Research on recommendation algorithm of Graph attention Network based on Knowledge graph

Abstract The introduction of knowledge graph as the auxiliary information of recommendation system provides a new research idea for personalized intelligent recommendation. However, most of the existing knowledge graph recommendation algorithms fail to effectively solve the problem of unrelated entities, leading to inaccurate prediction of potential preferences of users. To solve this problem, this paper proposes a KG-IGAT model combining knowledge graph and graph attention network, and adds an interest evolution module to graph attention network to capture user interest changes and generate top-N recommendations. Finally, experimental comparison between the proposed model and other algorithms using public data sets shows that KG-IGAT has better recommendation performance.

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AANMF: Attribute-Aware Attentional Neural Matrix Factorization

Information Technology And Control ◽

10.5755/j01.itc.48.4.23149 ◽

2019 ◽

Vol 48 (4) ◽

pp. 682-693

Author(s):

Bo Zheng ◽

Jinsong Hu

Keyword(s):

Matrix Factorization ◽

Recommendation System ◽

Auxiliary Information ◽

Inner Product ◽

Data Sets ◽

It Projects ◽

Real World Data ◽

Latent Space ◽

Almost All ◽

Novel Model

Matrix Factorization (MF) is one of the most intuitive and effective methods in the Recommendation System domain. It projects sparse (user, item) interactions into dense feature products which endues strong generality to the MF model. To leverage this interaction, recent works use auxiliary information of users and items. Despite effectiveness, irrationality still exists among these methods, since almost all of them simply add the feature of auxiliary information in dense latent space to the feature of the user or item. In this work, we propose a novel model named AANMF, short for Attribute-aware Attentional Neural Matrix Factorization. AANMF combines two main parts, namely, neural-network-based factorization architecture for modeling inner product and attention-mechanism-based attribute processing cell for attribute handling. Extensive experiments on two real-world data sets demonstrate the robust and stronger performance of our model. Notably, we show that our model can deal with the attributes of user or item more reasonably. Our implementation of AANMF is publicly available at https://github.com/Holy-Shine/AANMF.

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Using latent features for building an interpretable recommendation system

The Electronic Library ◽

10.1108/el-06-2020-0154 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Ziming Zeng ◽

Yu Shi ◽

Lavinia Florentina Pieptea ◽

Junhua Ding

Keyword(s):

Recommendation System ◽

User Preferences ◽

User Preference ◽

Data Sets ◽

Historical Records ◽

Data Link ◽

Content Type ◽

Fine Grained ◽

Public Data ◽

Latent Features

Purpose Aspects extracted from the user’s historical records are widely used to define user’s fine-grained preferences for building interpretable recommendation systems. As the aspects were extracted from the historical records, the aspects that represent user’s negative preferences cannot be identified because of their absence from the records. However, these latent aspects are also as important as those aspects representing user’s positive preferences for building a recommendation system. This paper aims to identify the user’s positive preferences and negative preferences for building an interpretable recommendation. Design/methodology/approach First, high-frequency tags are selected as aspects to describe user preferences in aspect-level. Second, user positive and negative preferences are calculated according to the positive and negative preference model, and the interaction between similar aspects is adopted to address the aspect sparsity problem. Finally, an experiment is designed to evaluate the effectiveness of the model. The code and the experiment data link is: https://github.com/shiyu108/Recommendation-system Findings Experimental results show the proposed approach outperformed the state-of-the-art methods in widely used public data sets. These latent aspects are also as important as those aspects representing the user’s positive preferences for building a recommendation system. Originality/value This paper provides a new approach that identifies and uses not only users’ positive preferences but also negative preferences, which can capture user preference precisely. Besides, the proposed model provides good interpretability.

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The Development of Building Materials Recommendation System Based on Collaborative Filtering

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.281.597 ◽

2013 ◽

Vol 281 ◽

pp. 597-602 ◽

Cited By ~ 2

Author(s):

Guo Fang Kuang ◽

Chun Lin Kuang

Keyword(s):

Collaborative Filtering ◽

Building Materials ◽

Recommendation System ◽

High Strength ◽

Data Sets ◽

User Interest ◽

Insulation Materials ◽

Target User ◽

Wide Range ◽

Materials Used

The building materials used in building materials collectively referred to as building materials. New building materials, including a wide range of insulation materials, insulation materials, high strength materials, breathing material belong to the new material. Collaborative filtering process is based on known user evaluation to predict the target user interest in the target, and then recommended to the target user. This paper proposes the development of building materials recommendation system based on Collaborative filtering. Experimental data sets prove that the proposed algorithm is effective and reasonable.

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Research on User Interest Expression and Recommendation Service based on Three-dimensional Relationship of Users and Items

International Journal on Recent and Innovation Trends in Computing and Communication ◽

10.17762/ijritcc.v8i5.5382 ◽

2020 ◽

Vol 8 (5) ◽

pp. 01-15

Author(s):

Dasong Sun ◽

Shuqing Li ◽

Wenjing Yan ◽

Fusen Jiao ◽

Junpeng Chen

Keyword(s):

Collaborative Filtering ◽

Recommendation System ◽

Memory Function ◽

Three Dimensional ◽

Matrix Decomposition ◽

Data Sets ◽

User Interest ◽

Recommendation Algorithm ◽

Trust Network ◽

Experimental Conditions

The existing recommendation algorithms often rely heavily on the original score information in the user rating matrix. However, the user's rating of items does not fully reflect the user's real interest. Therefore, the key to improve the existing recommendation system algorithm effectively is to eliminate the influence of these unfavorable factors and the accuracy of the recommendation algorithm can be improved by correcting the original user rating information reasonably. This paper makes a comprehensive theoretical analysis and method design from three aspects: the quality of the item, the memory function of the user and the influence of the social friends trusted by the user on the user's rating. Based on these methods, this paper finally proposes a collaborative filtering recommendation algorithm (FixCF) based on user rating modification. Using data sets such as Movielens, Epinions and Flixster, the data sets are divided into five representative subsets, and the experimental demonstration is carried out. FixCF and classical collaborative filtering algorithms, existing matrix decomposition-based algorithms and trust network-based inference are compared. The experimental results show that the accuracy and coverage of FixCF have been improved under many experimental conditions.

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Performance of Combined Recommendation Modeling Fused With Tagawareness In Mixed Traffic Scenarios

10.21203/rs.3.rs-1120711/v1 ◽

2021 ◽

Author(s):

Xiangyuan Li ◽

Fei Ding ◽

Suju Ren ◽

Jianmin Bao ◽

Ruoyu Su ◽

...

Keyword(s):

High Speed ◽

Recommendation System ◽

Short Term Memory ◽

User Behavior ◽

Data Sets ◽

Mixed Traffic ◽

User Interest ◽

Travel Information ◽

Lstm Network ◽

Behavior Characteristics

Abstract Due to the heterogeneous characteristics of vehicles and user terminals, information in mixed traffic scenarios can be interacted based on the Web protocol of different terminals. The recommendation system can dig users' travel preferences by analyzing historical travel information of different traffic participants, to publish accurate travel information and services for the terminals of traffic participants. The diversification of existing road network users and networking modes, as well as the dynamic changes of user interest distribution caused by high-speed movement of vehicles, traditional collaborative filtering algorithms have limitations in terms of effectiveness. This paper proposes a novel Hybrid Tag-aware Recommender Model (HTRM). The model embedding layer first employs the Word2vec model to represent the tags and ratings of projects and users, respectively. The feature layer then introduces the auto-encoder to extract self-similar features of the item, and a long short-term memory (LSTM) network is used to extract user behavior characteristics to provide higher-quality recommendations. The gating layer combines the features of users and projects and then makes score recommendations based on the Fully Connected Neural Network (FCNN). Finally, Web data sets of different service preferences of traffic participants during the trip are used to evaluate the model recommendation performance in different scenarios. The experimental results show that the HTRM model is reasonable in design and can achieve high recommendation accuracy.

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End-to-end Relation-Enhanced Learnable Graph Self-attention Network for Knowledge Graphs Embedding

10.21203/rs.3.rs-396932/v1 ◽

2021 ◽

Author(s):

Shengchen Jiang ◽

Hongbin Wang ◽

Xiang Hou

Keyword(s):

Large Scale ◽

Structural Characteristics ◽

Graph Embedding ◽

Knowledge Graph ◽

Data Sets ◽

Relevance Ranking ◽

Convolutional Network ◽

Attention Network ◽

Knowledge Graphs ◽

End To End

Abstract The existing methods ignore the adverse effect of knowledge graph incompleteness on knowledge graph embedding. In addition, the complexity and large-scale of knowledge information hinder knowledge graph embedding performance of the classic graph convolutional network. In this paper, we analyzed the structural characteristics of knowledge graph and the imbalance of knowledge information. Complex knowledge information requires that the model should have better learnability, rather than linearly weighted qualitative constraints, so the method of end-to-end relation-enhanced learnable graph self-attention network for knowledge graphs embedding is proposed. Firstly, we construct the relation-enhanced adjacency matrix to consider the incompleteness of the knowledge graph. Secondly, the graph self-attention network is employed to obtain the global encoding and relevance ranking of entity node information. Thirdly, we propose the concept of convolutional knowledge subgraph, it is constructed according to the entity relevance ranking. Finally, we improve the training effect of the convKB model by changing the construction of negative samples to obtain a better reliability score in the decoder. The experimental results based on the data sets FB15k-237 and WN18RR show that the proposed method facilitates more comprehensive representation of knowledge information than the existing methods, in terms of Hits@10 and MRR.

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The Development of E-Commerce Recommendation System Based on Collaborative Filtering

Advanced Engineering Forum ◽

10.4028/www.scientific.net/aef.6-7.636 ◽

2012 ◽

Vol 6-7 ◽

pp. 636-640 ◽

Cited By ~ 2

Author(s):

Guo Fang Kuang

Keyword(s):

Experimental Data ◽

Collaborative Filtering ◽

Recommendation System ◽

Product Information ◽

Data Sets ◽

User Evaluation ◽

User Interest ◽

Target User ◽

Purchase Process ◽

Sales Staff

The recommendation system in the e-commerce is to provide customers with product information and recommendations to help customers decide what to buy goods and analog sales staff to recommend merchandise to complete the purchase process. Collaborative filtering process is based on known user evaluation to predict the target user interest in the target, and then recommended to the target user. This paper proposes the development of E-commerce recommendation system based on Collaborative filtering. Experimental data sets prove that the proposed algorithm is effective and reasonable.

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A 3D Cascaded Spectral–Spatial Element Attention Network for Hyperspectral Image Classification

Remote Sensing ◽

10.3390/rs13132451 ◽

2021 ◽

Vol 13 (13) ◽

pp. 2451

Author(s):

Huaiping Yan ◽

Jun Wang ◽

Lei Tang ◽

Erlei Zhang ◽

Kun Yan ◽

...

Keyword(s):

Hyperspectral Image ◽

Training Sample ◽

Small Sample ◽

Data Sets ◽

Attention Network ◽

Deep Network ◽

Public Data ◽

Comparable Performance ◽

And Performance ◽

Spatial Element

Most traditional hyperspectral image (HSI) classification methods relied on hand-crafted or shallow-based descriptors, which limits their applicability and performance. Recently, deep learning has gradually become the mainstream method of HSI classification, because it can automatically extract deep abstract features for classification. However, it remains a challenge to learn more meaningful features for HSI classification from a small training sample set. In this paper, a 3D cascaded spectral–spatial element attention network (3D-CSSEAN) is proposed to solve this issue. The 3D-CSSEAN integrates the spectral–spatial feature extraction and attention area extraction for HSI classification. Two element attention modules in the 3D-CSSEAN enable the deep network to focus on primary spectral features and meaningful spatial features. All attention modules are implemented though several simple activation operations and elementwise multiplication operations. In this way, the training parameters of the network are not added too much, which also makes the network structure suitable for small sample learning. The adopted module cascading pattern not only reduces the computational burden in the deep network but can also be easily operated via plug–expand–play. Experimental results on three public data sets show that the proposed 3D-CSSEAN achieved comparable performance with the state-of-the-art methods.

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