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 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.

Download Full-text

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

Symmetry ◽

10.3390/sym13112158 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2158

Author(s):

Xin Zhang ◽

Jiwei Qin ◽

Jiong Zheng

Keyword(s):

Matrix Factorization ◽

Triangle Inequality ◽

Metric Learning ◽

Social Recommendation ◽

Rating Data ◽

Fine Grained ◽

Information Metric ◽

Occurrence Pattern ◽

Public Datasets ◽

Occurrence Patterns

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.

Download Full-text

Collaborative filtering-based recommendation of online social voting

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i3.11630 ◽

2018 ◽

Vol 7 (3) ◽

pp. 1504 ◽

Cited By ~ 5

Author(s):

Dr Mohammed Ismail ◽

Dr K. Bhanu Prakash ◽

Dr M. Nagabhushana Rao

Keyword(s):

Recommender Systems ◽

Matrix Factorization ◽

Recommendation System ◽

Nearest Neighbor ◽

Social Trust ◽

Social Recommendation ◽

Peer Reviewers ◽

Hybrid Recommender System ◽

Hybrid Recommender ◽

Cold Start Problem

Social voting is becoming the new reason behind social recommendation these days. It helps in providing accurate recommendations with the help of factors like social trust etc. Here we propose Matrix factorization (MF) and nearest neighbor-based recommender systems accommodating the factors of user activities and also compared them with the peer reviewers, to provide a accurate recommendation. Through experiments we realized that the affiliation factors are very much needed for improving the accuracy of the recommender systems. This information helps us to overcome the cold start problem of the recommendation system and also y the analysis this information was much useful to cold users than to heavy users. In our experiments simple neighborhood model outperform the computerized matrix factorization models in the hot voting and non hot voting recommendation. We also proposed a hybrid recommender system producing a top-k recommendation inculcating different single approaches.

Download Full-text

Matrix Factorization Recommendation Algorithm Based on Multiple Social Relationships

Mathematical Problems in Engineering ◽

10.1155/2021/6610645 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Sheng Bin ◽

Gengxin Sun

Keyword(s):

Social Networks ◽

Recommender Systems ◽

Social Relationships ◽

Matrix Factorization ◽

Social Relationship ◽

Recommendation System ◽

Social Recommendation ◽

Recommendation Algorithm ◽

Recommendation Algorithms

With the widespread use of social networks, social recommendation algorithms that add social relationships between users to recommender systems have been widely applied. Existing social recommendation algorithms only introduced one type of social relationship to the recommendation system, but in reality, there are often multiple social relationships among users. In this paper, a new matrix factorization recommendation algorithm combined with multiple social relationships is proposed. Through experiment results analysis on the Epinions dataset, the proposed matrix factorization recommendation algorithm has a significant improvement over the traditional and matrix factorization recommendation algorithms that integrate a single social relationship.

Download Full-text

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.

Download Full-text

Collaborative Metric Learning with Memory Network for Multi-Relational Recommender Systems

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2019/619 ◽

2019 ◽

Cited By ~ 3

Author(s):

Xiao Zhou ◽

Danyang Liu ◽

Jianxun Lian ◽

Xing Xie

Keyword(s):

Recommender Systems ◽

Metric Learning ◽

User Feedback ◽

User Preferences ◽

Single Type ◽

Fine Grained ◽

Neural Learning ◽

Wide Range ◽

Memory Network ◽

Feedback Data

The success of recommender systems in modern online platforms is inseparable from the accurate capture of users' personal tastes. In everyday life, large amounts of user feedback data are created along with user-item online interactions in a variety of ways, such as browsing, purchasing, and sharing. These multiple types of user feedback provide us with tremendous opportunities to detect individuals' fine-grained preferences. Different from most existing recommender systems that rely on a single type of feedback, we advocate incorporating multiple types of user-item interactions for better recommendations. Based on the observation that the underlying spectrum of user preferences is reflected in various types of interactions with items and can be uncovered by latent relational learning in metric space, we propose a unified neural learning framework, named Multi-Relational Memory Network (MRMN). It can not only model fine-grained user-item relations but also enable us to discriminate between feedback types in terms of the strength and diversity of user preferences. Extensive experiments show that the proposed MRMN model outperforms competitive state-of-the-art algorithms in a wide range of scenarios, including e-commerce, local services, and job recommendations.

Download Full-text

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

Download Full-text

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.

Download Full-text