A General Framework for Implicit and Explicit Social Recommendation

Hsu, Chin-Chi; Yeh, Mi-Yen; Lin, Shou-De

doi:10.1109/tkde.2018.2821174

Cited by 38 publications

(16 citation statements)

References 25 publications

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“…Alternating Least Squares has been widely applied to many models, especially matrix completion/approximation. Nonnegative matrix factorization (NMF) is an important topic in matrix completion/approximation, and it became a highlight when it was applied to recommendation systems in a Netflix contest [36][37][38]. At present, NMF has developed multiple variants, including (i) regularization based on L 1 -norms, L 2 -norms [39], L 2,1 -norms, nuclear norms, mixed norms, and graphs, (ii) different loss functions like Huber loss, the correntropy induced metric [40,41], Cauchy functions [42], and Truncated Cauchy functions [43,44], and (iii) many more, such as projected gradient NMF [45], projective NMF [46,47], and orthogonal NMF [48].…”

Section: Nmfimputementioning

confidence: 99%

Incomplete Data Analysis

Chen¹,

Wang²

2021

Applications of Pattern Recognition

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This chapter discusses missing-value problems from the perspective of machine learning. Missing values frequently occur during data acquisition. When a dataset contains missing values, nonvectorial data are generated. This subsequently causes a serious problem in pattern recognition models because nonvectorial data need further data wrangling before models are built. In view of such, this chapter reviews the methodologies of related works and examines their empirical effectiveness. At present, a great deal of effort has been devoted in this field, and those works can be roughly divided into two types — Multiple imputation and single imputation, where the latter can be further classified into subcategories. They include deletion, fixed-value replacement, K-Nearest Neighbors, regression, tree-based algorithms, and latent component-based approaches. In this chapter, those approaches are introduced and commented. Finally, numerical examples are provided along with recommendations on future development.

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Section: Nmfimputementioning

confidence: 99%

Incomplete Data Analysis

Chen¹,

Wang²

2021

Applications of Pattern Recognition

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“…The role of SNs in the recommendation procedure is examined in a large-scale field experiment that randomized exposure to friend information signals to ascertain the relative role of weak and strong ties [3], [12]. In addition, the work in [13] develops a way to combine SN information and CF methods for improved recommendation accuracy, through methods for selecting neighbors and boosting data from friends using user preference ratings and user SN relations collected from SN sources.…”

Section: Related Workmentioning

confidence: 99%

An Adaptive Social Network-Aware Collaborative Filtering Algorithm for Improved Rating Prediction Accuracy

et al. 2020

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When information from traditional recommender systems is augmented with information about user relationships that social networks store, more successful recommendations can be produced. However, this information regarding user relationships may not always be available, since some users may not consent to the use of their social network information for recommendations or may not have social network accounts at all. Moreover, the rating data (categories and characteristics of products) may be unavailable for a recommender system. In this paper, we present an algorithm that can be applied in any social network-aware recommender system that utilizes the users' ratings on items and users' social relations. The proposed algorithm addresses the issues of limited social network information or limited collaborative filtering information for some users by adapting its behavior, taking into account the density and utility of each user's social network and collaborative filtering neighborhoods. Through this adaptation, the proposed algorithm achieves considerable improvement in rating prediction accuracy. Furthermore, the proposed algorithm can be easily implemented in recommender systems.

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“…Paradoxically, if we only focus on solving the noise problem, the data will become more sparse [6]. On the contrary, if we only focus on solving the data sparse problem, the data will have more noise [16], [17]. Therefore, a few studies attempt to solve these shortcomings simultaneously [9], [15], identify reliable trust relationships in trust information by finding explicit connections between social relationships in a supervised manner, for example, Yu et al [15] proposed a deep adversarial framework, using multiple triangular heterogeneity relationships between users and trust friends to generate explicit user trust features, then GAN will generate reliable trust data based on these explicit trust features.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Learning User Implicit Trust Behavior Based on Graph Convolution Network

Seng

Lai

et al. 2021

IEEE Access

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In the existing social recommendation algorithms, the performance of the algorithms is bothered by the problem of noise and sparsity in social information. In the past few years, researchers have usually focused on solving noise problem or sparse problem, but ignored a key problem, that is if only solving the noise problem, the sparse problem will become more serious, similarly, if only solving the sparse problem, the noise problem will become more serious. Recently, a few studies attempt to solve both noise and sparse problems, usually using explicit trust features to filter trust information and generate implicit trust friends, but this will depend heavily on the quality of explicit trust features. Therefore, in this paper, we propose an adaptive algorithm based on graph convolution network, The main idea is to learn users' implicit trust behavior adaptively. Briefly, we use Jaccard similarity coefficient to filter out unnecessary trust information, the reliable trust information will be the users' trust attributes, then put into the graph convolution network, because of the good feature extraction ability of graph convolution network, we can use adaptive network to learn implicit trust features based on users' features, the trust features can infer the trust attributes for all users. In order to achieve the best effect of learning, we use iterative repetition of the above process, to mining the implicit influence of user trust behavior, so as to alleviate noise and sparsity problems. Experimental result shows that, on three open datasets: LastFM, Douban and Gowalla, the recommendation performance is improved, which proves the effectiveness of the algorithm.

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A General Framework for Implicit and Explicit Social Recommendation

Cited by 38 publications

References 25 publications

Incomplete Data Analysis

Incomplete Data Analysis

An Adaptive Social Network-Aware Collaborative Filtering Algorithm for Improved Rating Prediction Accuracy

Adaptive Learning User Implicit Trust Behavior Based on Graph Convolution Network

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