborative filtering scheme is used to build an extended data. We also apply the probabilistic ranking algorithm considering the user and item profiles to improve the recommendation performance. The proposed method is evaluated based on Movielens dataset, and the results show that our approach improves the performance compared to other methods.

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