Trust inference using implicit influence and projected user network for item recommendation

Pal, Bithika; Jenamani, Mamata

doi:10.1007/s10844-018-0537-0

Cited by 9 publications

(5 citation statements)

References 35 publications

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“…Since they have different effects on the objective function, it is required to select two parameters to adjust their weights. Regularization [11], [16] coefficients are adopted to avoid excessive reliance on training data. As the F paradigm is a generally used regularization term, the loss function can describe as:…”

Section: Scmf Algorithmmentioning

confidence: 99%

Matrix Factorization Based Recommendation Algorithm for Sharing Patent Resource

Zhang¹,

Liu²,

Guo³

et al. 2021

IEICE Trans. Inf. & Syst.

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Xueqing ZHANG †a) , Xiaoxia LIU †b) , Jun GUO †c) , Nonmembers, Wenlei BAI †d) , Student Member, and Daguang GAN † †e) , Nonmember SUMMARY As scientific and technological resources are experiencing information overload, it is quite expensive to find resources that users are interested in exactly. The personalized recommendation system is a good candidate to solve this problem, but data sparseness and the cold starting problem still prevent the application of the recommendation system. Sparse data affects the quality of the similarity measurement and consequently the quality of the recommender system. In this paper, we propose a matrix factorization recommendation algorithm based on similarity calculation(SCMF), which introduces potential similarity relationships to solve the problem of data sparseness. A penalty factor is adopted in the latent item similarity matrix calculation to capture more real relationships furthermore. We compared our approach with other 6 recommendation algorithms and conducted experiments on 5 public data sets. According to the experimental results, the recommendation precision can improve by 2% to 9% versus the traditional best algorithm. As for sparse data sets, the prediction accuracy can also improve by 0.17% to 18%. Besides, our approach was applied to patent resource exploitation provided by the wanfang patents retrieval system. Experimental results show that our method performs better than commonly used algorithms, especially under the cold starting condition.

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Section: Scmf Algorithmmentioning

confidence: 99%

Matrix Factorization Based Recommendation Algorithm for Sharing Patent Resource

Zhang¹,

Liu²,

Guo³

et al. 2021

IEICE Trans. Inf. & Syst.

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show abstract

“…Therefore, the shortest distance in our article is set to less than or equal to 3. The same strategy can be referred to in [4] and [41].…”

Section: A the Process Of The Proposed Approachmentioning

confidence: 99%

A Novel Implicit Trust Recommendation Approach for Rating Prediction

Liu

Ren

et al. 2020

IEEE Access

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Rating predictions, as an application that is widely used in recommender systems, have gradually become a valuable way which can help user narrow down their choices quickly and make wise decisions from the vast amount of information. However, most existing collaborative recommendation models suffer from poor accuracy due to data sparsity and cold start problems that recommender systems contain only a few explicit data. To solve this problem, a new implicit trust recommendation approach (ITRA) is proposed to generate item rating prediction by mining and utilizing user implicit information in recommender systems. Specifically, user trust neighbor set that has similar preference and taste with a target user is first obtained by trust expansion strategy via user trust diffusion features in a trust network. Then, the trust ratings mined from user trust neighbors are used to compute trust similarity among users based on user collaborative filtering model. Finally, using the above filtered trust ratings and user trust similarity, the prediction results are generated by a trust weighting method. In addition, the empirical experiments are conducted on three real-world datasets, and the results demonstrate that our rating prediction model has obvious advantages over the state-of-the-art comparison methods in terms of the accuracy of recommendations.

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“…Unfortunately, in the real world, most social media websites do not provide users any way to explicitly express their trusts in each other, and therefore researchers have to design trust inference mechanisms based on the information containing trust relationships implicitly [11]. To the best of our knowledge, although trust-aware RS have been widely studied in recent years, there are still two problems to be resolved efficiently: (1) how to derive trust values from various social information; (2) how to integrate the data of trusts with user ratings [12].…”

Section: Introductionmentioning

confidence: 99%

A Social Collaborative Filtering Method to Alleviate Data Sparsity Based on Graph Convolutional Networks

Xie¹,

Fan²,

Xiao

2020

IEICE Trans. Inf. & Syst.

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Nowadays recommender systems (RS) keep drawing attention from academia, and collaborative filtering (CF) is the most successful technique for building RS. To overcome the inherent limitation, which is referred to as data sparsity in CF, various solutions are proposed to incorporate additional social information into recommendation processes, such as trust networks. However, existing methods suffer from multi-source data integration (i.e., fusion of social information and ratings), which is the basis for similarity calculation of user preferences. To this end, we propose a social collaborative filtering method based on novel trust metrics. Firstly, we use Graph Convolutional Networks (GCNs) to learn the associations between social information and user ratings while considering the underlying social network structures. Secondly, we measure the direct-trust values between neighbors by representing multi-source data as user ratings on popular items, and then calculate the indirect-trust values based on trust propagations. Thirdly, we employ all trust values to create a social regularization in user-item rating matrix factorization in order to avoid overfittings. The experiments on real datasets show that our approach outperforms the other state-of-the-art methods on usage of multi-source data to alleviate data sparsity.

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Trust inference using implicit influence and projected user network for item recommendation

Cited by 9 publications

References 35 publications

Matrix Factorization Based Recommendation Algorithm for Sharing Patent Resource

Matrix Factorization Based Recommendation Algorithm for Sharing Patent Resource

A Novel Implicit Trust Recommendation Approach for Rating Prediction

A Social Collaborative Filtering Method to Alleviate Data Sparsity Based on Graph Convolutional Networks

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