A Social Recommendation Based on Metric Learning and Users’ Co-Occurrence Pattern

For personalized recommender systems, matrix factorization and its variants have become mainstream in collaborative filtering. However, the dot product in matrix factorization does not satisfy the triangle inequality and therefore fails to capture fine-grained information. Metric learning-based models have been shown to be better at capturing fine-grained information than matrix factorization. Nevertheless, most of these models only focus on rating data and social information, which are not sufficient for dealing with the challenges of data sparsity. In this paper, we propose a metric learning-based social recommendation model called SRMC. SRMC exploits users’ co-occurrence patterns to discover their potentially similar or dissimilar users with symmetric relationships and change their relative positions to achieve better recommendations. Experiments on three public datasets show that our model is more effective than the compared models.

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A Social Recommendation Based on Metric Learning and Users’ Co-occurrence Pattern

10.20944/preprints202109.0489.v1 ◽

2021 ◽

Author(s):

Xin Zhang ◽

Jiwei Qin ◽

Jiong Zheng

Keyword(s):

Recommender Systems ◽

Matrix Factorization ◽

Triangle Inequality ◽

Metric Learning ◽

Social Recommendation ◽

Rating Data ◽

Fine Grained ◽

Information Metric ◽

Occurrence Pattern ◽

Public Datasets

For personalized recommender systems,matrix factorization and its variants have become mainstream in collaborative filtering.However,the dot product in matrix factorization does not satisfy the triangle inequality and therefore fails to capture fine-grained information. Metric learning-based models have been shown to be better at capturing fine-grained information than matrix factorization. Nevertheless,most of these models only focus on rating data and social information, which are not sufficient for dealing with the challenges of data sparsity. In this paper,we propose a metric learning-based social recommendation model called SRMC.SRMC exploits users' co-occurrence pattern to discover their potentially similar or dissimilar users with symmetric relationships and change their relative positions to achieve better recommendations.Experiments on three public datasets show that our model is more effective than the compared models.

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HyperSoRec: Exploiting Hyperbolic User and Item Representations with Multiple Aspects for Social-aware Recommendation

ACM Transactions on Information Systems ◽

10.1145/3463913 ◽

2022 ◽

Vol 40 (2) ◽

pp. 1-28

Author(s):

Hao Wang ◽

Defu Lian ◽

Hanghang Tong ◽

Qi Liu ◽

Zhenya Huang ◽

...

Keyword(s):

Hyperbolic Space ◽

Message Passing ◽

Metric Learning ◽

Great Success ◽

Social Recommendation ◽

Item Pair ◽

Trade Offs ◽

Location Based Social Networks ◽

Public Datasets ◽

Multiple Aspect

Social recommendation has achieved great success in many domains including e-commerce and location-based social networks. Existing methods usually explore the user-item interactions or user-user connections to predict users’ preference behaviors. However, they usually learn both user and item representations in Euclidean space, which has large limitations for exploring the latent hierarchical property in the data. In this article, we study a novel problem of hyperbolic social recommendation, where we aim to learn the compact but strong representations for both users and items. Meanwhile, this work also addresses two critical domain-issues, which are under-explored. First, users often make trade-offs with multiple underlying aspect factors to make decisions during their interactions with items. Second, users generally build connections with others in terms of different aspects, which produces different influences with aspects in social network. To this end, we propose a novel graph neural network (GNN) framework with multiple aspect learning, namely, HyperSoRec. Specifically, we first embed all users, items, and aspects into hyperbolic space with superior representations to ensure their hierarchical properties. Then, we adapt a GNN with novel multi-aspect message-passing-receiving mechanism to capture different influences among users. Next, to characterize the multi-aspect interactions of users on items, we propose an adaptive hyperbolic metric learning method by introducing learnable interactive relations among different aspects. Finally, we utilize the hyperbolic translational distance to measure the plausibility in each user-item pair for recommendation. Experimental results on two public datasets clearly demonstrate that our HyperSoRec not only achieves significant improvement for recommendation performance but also shows better representation ability in hyperbolic space with strong robustness and reliability.

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Metric Learning Based Fine-Grained Classification for PolSAR Imagery

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss39084.2020.9323087 ◽

2020 ◽

Author(s):

Jun Ni ◽

Yunzhe Jia ◽

Qiang Yin ◽

Yongsheng Zhou ◽

Fan Zhang

Keyword(s):

Metric Learning ◽

Fine Grained

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A social trust and preference segmentation-based matrix factorization recommendation algorithm

EURASIP Journal on Wireless Communications and Networking ◽

10.1186/s13638-019-1600-4 ◽

2019 ◽

Vol 2019 (1) ◽

Cited By ~ 2

Author(s):

Wei Peng ◽

Baogui Xin

Keyword(s):

Matrix Factorization ◽

Social Trust ◽

State Of The Art ◽

User Preference ◽

Social Recommendation ◽

Recommendation Algorithm ◽

Commercial Activities ◽

Trust Relationships ◽

Recommendation Algorithms ◽

The Difference

AbstractA recommendation can inspire potential demands of users and make e-commerce platforms more intelligent and is essential for e-commerce enterprises’ sustainable development. The traditional social recommendation algorithm ignores the following fact: the preferences of users with trust relationships are not necessarily similar, and the consideration of user preference similarity should be limited to specific areas. To solve these problems mentioned above, we propose a social trust and preference segmentation-based matrix factorization (SPMF) recommendation algorithm. Experimental results based on the Ciao and Epinions datasets show that the accuracy of the SPMF algorithm is significantly superior to that of some state-of-the-art recommendation algorithms. The SPMF algorithm is a better recommendation algorithm based on distinguishing the difference of trust relations and preference domain, which can support commercial activities such as product marketing.

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Matrix Factorization with Dual-Network Collaborative Embedding for Social Recommendation

International Journal of Wavelets Multiresolution and Information Processing ◽

10.1142/s0219691321500168 ◽

2021 ◽

Author(s):

Maosheng Wei ◽

Jun Wu ◽

Lina Yang ◽

Yuanyan Tang

Keyword(s):

Matrix Factorization ◽

Social Recommendation

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Efficient Vector Partitioning Algorithms for Graph Clustering

journal of Data Intelligence ◽

10.26421/jdi1.2-1 ◽

2020 ◽

Vol 1 (2) ◽

pp. 101-123

Author(s):

Hiroaki Shiokawa ◽

Yasunori Futamura

Keyword(s):

Social Networks ◽

Large Scale ◽

Clustering Algorithm ◽

Ground Truth ◽

Graph Clustering ◽

Mining Communities ◽

Fine Grained ◽

Efficient Vector ◽

Public Datasets ◽

Many Core

This paper addressed the problem of finding clusters included in graph-structured data such as Web graphs, social networks, and others. Graph clustering is one of the fundamental techniques for understanding structures present in the complex graphs such as Web pages, social networks, and others. In the Web and data mining communities, the modularity-based graph clustering algorithm is successfully used in many applications. However, it is difficult for the modularity-based methods to find fine-grained clusters hidden in large-scale graphs; the methods fail to reproduce the ground truth. In this paper, we present a novel modularity-based algorithm, \textit{CAV}, that shows better clustering results than the traditional algorithm. The proposed algorithm employs a cohesiveness-aware vector partitioning into the graph spectral analysis to improve the clustering accuracy. Additionally, this paper also presents a novel efficient algorithm \textit{P-CAV} for further improving the clustering speed of CAV; P-CAV is an extension of CAV that utilizes the thread-based parallelization on a many-core CPU. Our extensive experiments on synthetic and public datasets demonstrate the performance superiority of our approaches over the state-of-the-art approaches.

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