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Journal articles on the topic 'Recommendation graph'
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1
Jiang, Liwei, Guanghui Yan, Hao Luo, and Wenwen Chang. "Improved Collaborative Recommendation Model: Integrating Knowledge Embedding and Graph Contrastive Learning." Electronics 12, no. 20 (October 13, 2023): 4238. http://dx.doi.org/10.3390/electronics12204238.
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A recommendation algorithm combined with a knowledge graph enables auxiliary information on items to be obtained by using the knowledge graph to achieve better recommendations. However, the recommendation performance of existing methods relies heavily on the quality of the knowledge graph. Knowledge graphs often contain noise and irrelevant connections between items and entities in the real world. This knowledge graph sparsity and noise significantly amplifies the noise effects and hinders the accurate representation of user preferences. In response to these problems, an improved collaborative recommendation model is proposed which integrates knowledge embedding and graph contrastive learning. Specifically, we propose a knowledge contrastive learning scheme to mitigate noise within the knowledge graph during information aggregation, thereby enhancing the embedding quality of items. Simultaneously, to tackle the issue of insufficient user-side information in the knowledge graph, graph convolutional neural networks are utilized to propagate knowledge graph information from the item side to the user side, thereby enhancing the personalization capability of the recommendation system. Additionally, to resolve the over-smoothing issue in graph convolutional networks, a residual structure is employed to establish the message propagation network between adjacent layers of the same node, which expands the information propagation path. Experimental results on the Amazon-book and Yelp2018 public datasets demonstrate that the proposed model outperforms the best baseline models by 11.4% and 11.6%, respectively, in terms of the Recall@20 evaluation metric. This highlights the method’s efficacy in improving the recommendation accuracy and effectiveness when incorporating knowledge graphs into the recommendation process.
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Chen, Fukun, Guisheng Yin, Yuxin Dong, Gesu Li, and Weiqi Zhang. "KHGCN: Knowledge-Enhanced Recommendation with Hierarchical Graph Capsule Network." Entropy 25, no. 4 (April 20, 2023): 697. http://dx.doi.org/10.3390/e25040697.
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Knowledge graphs as external information has become one of the mainstream directions of current recommendation systems. Various knowledge-graph-representation methods have been proposed to promote the development of knowledge graphs in related fields. Knowledge-graph-embedding methods can learn entity information and complex relationships between the entities in knowledge graphs. Furthermore, recently proposed graph neural networks can learn higher-order representations of entities and relationships in knowledge graphs. Therefore, the complete presentation in the knowledge graph enriches the item information and alleviates the cold start of the recommendation process and too-sparse data. However, the knowledge graph’s entire entity and relation representation in personalized recommendation tasks will introduce unnecessary noise information for different users. To learn the entity-relationship presentation in the knowledge graph while effectively removing noise information, we innovatively propose a model named knowledge—enhanced hierarchical graph capsule network (KHGCN), which can extract node embeddings in graphs while learning the hierarchical structure of graphs. Our model eliminates noisy entities and relationship representations in the knowledge graph by the entity disentangling for the recommendation and introduces the attentive mechanism to strengthen the knowledge-graph aggregation. Our model learns the presentation of entity relationships by an original graph capsule network. The capsule neural networks represent the structured information between the entities more completely. We validate the proposed model on real-world datasets, and the validation results demonstrate the model’s effectiveness.
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Tolety, Venkata Bhanu Prasad, and Evani Venkateswara Prasad. "Graph Neural Networks for E-Learning Recommendation Systems." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9s (August 31, 2023): 43–50. http://dx.doi.org/10.17762/ijritcc.v11i9s.7395.
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This paper presents a novel recommendation system for e-learning platforms. Recent years have seen the emergence of graph neural networks (GNNs) for learning representations over graph-structured data. Due to their promising performance in semi-supervised learning over graphs and in recommendation systems, we employ them in e-learning platforms for user profiling and content profiling. Affinity graphs between users and learning resources are constructed in this study, and GNNs are employed to generate recommendations over these affinity graphs. In the context of e-learning, our proposed approach outperforms multiple different content-based and collaborative filtering baselines.
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Wang, Yan, Zhixuan Chu, Xin Ouyang, Simeng Wang, Hongyan Hao, Yue Shen, Jinjie Gu, et al. "LLMRG: Improving Recommendations through Large Language Model Reasoning Graphs." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 17 (March 24, 2024): 19189–96. http://dx.doi.org/10.1609/aaai.v38i17.29887.
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Recommendation systems aim to provide users with relevant suggestions, but often lack interpretability and fail to capture higher-level semantic relationships between user behaviors and profiles. In this paper, we propose a novel approach that leverages large language models (LLMs) to construct personalized reasoning graphs. These graphs link a user's profile and behavioral sequences through causal and logical inferences, representing the user's interests in an interpretable way. Our approach, LLM reasoning graphs (LLMRG), has four components: chained graph reasoning, divergent extension, self-verification and scoring, and knowledge base self-improvement. The resulting reasoning graph is encoded using graph neural networks, which serves as additional input to improve conventional recommender systems, without requiring extra user or item information. Our approach demonstrates how LLMs can enable more logical and interpretable recommender systems through personalized reasoning graphs. LLMRG allows recommendations to benefit from both engineered recommendation systems and LLM-derived reasoning graphs. We demonstrate the effectiveness of LLMRG on benchmarks and real-world scenarios in enhancing base recommendation models.
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Liu, Jiawei, Haihan Gao, Chuan Shi, Hongtao Cheng, and Qianlong Xie. "Self-Supervised Spatio-Temporal Graph Learning for Point-of-Interest Recommendation." Applied Sciences 13, no. 15 (August 1, 2023): 8885. http://dx.doi.org/10.3390/app13158885.
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As one of the most crucial topics in the recommendation system field, point-of-interest (POI) recommendation aims to recommending potential interesting POIs to users. Recently, graph neural networks have been successfully used to model interaction and spatio-temporal information in POI recommendations, but the data sparsity of POI recommendations affects the training of GNNs. Although some existing GNN-based POI recommendation approaches try to use social relationships or user attributes to alleviate the data sparsity problem, such auxiliary information is not always available for privacy reasons. Self-supervised learning provides a new idea to alleviate the data sparsity problem, but most existing self-supervised recommendation methods are designed for bi-partite graphs or social graphs, and cannot be directly used in the spatio-temporal graph of POI recommendations. In this paper, we propose a new method named SSTGL to combine self-supervised learning and GNN-based POI recommendation for the first time. SSTGL is empowered with spatio-temporal-aware strategies in the data augmentation and pre-text task stages, respectively, so that it can provide high-quality supervision information by incorporating spatio-temporal prior knowledge. By combining self-supervised learning objective with recommendation objectives, SSTGL can improve the performance of GNN-based POI recommendations. Extensive experiments on three POI recommendation datasets demonstrate the effectiveness of SSTGL, which performed better than existing mainstream methods.
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Li, Ran, Yuexin Li, Jingsheng Lei, and Shengying Yang. "A Multi-Behavior Recommendation Method for Users Based on Graph Neural Networks." Applied Sciences 13, no. 16 (August 16, 2023): 9315. http://dx.doi.org/10.3390/app13169315.
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Most existing recommendation models only consider single user–item interaction information, which leads to serious cold-start or data sparsity problems. In practical applications, a user’s behavior is multi-type, and different types of user behavior show different semantic information. To achieve more accurate recommendations, a major challenge comes from being able to handle heterogeneous behavior data from users more finely. To address this problem, this paper proposes a multi-behavior recommendation framework based on a graph neural network, which captures personalized semantics of specific behavior and thus distinguishes the importance of different behaviors for predicting the target behavior. Meanwhile, this model establishes dependency relationships among different types of interaction behaviors under the graph-based information transfer network, and the graph convolutional network is further used to capture the high-order complexity of interaction graphs. The experimental results of three benchmark datasets show that the proposed graph-based multi-behavior recommendation model displays significant improvements in recommendation accuracy compared to the baseline method.
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Wu, Ziteng, Chengyun Song, Yunqing Chen, and Lingxuan Li. "A review of recommendation system research based on bipartite graph." MATEC Web of Conferences 336 (2021): 05010. http://dx.doi.org/10.1051/matecconf/202133605010.
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The interaction history between users and items is usually stored and displayed in the form of bipartite graphs. Neural network recommendation based on the user-item bipartite graph has a significant effect on alleviating the long-standing data sparseness and cold start of the recommendation system. The whole paper is based on the bipartite graph. An review of the recommendation system of graphs summarizes the three characteristics of graph neural network processing bipartite graph data in the recommendation field: interchangeability, Multi-hop transportability, and strong interpretability. The biggest contribution of the full paper is that it summarizes the general framework of graph neural network processing bipartite graph recommendation from the models with the best recommendation effect in the past three years: embedding layer, propagation update layer, and prediction layer. Although there are subtle differences between different models, they are all this framework can be applied, and different models can be regarded as variants of this general model, that is, other models are fine-tuned on the basis of this framework. At the end of the paper, the latest research progress is introduced, and the main challenges and research priorities that will be faced in the future are pointed out.
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Yu, Wenhui, Zixin Zhang, and Zheng Qin. "Low-Pass Graph Convolutional Network for Recommendation." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 8 (June 28, 2022): 8954–61. http://dx.doi.org/10.1609/aaai.v36i8.20878.
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Spectral graph convolution is extremely time-consuming for large graphs, thus existing Graph Convolutional Networks (GCNs) reconstruct the kernel by a polynomial, which is (almost) fixed. To extract features from the graph data by learning kernels, Low-pass Collaborative Filter Network (LCFN) was proposed as a new paradigm with trainable kernels. However, there are two demerits of LCFN: (1) The hypergraphs in LCFN are constructed by mining 2-hop connections of the user-item bipartite graph, thus 1-hop connections are not used, resulting in serious information loss. (2) LCFN follows the general network structure of GCNs, which is suboptimal. To address these issues, we utilize the bipartite graph to define the graph space directly and explore the best network structure based on experiments. Comprehensive experiments on two real-world datasets demonstrate the effectiveness of the proposed model. Codes are available on https://github.com/Wenhui-Yu/LCFN.
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Zhang, Shengzhe, Liyi Chen, Chao Wang, Shuangli Li, and Hui Xiong. "Temporal Graph Contrastive Learning for Sequential Recommendation." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 8 (March 24, 2024): 9359–67. http://dx.doi.org/10.1609/aaai.v38i8.28789.
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Sequential recommendation is a crucial task in understanding users' evolving interests and predicting their future behaviors. While existing approaches on sequence or graph modeling to learn interaction sequences of users have shown promising performance, how to effectively exploit temporal information and deal with the uncertainty noise in evolving user behaviors is still quite challenging. To this end, in this paper, we propose a Temporal Graph Contrastive Learning method for Sequential Recommendation (TGCL4SR) which leverages not only local interaction sequences but also global temporal graphs to comprehend item correlations and analyze user behaviors from a temporal perspective. Specifically, we first devise a Temporal Item Transition Graph (TITG) to fully leverage global interactions to understand item correlations, and augment this graph by dual transformations based on neighbor sampling and time disturbance. Accordingly, we design a Temporal item Transition graph Convolutional network (TiTConv) to capture temporal item transition patterns in TITG. Then, a novel Temporal Graph Contrastive Learning (TGCL) mechanism is designed to enhance the uniformity of representations between augmented graphs from identical sequences. For local interaction sequences, we design a temporal sequence encoder to incorporate time interval embeddings into the architecture of Transformer. At the training stage, we take maximum mean discrepancy and TGCL losses as auxiliary objectives. Extensive experiments on several real-world datasets show the effectiveness of TGCL4SR against state-of-the-art baselines of sequential recommendation.
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Zeng, Yiping, and Shumin Liu. "Research on recommendation algorithm of Graph attention Network based on Knowledge graph." Journal of Physics: Conference Series 2113, no. 1 (November 1, 2021): 012085. http://dx.doi.org/10.1088/1742-6596/2113/1/012085.
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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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Zhang, Tingting, and Shengnan Liu. "Hybrid Music Recommendation Algorithm Based on Music Gene and Improved Knowledge Graph." Security and Communication Networks 2022 (April 9, 2022): 1–11. http://dx.doi.org/10.1155/2022/5889724.
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Combining music as a specific recommendation object, a hybrid recommendation algorithm based on music genes and improved knowledge graph is proposed for the traditional single recommendation algorithm that cannot effectively solve the accuracy problem in music recommendation. The algorithm first gives the recommendation pattern of music genes and gets the relevant recommendation results through the genetic preference analysis. After that, the algorithm in this paper utilizes item and user label information and knowledge graphs from two different domains to enrich and mine the potential information of users and items. In addition, deep learning method is applied to extract low-dimensional, abstract deep semantic features of users and items, based on which, score prediction is performed. The mixed-mode based recommendation addresses the drawbacks of these two recommendations and can adopt different weighting strategies in different situations. The advantages of music gene and knowledge graph-based recommendation algorithms are combined via this method. The experimental results indicate that the algorithm in this paper outperforms other existing recommendation algorithms.
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Kłopotek, Robert. "Modeling Bimodal Social Networks Subject to the Recommendation with the Cold Start User-Item Model." Computers 9, no. 1 (February 12, 2020): 11. http://dx.doi.org/10.3390/computers9010011.
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This paper describes the modeling of social networks subject to a recommendation. The Cold Start User-Item Model (CSUIM) of a bipartite graph is considered, which simulates bipartite graph growth based on several parameters. An algorithm is proposed to compute parameters of this model with desired properties. The primary desired property is that the generated graph has similar graph metrics. The next is a change in our graph growth process due to recommendations. The meaning of CSUI model parameters in the recommendation process is described. We make several simulations generating networks from the CSUI model to verify theoretical properties. Also, proposed methods are tested on real-life networks. We prove that the CSUIM model of bipartite graphs is very flexible and can be applied to many different problems. We also show that the parameters of this model can be easily obtained from an unknown bipartite graph.
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Shen, Lijuan, and Liping Jiang. "Eliminating bias: enhancing children’s book recommendation using a hybrid model of graph convolutional networks and neural matrix factorization." PeerJ Computer Science 10 (February 29, 2024): e1858. http://dx.doi.org/10.7717/peerj-cs.1858.
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Managing user bias in large-scale user review data is a significant challenge in optimizing children’s book recommendation systems. To tackle this issue, this study introduces a novel hybrid model that combines graph convolutional networks (GCN) based on bipartite graphs and neural matrix factorization (NMF). This model aims to enhance the precision and efficiency of children’s book recommendations by accurately capturing user biases. In this model, the complex interactions between users and books are modeled as a bipartite graph, with the users’ book ratings serving as the weights of the edges. Through GCN and NMF, we can delve into the structure of the graph and the behavioral patterns of users, more accurately identify and address user biases, and predict their future behaviors. Compared to traditional recommendation systems, our hybrid model excels in handling large-scale user review data. Experimental results confirm that our model has significantly improved in terms of recommendation accuracy and scalability, positively contributing to the advancement of children’s book recommendation systems.
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Nie, Na. "Research on Personalized Recommendation Algorithm of Internet Platform Goods Based on Knowledge Graph." Highlights in Science, Engineering and Technology 56 (July 14, 2023): 415–22. http://dx.doi.org/10.54097/hset.v56i.10704.
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Personalized recommendation method is an effective means to filter out the information users need from a large amount of information, which is rich in practical value. Personalized recommendation methods are maturing, and many e-commerce platforms have been using different forms of recommendation methods with great success. In the recommendation systems of large-scale e-commerce platforms, traditional recommendation algorithms represented by collaborative filtering are modeled only based on users' rating data, and sparse user-project interaction data and cold start are two inevitable problems. The introduction of knowledge graphs in recommendation systems can effectively solve these problems because of their rich knowledge content and powerful relationship processing capability. In this paper, we study the personalized recommendation algorithm based on knowledge graph as auxiliary information, and use the temporal information of user-item interaction in the graph to model users' interests over time at a finer granularity, taking into account the problem of high training time cost of the model due to frequent updates of the knowledge graph when recommending to users dynamically. The article proposes the Interactive Knowledge-Aware Attention Network Algorithmic Model for Recommendations (IKANAM) and conducts comparison experiments on public datasets. The results show that the IKANAM recommendation algorithm can effectively improve the effectiveness of personalized recommendation of products on Internet platforms.
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Li, Pu, Tianci Li, Xin Wang, Suzhi Zhang, Yuncheng Jiang, and Yong Tang. "Scholar Recommendation Based on High-Order Propagation of Knowledge Graphs." International Journal on Semantic Web and Information Systems 18, no. 1 (January 2022): 1–19. http://dx.doi.org/10.4018/ijswis.297146.
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In a big data environment, traditional recommendation methods have limitations such as data sparseness and cold start, etc. In view of the rich semantics, excellent quality, and good structure of knowledge graphs, many researchers have introduced knowledge graphs into the research about recommendation systems, and studied interpretable recommendations based on knowledge graphs. Along this line, this paper proposes a scholar recommendation method based on the high-order propagation of knowledge graph (HoPKG), which analyzes the high-order semantic information in the knowledge graph, and generates richer entity representations to obtain users’ potential interest by distinguishing the importance of different entities. On this basis, a dual aggregation method of high-order propagation is proposed to enable entity information to be propagated more effectively. Through experimental analysis, compared with some baselines, such as Ripplenet, RKGE and CKE, our method has certain advantages in the evaluation indicators AUC and F1.
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Crișan, Gheorghe-Cătălin. "Suggest Recommendation for Library Users Using Graphs." International Journal of Advanced Statistics and IT&C for Economics and Life Sciences 9, no. 1 (June 1, 2019): 41–51. http://dx.doi.org/10.2478/ijasitels-2019-0005.
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AbstractThe aim of this paper is to prove the usefulness of graphs in solving an ever-present problem for library users: finding books they like and they are looking for. Graphs are known as an important tool in solving conditioned optimization problems. We propose a graph-based system of recommendation which can be easy used in a library for assisting and helping users in finding in real time the books they like. The main advantage of the proposed graph-based approach lies in the ease with which new data or even new entities from different sources are added to the graph without disturbing the entire system. The system uses the similarity scores in order to find the similarity between objects and to get the best recommendation for a user’s request. In the end, we will compare the results from used formulas..
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Ni, Wenkai, Yanhui Du, Xingbang Ma, and Haibin Lv. "Research on Hybrid Recommendation Model for Personalized Recommendation Scenarios." Applied Sciences 13, no. 13 (July 5, 2023): 7903. http://dx.doi.org/10.3390/app13137903.
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One of the five types of Internet information service recommendation technologies is the personalized recommendation algorithm, and knowledge graphs are frequently used in these algorithms. RippleNet is a personalized recommendation model based on knowledge graphs, but it is susceptible to localization issues in user portrait updating. In this study, we propose NRH (Node2vec-side and RippleNet Hybrid Model), a hybrid recommendation model based on RippleNet that uses Node2vec-side for item portrait modeling and explores potential association relationships of items; the user portrait is split into two parts, namely, a static history portrait and a dynamic preference portrait; the NRH model adopts a hybrid recommendation approach based on collaborative filtering and a knowledge graph to obtain the user’s preferences on three publicly accessible datasets; and comparison experiments with the mainstream model are lastly carried out. The AUC and ACC increased, respectively, by 0.9% to 29.5% and 1.6% to 31.4% in the MovieLens-1M dataset, by 1.5% to 17.1% and 4.4% to 18.7% in the Book-Crossing dataset, and by 0.8% to 27.9% and 2.9% to 24.1% in the Last.FM dataset. The RippleNet model was used for comparison experiments comparing suggestion diversity. According to the experimental findings, the NRH model performs better in accuracy and variety than the popular customized knowledge graph recommendation algorithms now in use.
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Peng, Jiquan, Jibing Gong, Chao Zhou, Qian Zang, Xiaohan Fang, Kailun Yang, and Jing Yu. "KGCFRec: Improving Collaborative Filtering Recommendation with Knowledge Graph." Electronics 13, no. 10 (May 15, 2024): 1927. http://dx.doi.org/10.3390/electronics13101927.
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Traditional collaborative filtering (CF)-based recommendation systems are often challenged by data sparsity. The recent research has recognized the potential of integrating new information sources, such as knowledge graphs, to address this issue. However, a common drawback is the neglect of the interplay between user–item interaction data and knowledge graph information, resulting in insufficient model performance due to coarse-grained feature fusion. To bridge this gap, in this paper, we propose a novel graph neural network (GNN) model called KGCFRec, which leverages both Knowledge Graph and user–item Collaborative Filtering information for an enhanced Recommender system. KGCFRec employs a dual-channel information propagation and aggregation mechanism to generate distinct representations for the collaborative knowledge graph and the user–item interaction graph. This is followed by an attention mechanism that adaptively fuses the knowledge graph with collaborative information, thereby refining the representations and narrowing the gap between them. The experiments conducted on three real-world datasets demonstrate that KGCFRec outperforms state-of-the-art methods. These promising results underscore the capability of KGCFRec to enhance recommendation accuracy by integrating knowledge graph information.
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Pan, Zhiqiang, Wanyu Chen, and Honghui Chen. "Dynamic Graph Learning for Session-Based Recommendation." Mathematics 9, no. 12 (June 19, 2021): 1420. http://dx.doi.org/10.3390/math9121420.
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Session-based recommendation (SBRS) aims to make recommendations for users merely based on the ongoing session. Existing GNN-based methods achieve satisfactory performance by exploiting the pair-wise item transition pattern; however, they ignore the temporal evolution of the session graphs over different time-steps. Moreover, the widely applied cross-entropy loss with softmax in SBRS faces the serious overfitting problem. To deal with the above issues, we propose dynamic graph learning for session-based recommendation (DGL-SR). Specifically, we design a dynamic graph neural network (DGNN) to simultaneously take the graph structural information and the temporal dynamics into consideration for learning the dynamic item representations. Moreover, we propose a corrective margin softmax (CMS) to prevent overfitting in the model optimization by correcting the gradient of the negative samples. Comprehensive experiments are conducted on two benchmark datasets, that is, Diginetica and Gowalla, and the experimental results show the superiority of DGL-SR over the state-of-the-art baselines in terms of Recall@20 and MRR@20, especially on hitting the target item in the recommendation list.
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Ma, Xintao, Liyan Dong, Yuequn Wang, Yongli Li, and Minghui Sun. "AIRC: Attentive Implicit Relation Recommendation Incorporating Content Information for Bipartite Graphs." Mathematics 8, no. 12 (November 30, 2020): 2132. http://dx.doi.org/10.3390/math8122132.
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With users being exposed to the growing volume of online information, the recommendation system aiming at mining the important or interesting information is becoming a modern research topic. One approach of recommendation is to integrate the graph neural network with deep learning algorithms. However, some of them are not tailored for bipartite graphs, which is a unique type of heterogeneous graph having two entity types. Others, though customized, neglect the importance of implicit relation and content information. In this paper, we propose the attentive implicit relation recommendation incorporating content information (AIRC) framework that is designed for bipartite graphs based on the GC–MC algorithm. First, through reconstructing the bipartite graphs, we obtain the implicit relation graphs. Then we analyze the content information of users and items with a CNN process, so that each user and item has its feature-tailored embeddings. Besides, we expand the GC–MC algorithms by adding a graph attention mechanism layer, which handles the implicit relation graph by highlighting important features and neighbors. Therefore, our framework takes into consideration both the implicit relation and content information. Finally, we test our framework on Movielens dataset and the results show that our framework performs better than other state-of-art recommendation algorithms.
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Tallapally, Dharahas, John Wang, Katerina Potika, and Magdalini Eirinaki. "Using Graph Neural Networks for Social Recommendations." Algorithms 16, no. 11 (November 10, 2023): 515. http://dx.doi.org/10.3390/a16110515.
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Recommender systems have revolutionized the way users discover and engage with content. Moving beyond the collaborative filtering approach, most modern recommender systems leverage additional sources of information, such as context and social network data. Such data can be modeled using graphs, and the recent advances in Graph Neural Networks have led to the prominence of a new family of graph-based recommender system algorithms. In this work, we propose the RelationalNet algorithm, which not only models user–item, and user–user relationships but also item–item relationships with graphs and uses them as input to the recommendation process. The rationale for utilizing item–item interactions is to enrich the item embeddings by leveraging the similarities between items. By using Graph Neural Networks (GNNs), RelationalNet incorporates social influence and similar item influence into the recommendation process and captures more accurate user interests, especially when traditional methods fall short due to data sparsity. Such models improve the accuracy and effectiveness of recommendation systems by leveraging social connections and item interactions. Results demonstrate that RelationalNet outperforms current state-of-the-art social recommendation algorithms.
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Yang, Xu, Ziyi Huan, Yisong Zhai, and Ting Lin. "Research of Personalized Recommendation Technology Based on Knowledge Graphs." Applied Sciences 11, no. 15 (July 31, 2021): 7104. http://dx.doi.org/10.3390/app11157104.
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Nowadays, personalized recommendation based on knowledge graphs has become a hot spot for researchers due to its good recommendation effect. In this paper, we researched personalized recommendation based on knowledge graphs. First of all, we study the knowledge graphs’ construction method and complete the construction of the movie knowledge graphs. Furthermore, we use Neo4j graph database to store the movie data and vividly display it. Then, the classical translation model TransE algorithm in knowledge graph representation learning technology is studied in this paper, and we improved the algorithm through a cross-training method by using the information of the neighboring feature structures of the entities in the knowledge graph. Furthermore, the negative sampling process of TransE algorithm is improved. The experimental results show that the improved TransE model can more accurately vectorize entities and relations. Finally, this paper constructs a recommendation model by combining knowledge graphs with ranking learning and neural network. We propose the Bayesian personalized recommendation model based on knowledge graphs (KG-BPR) and the neural network recommendation model based on knowledge graphs (KG-NN). The semantic information of entities and relations in knowledge graphs is embedded into vector space by using improved TransE method, and we compare the results. The item entity vectors containing external knowledge information are integrated into the BPR model and neural network, respectively, which make up for the lack of knowledge information of the item itself. Finally, the experimental analysis is carried out on MovieLens-1M data set. The experimental results show that the two recommendation models proposed in this paper can effectively improve the accuracy, recall, F1 value and MAP value of recommendation.
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Gao, Wei, and Jian Wu. "Multigraph Convolutional Network Enhanced Neural Factorization Machine for Service Recommendation." Mathematical Problems in Engineering 2022 (April 1, 2022): 1–19. http://dx.doi.org/10.1155/2022/3747033.
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With an increasing number of web services on the Web, selecting appropriate services to meet the developer’s needs for mashup development has become a difficult task. To tackle the problem, various service recommendation methods have been proposed. However, there are still challenges, including the sparsity and imbalance of features, as well as the cold-start of mashups and services. To tackle these challenges, in this paper, we propose a Multigraph Convolutional Network enhanced Neural Factorization Machine model (MGCN-NFM) for service recommendation. It first constructs three graphs, namely, the collaborative graph, the description graph, and the tag graph. Each graph represents a different type of relation between mashups and services. Next, graph convolution is performed on the three graphs to learn the feature embeddings of mashups, services, and tags. Each node iteratively aggregates the information from its higher-order neighbors through message passing in each graph. Finally, the feature embeddings as well as the description features learned by Doc2vec are modeled by the neural factorization machine model, which captures the nonlinear and higher-order feature interaction relations between them. We conduct extensive experiments on the ProgrammableWeb dataset, and demonstrate that our proposed method outperforms state-of-the-art factorization machine-based methods in service recommendation.
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Xu, Zhuoming, Hanlin Liu, Jian Li, Qianqian Zhang, and Yan Tang. "CKGAT: Collaborative Knowledge-Aware Graph Attention Network for Top-N Recommendation." Applied Sciences 12, no. 3 (February 5, 2022): 1669. http://dx.doi.org/10.3390/app12031669.
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Knowledge graph-based recommendation methods are a hot research topic in the field of recommender systems in recent years. As a mainstream knowledge graph-based recommendation method, the propagation-based recommendation method captures users’ potential interests in items by integrating the representations of entities and relations in the knowledge graph and the high-order connection patterns between entities to provide personalized recommendations. For example, the collaborative knowledge-aware attentive network (CKAN) is a typical state-of-the-art propagation-based recommendation method that combines user-item interactions and knowledge associations in the knowledge graph, and performs heterogeneous propagation in the knowledge graph to generate multi-hop ripple sets, thereby capturing users’ potential interests. However, existing propagation-based recommendation methods, including CKAN, usually ignore the complex relations between entities in the multi-hop ripple sets and do not distinguish the importance of different ripple sets, resulting in inaccurate user potential interests being captured. Therefore, this paper proposes a top-N recommendation method named collaborative knowledge-aware graph attention network (CKGAT). Based on the heterogeneous propagation strategy, CKGAT uses the knowledge-aware graph attention network to extract the topological proximity structures of entities in the multi-hop ripple sets and then learn high-order entity representations, thereby generating refined ripple set embeddings. CKGAT further uses an attention aggregator to perform weighted aggregation on the ripple set embeddings, the user/item initial entity set embeddings, and the original representations of items to generate accurate user embeddings and item embeddings for the top-N recommendations. Experimental results show that CKGAT, overall, outperforms three baseline methods and six state-of-the-art propagation-based recommendation methods in terms of recommendation accuracy, and outperforms four representative propagation-based recommendation methods in terms of recommendation diversity.
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Zhang, Chengyuan, Yang Wang, Lei Zhu, Jiayu Song, and Hongzhi Yin. "Multi-Graph Heterogeneous Interaction Fusion for Social Recommendation." ACM Transactions on Information Systems 40, no. 2 (April 30, 2022): 1–26. http://dx.doi.org/10.1145/3466641.
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With the rapid development of online social recommendation system, substantial methods have been proposed. Unlike traditional recommendation system, social recommendation performs by integrating social relationship features, where there are two major challenges, i.e., early summarization and data sparsity. Thus far, they have not been solved effectively. In this article, we propose a novel social recommendation approach, namely Multi-Graph Heterogeneous Interaction Fusion (MG-HIF), to solve these two problems. Our basic idea is to fuse heterogeneous interaction features from multi-graphs, i.e., user–item bipartite graph and social relation network, to improve the vertex representation learning. A meta-path cross-fusion model is proposed to fuse multi-hop heterogeneous interaction features via discrete cross-correlations. Based on that, a social relation GAN is developed to explore latent friendships of each user. We further fuse representations from two graphs by a novel multi-graph information fusion strategy with attention mechanism. To the best of our knowledge, this is the first work to combine meta-path with social relation representation. To evaluate the performance of MG-HIF, we compare MG-HIF with seven states of the art over four benchmark datasets. The experimental results show that MG-HIF achieves better performance.
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Ling, Chun-Yang, Yan-Zhen Zou, Ze-Qi Lin, and Bing Xie. "Graph Embedding Based API Graph Search and Recommendation." Journal of Computer Science and Technology 34, no. 5 (September 2019): 993–1006. http://dx.doi.org/10.1007/s11390-019-1956-2.
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Li, Shanshan, Xinzhuan Hu, Jingfeng Guo, Bin Liu, Mingyue Qi, and Yutong Jia. "Popularity-Debiased Graph Self-Supervised for Recommendation." Electronics 13, no. 4 (February 6, 2024): 677. http://dx.doi.org/10.3390/electronics13040677.
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The rise of graph neural networks has greatly contributed to the development of recommendation systems, and self-supervised learning has emerged as one of the most important approaches to address sparse interaction data. However, existing methods mostly focus on the recommendation’s accuracy while neglecting the role of recommended item diversity in enhancing user interest and merchant benefits. The reason for this phenomenon is mainly due to the bias of popular items, which makes the long-tail items (account for a large proportion) be neglected. How to mitigate the bias caused by item popularity has become one of the hot topics in current research. To address the above problems, we propose a Popularity-Debiased Graph Self-Supervised for Recommendation (PDGS). Specifically, we apply a penalty constraint on item popularity during the data enhancement process on the user–item interaction graph to eliminate the inherent popularity bias. We generate item similarity graphs with the popularity bias removed to construct a self-supervised learning task under multiple views, and we design model optimization strategies from the perspectives of popular items and long-tail items to generate recommendation lists. We conduct a large number of comparison experiments, as well as ablation experiments, on three public datasets to verify the effectiveness and the superiority of the model in balancing recommendation accuracy and diversity.
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Pan, Zhiqiang, Fei Cai, Wanyu Chen, and Honghui Chen. "Graph Co-Attentive Session-based Recommendation." ACM Transactions on Information Systems 40, no. 4 (October 31, 2022): 1–31. http://dx.doi.org/10.1145/3486711.
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Session-based recommendation aims to generate recommendations merely based on the ongoing session, which is a challenging task. Previous methods mainly focus on modeling the sequential signals or the transition relations between items in the current session using RNNs or GNNs to identify user’s intent for recommendation. Such models generally ignore the dynamic connections between the local and global item transition patterns, although the global information is taken into consideration by exploiting the global-level pair-wise item transitions. Moreover, existing methods that mainly adopt the cross-entropy loss with softmax generally face a serious over-fitting problem, harming the recommendation accuracy. Thus, in this article, we propose a Graph Co-Attentive Recommendation Machine (GCARM) for session-based recommendation. In detail, we first design a Graph Co-Attention Network (GCAT) to consider the dynamic correlations between the local and global neighbors of each node during the information propagation. Then, the item-level dynamic connections between the output of the local and global graphs are modeled to generate the final item representations. After that, we produce the prediction scores and design a Max Cross-Entropy (MCE) loss to prevent over-fitting. Extensive experiments are conducted on three benchmark datasets, i.e., Diginetica, Gowalla, and Yoochoose. The experimental results show that GCARM can achieve the state-of-the-art performance in terms of Recall and MRR, especially on boosting the ranking of the target item.
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Zhang, Suqi, Ningjing Zhang, Shuai Fan, Junhua Gu, and Jianxin Li. "Knowledge Graph Recommendation Model Based on Adversarial Training." Applied Sciences 12, no. 15 (July 24, 2022): 7434. http://dx.doi.org/10.3390/app12157434.
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The recommendation model based on the knowledge graph (KG) alleviates the problem of data sparsity in the recommendation to a certain extent and further improves the accuracy, diversity, and interpretability of recommendations. Therefore, the knowledge graph recommendation model has become a major research topic, and the question of how to utilize the entity and relation information fully and effectively in KG has become the focus of research. This paper proposes a knowledge graph recommendation model based on adversarial training (ATKGRM), which can dynamically and adaptively adjust the knowledge graph aggregation weight based on adversarial training to learn the features of users and items more reasonably. First, the generator adopts a novel long- and short-term interest model to obtain user features and item features and generates a high-quality set of candidate items. Then, the discriminator discriminates candidate items by comparing the user’s scores of positive items, negative items, and candidate items. Finally, experimental studies on five real-world datasets with multiple knowledge graph recommendation models and multiple adversarial training recommendation models prove the effectiveness of our model.
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Fan, Lihang, Wenfei Fan, Ping Lu, Chao Tian, and Qiang Yin. "Enriching Recommendation Models with Logic Conditions." Proceedings of the ACM on Management of Data 1, no. 3 (November 13, 2023): 1–28. http://dx.doi.org/10.1145/3617330.
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This paper proposes RecLogic, a framework for improving the accuracy of machine learning (ML) models for recommendation. It aims to enhance existing ML models with logic conditions to reduce false positives and false negatives, without training a new model. Underlying RecLogic are (a) a class of prediction rules on graphs, denoted by TIEs, (b) a new approach to learning TIEs, and (c) a new paradigm for recommendation with TIEs. TIEs may embed ML recommendation models as predicates; as opposed to prior graph rules, it is tractable to decide whether a graph satisfies a set of TIEs. To enrich ML models, RecLogic iteratively trains a generator with feedback from each round, to learn TIEs with a probabilistic bound. RecLogic also provides a PTIME parallel algorithm for making recommendations with the learned TIEs. Using real-life data, we empirically verify that RecLogic improves the accuracy of ML predictions by 22.89% on average in an area where the prediction strength is neither sufficiently large nor sufficiently small, up to 33.10%.
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Ma, Ruixin, Fangqing Guo, Liang Zhao, Biao Mei, Xiya Bu, Hao Wu, and Enxin Song. "Knowledge Graph Extrapolation Network with Transductive Learning for Recommendation." Applied Sciences 12, no. 10 (May 12, 2022): 4899. http://dx.doi.org/10.3390/app12104899.
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A knowledge graph is introduced into the personalized recommendation algorithm due to its strong ability to express structural information and exploit side information. However, there is a long tail phenomenon and data sparsity in real knowledge graphs, and most items are related to only a few triples. This results in a significant reduction in the amount of data available for training, and makes it difficult to make accurate recommendations. Motivated by these limitations, the Knowledge Graph Extrapolation Network with Transductive Learning for Recommendation (KGET) is proposed to improve recommendation quality. To be specific, the method first learns the embedding of users and items by knowledge propagation combined with collaborative signal to obtain high-order structural information, and the attention mechanism is used to distinguish the contributions of different neighbor nodes in propagation. In order to better solve with data sparsity and long tail phenomenon, transductive learning is designed to model links between unknown items to enrich feature representation to further extrapolate the knowledge graph. We conduct experiments with two datasets about music and books, the experiment results reveal that our proposed method outperforms state-of-the-art recommendation methods. KGET also achieves strong and stable performance in sparse data scenarios where items have merely a few triples.
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Ma, Chuang, Xin Ren, Guangxia Xu, and Bo He. "FedGR: Federated Graph Neural Network for Recommendation Systems." Axioms 12, no. 2 (February 7, 2023): 170. http://dx.doi.org/10.3390/axioms12020170.
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Social recommendation systems based on the graph neural network (GNN) have received a lot of research-related attention recently because they can use social information to improve recommendation accuracy and because of the benefits derived from the excellent performance of the graph neural network in graphic data modeling. A large number of excellent studies in this area have been proposed one after another, but they all share a common requirement that the data should be centrally stored. In recent years, there have been growing concerns about data privacy. At the same time, the introduction of numerous stringent data protection regulations, represented by general data protection regulations (GDPR), has challenged the recommendation models with conventional centralized data storage. For the above reasons, we have designed a flexible model of recommendation algorithms for social scenarios based on federated learning. We call it the federated graph neural network for recommendation systems (FedGR). Previous related work in this area has only considered GNN, social networks, and federated learning separately. Our work is the first to consider all three together, and we have carried out a detailed design for each part. In FedGR, we used the graph attention network to assist in modeling the implicit vector representation learned by users from social relationship graphs and historical item graphs. In order to protect data privacy, we used FedGR flexible data privacy protection by incorporating traditional cryptography encryption techniques with the proposed “noise injection” strategy, which enables FedGR to ensure data privacy while minimizing the loss of recommended performance. We also demonstrate a different learning paradigm for the recommendation model under federation. Our proposed work has been validated on two publicly available popular datasets. According to the experimental results, FedGR has decreased MAE and RMSE compared with previous work, which proves its rationality and effectiveness.
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Liu, Jingtong, Huawei Yi, Yixuan Gao, and Rong Jing. "Personalized Point-of-Interest Recommendation Using Improved Graph Convolutional Network in Location-Based Social Network." Electronics 12, no. 16 (August 18, 2023): 3495. http://dx.doi.org/10.3390/electronics12163495.
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Data sparsity limits the performance of point-of-interest (POI) recommendation models, and the existing works ignore the higher-order collaborative influence of users and POIs and lack in-depth mining of user social influence, resulting in unsatisfactory recommendation results. To address the above issues, this paper proposes a personalized POI recommendation using an improved graph convolutional network (PPR_IGCN) model, which integrates collaborative influence and social influence into POI recommendations. On the one hand, a user-POI interaction graph, a POI-POI graph, and a user–user graph are constructed based on check-in data and social data in a location-based social network (LBSN). The improved graph convolutional network (GCN) is used to mine the higher-order collaborative influence of users and POIs in the three types of relationship graphs and to deeply extract the potential features of users and POIs. On the other hand, the social influence of the user’s higher-order social friends and community neighbors on the user is obtained according to the user’s higher-order social embedding vector learned in the user–user graph. Finally, the captured user and POI’s higher-order collaborative influence and social influence are used to predict user preferences. The experimental results on Foursquare and Yelp datasets indicate that the proposed model PPR_IGCN outperforms other models in terms of precision, recall, and normalized discounted cumulative gain (NDCG), which proves the effectiveness of the model.
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Zhu, Wenhao, Yujun Xie, Qun Huang, Zehua Zheng, Xiaozhao Fang, Yonghui Huang, and Weijun Sun. "Graph Transformer Collaborative Filtering Method for Multi-Behavior Recommendations." Mathematics 10, no. 16 (August 16, 2022): 2956. http://dx.doi.org/10.3390/math10162956.
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Graph convolutional networks are widely used in recommendation tasks owing to their ability to learn user and item embeddings using collaborative signals from high-order neighborhoods. Most of the graph convolutional recommendation tasks in existing studies have specialized in modeling a single type of user–item interaction preference. Meanwhile, graph-convolution-network-based recommendation models are prone to over-smoothing problems when stacking increased numbers of layers. Therefore, in this study we propose a multi-behavior recommendation method based on graph transformer collaborative filtering. This method utilizes an unsupervised subgraph generation model that divides users with similar preferences and their interaction items into subgraphs. Furthermore, it fuses multi-headed attention layers with temporal coding strategies based on the user–item interaction graphs in the subgraphs such that the learned embeddings can reflect multiple user–item relationships and the potential for dynamic interactions. Finally, multi-behavior recommendation is performed by uniting multi-layer embedding representations. The experimental results on two real-world datasets show that the proposed method performs better than previously developed systems.
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Xie, Lijie, Zhaoming Hu, Xingjuan Cai, Wensheng Zhang, and Jinjun Chen. "Explainable recommendation based on knowledge graph and multi-objective optimization." Complex & Intelligent Systems 7, no. 3 (March 6, 2021): 1241–52. http://dx.doi.org/10.1007/s40747-021-00315-y.
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AbstractRecommendation system is a technology that can mine user's preference for items. Explainable recommendation is to produce recommendations for target users and give reasons at the same time to reveal reasons for recommendations. The explainability of recommendations that can improve the transparency of recommendations and the probability of users choosing the recommended items. The merits about explainability of recommendations are obvious, but it is not enough to focus solely on explainability of recommendations in field of explainable recommendations. Therefore, it is essential to construct an explainable recommendation framework to improve the explainability of recommended items while maintaining accuracy and diversity. An explainable recommendation framework based on knowledge graph and multi-objective optimization is proposed that can optimize the precision, diversity and explainability about recommendations at the same time. Knowledge graph connects users and items through different relationships to obtain an explainable candidate list for target user, and the path between target user and recommended item is used as an explanation basis. The explainable candidate list is optimized through multi-objective optimization algorithm to obtain the final recommendation list. It is concluded from the results about experiments that presented explainable recommendation framework provides high-quality recommendations that contains high accuracy, diversity and explainability.
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Shang, Songtao, Wenqian Shang, Minyong Shi, Shuchao Feng, and Zhiguo Hong. "A Video Recommendation Algorithm Based on Hyperlink-Graph Model." International Journal of Software Innovation 5, no. 3 (July 2017): 49–63. http://dx.doi.org/10.4018/ijsi.2017070104.
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The traditional graph-based personal recommendation algorithms mainly depend the user-item model to construct a bipartite graph. However, the traditional algorithms have low efficiency, because the matrix of the algorithms is sparse and it cost lots of time to compute the similarity between users or items. Therefore, this paper proposes an improved video recommendation algorithm based on hyperlink-graph model. This method cannot only improve the accuracy of the recommendation algorithms, but also reduce the running time. Furthermore, the Internet users may have different interests, for example, a user interest in watching news videos, and at the same time he or she also enjoy watching economic and sports videos. This paper proposes a complement algorithm based on hyperlink-graph for video recommendations. This algorithm improves the accuracy of video recommendations by cross clustering in user layers.
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Chicaiza, Janneth, and Priscila Valdiviezo-Diaz. "A Comprehensive Survey of Knowledge Graph-Based Recommender Systems: Technologies, Development, and Contributions." Information 12, no. 6 (May 28, 2021): 232. http://dx.doi.org/10.3390/info12060232.
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In recent years, the use of recommender systems has become popular on the web. To improve recommendation performance, usage, and scalability, the research has evolved by producing several generations of recommender systems. There is much literature about it, although most proposals focus on traditional methods’ theories and applications. Recently, knowledge graph-based recommendations have attracted attention in academia and the industry because they can alleviate information sparsity and performance problems. We found only two studies that analyze the recommendation system’s role over graphs, but they focus on specific recommendation methods. This survey attempts to cover a broader analysis from a set of selected papers. In summary, the contributions of this paper are as follows: (1) we explore traditional and more recent developments of filtering methods for a recommender system, (2) we identify and analyze proposals related to knowledge graph-based recommender systems, (3) we present the most relevant contributions using an application domain, and (4) we outline future directions of research in the domain of recommender systems. As the main survey result, we found that the use of knowledge graphs for recommendations is an efficient way to leverage and connect a user’s and an item’s knowledge, thus providing more precise results for users.
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Pan, Long, Jiwei Qin, and Liejun Wang. "Graph-based recommendation by trust." International Journal of Internet Protocol Technology 14, no. 1 (2021): 33. http://dx.doi.org/10.1504/ijipt.2021.10036585.
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Wang, Liejun, Long Pan, and Jiwei Qin. "Graph-based recommendation by trust." International Journal of Internet Protocol Technology 14, no. 1 (2021): 33. http://dx.doi.org/10.1504/ijipt.2021.113906.
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Chen, Chong, Weizhi Ma, Min Zhang, Zhaowei Wang, Xiuqiang He, Chenyang Wang, Yiqun Liu, and Shaoping Ma. "Graph Heterogeneous Multi-Relational Recommendation." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 5 (May 18, 2021): 3958–66. http://dx.doi.org/10.1609/aaai.v35i5.16515.
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Traditional studies on recommender systems usually leverage only one type of user behaviors (the optimization target, such as purchase), despite the fact that users also generate a large number of various types of interaction data (e.g., view, click, add-to-cart, etc). Generally, these heterogeneous multi-relational data provide well-structured information and can be used for high-quality recommendation. Early efforts towards leveraging these heterogeneous data fail to capture the high-hop structure of user-item interactions, which are unable to make full use of them and may only achieve constrained recommendation performance. In this work, we propose a new multi-relational recommendation model named Graph Heterogeneous Collaborative Filtering (GHCF). To explore the high-hop heterogeneous user-item interactions, we take the advantages of Graph Convolutional Network (GCN) and further improve it to jointly embed both representations of nodes (users and items) and relations for multi-relational prediction. Moreover, to fully utilize the whole heterogeneous data, we perform the advanced efficient non-sampling optimization under a multi-task learning framework. Experimental results on two public benchmarks show that GHCF significantly outperforms the state-of-the-art recommendation methods, especially for cold-start users who have few primary item interactions. Further analysis verifies the importance of the proposed embedding propagation for modelling high-hop heterogeneous user-item interactions, showing the rationality and effectiveness of GHCF. Our implementation has been released (https://github.com/chenchongthu/GHCF).
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Wang, Xiaole, Jiwei Qin, Shangju Deng, and Wei Zeng. "Knowledge-Aware Enhanced Network Combining Neighborhood Information for Recommendations." Applied Sciences 13, no. 7 (April 4, 2023): 4577. http://dx.doi.org/10.3390/app13074577.
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In recent years, the application of knowledge graphs to alleviate cold start and data sparsity problems of users and items in recommendation systems, has aroused great interest. In this paper, in order to address the insufficient representation of user and item embeddings in existing knowledge graph-based recommendation methods, a knowledge-aware enhanced network, combining neighborhood information recommendation (KCNR), is proposed. Specifically, KCNR first encodes prior information about the user–item interaction, and obtains the user’s different knowledge neighbors by propagating them in the knowledge graph, and uses a knowledge-aware attention network to distinguish and aggregate the contributions of the different neighbors in the knowledge graph, as a way to enrich the user’s description. Similarly, KCNR samples multiple-hop neighbors of item entities in the knowledge graph, and has a bias to aggregate the neighborhood information, to enhance the item embedding representation. With the above processing, KCNR can automatically discover structural and associative semantic information in the knowledge graph, and capture users’ latent distant personalized preferences, by propagating them across the knowledge graph. In addition, considering the relevance of items to entities in the knowledge graph, KCNR has designed an information complementarity module, which automatically shares potential interaction characteristics of items and entities, and enables items and entities to complement the available information. We have verified that KCNR has excellent recommendation performance through extensive experiments in three real-life scenes: movies, books, and music.
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Zhang, Gengyue, Hao Li, Shuangling Li, Beibei Wang, and Zhixing Ding. "MMKG-PAR: Multi-Modal Knowledge Graphs-Based Personalized Attraction Recommendation." Sustainability 16, no. 5 (March 6, 2024): 2211. http://dx.doi.org/10.3390/su16052211.
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As the tourism industry rapidly develops, providing personalized attraction recommendations has become a hot research area. Knowledge graphs, with their rich semantic information and entity relationships, not only enhance the accuracy and personalization of recommendation systems but also energize the sustainable development of the tourism industry. Current research mainly focuses on single-modal knowledge modeling, limiting the in-depth understanding of complex entity characteristics and relationships. To address this challenge, this paper proposes a multi-modal knowledge graphs-based personalized attraction recommendation (MMKG-PAR) model. We utilized data from the “Travel Yunnan” app, along with users’ historical interaction data, to construct a collaborative multi-modal knowledge graph for Yunnan tourist attractions, which includes various forms such as images and text. Then, we employed advanced feature extraction methods to extract useful features from multi-modal data (images and text), and these were used as entity attributes to enhance the representation of entity nodes. To more effectively process graph-structured data and capture the complex relationships between nodes, our model incorporated graph neural networks and introduced an attention mechanism for mining and inferring higher-order information about entities. Additionally, MMKG-PAR introduced a dynamic time-weighted strategy for representing users, effectively capturing and precisely describing the dynamics of user behavior. Experimental results demonstrate that MMKG-PAR surpasses existing methods in personalized recommendations, providing significant support for the continuous development and innovation in the tourism industry.
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Li, Qi. "A Study on the Construction of Translation Curriculum System for English Majors from the Perspective of Human-Computer Interaction." Advances in Multimedia 2022 (August 26, 2022): 1–10. http://dx.doi.org/10.1155/2022/5902199.
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This paper proposes a collaborative human-computer interaction recommendation model for English major translation courses to address the problems of poor course recommendation and lack of robustness to noisy data in traditional recommendation models for English major translation courses. First, a new data enhancement method is proposed for the bipartite graph structure. Then, the enhanced data is fed into a graph convolutional neural network for node feature extraction to obtain node representations of users and items. A recommendation supervision task and an auxiliary task for contrast learning are constructed for joint optimization. The human-computer interaction model of the knowledge graph is designed, and the dialogue entities are embedded in the knowledge graph ripple network to obtain potentially interesting content for students. Finally, the student interaction content and node representations are combined to obtain the optimal translation course recommendation. The experimental results indicate that the proposed model in this work is capable of producing higher-quality English major course recommendations and beats other current models. This model is suitable for English primary translation course content recommendation and helps to improve students’ translation ability.
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Krasanakis, Emmanouil, and Andreas Symeonidis. "Fast Library Recommendation in Software Dependency Graphs with Symmetric Partially Absorbing Random Walks." Future Internet 14, no. 5 (April 20, 2022): 124. http://dx.doi.org/10.3390/fi14050124.
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To help developers discover libraries suited to their software projects, automated approaches often start from already employed libraries and recommend more based on co-occurrence patterns in other projects. The most accurate project–library recommendation systems employ Graph Neural Networks (GNNs) that learn latent node representations for link prediction. However, GNNs need to be retrained when dependency graphs are updated, for example, to recommend libraries for new projects, and are thus unwieldy for scalable deployment. To avoid retraining, we propose that recommendations can instead be performed with graph filters; by analyzing dependency graph dynamics emulating human-driven library discovery, we identify low-pass filtering with memory as a promising direction and introduce a novel filter, called symmetric partially absorbing random walks, which infers rather than trains the parameters of filters with node-specific memory to guarantee low-pass filtering. Experiments on a dependency graph between Android projects and third-party libraries show that our approach makes recommendations with a quality and diversification loosely comparable to those state-of-the-art GNNs without computationally intensive retraining for new predictions.
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Ma, Xintao, Liyan Dong, Yuequn Wang, Yongli Li, and Hao Zhang. "MNI: An enhanced multi-task neighborhood interaction model for recommendation on knowledge graph." PLOS ONE 16, no. 10 (October 28, 2021): e0258410. http://dx.doi.org/10.1371/journal.pone.0258410.
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To alleviate the data sparsity and cold start problems for collaborative filtering in recommendation systems, side information is usually leveraged by researchers to improve the recommendation performance. The utility of knowledge graph regards the side information as part of the graph structure and gives an explanation for recommendation results. In this paper, we propose an enhanced multi-task neighborhood interaction (MNI) model for recommendation on knowledge graphs. MNI explores not only the user-item interaction but also the neighbor-neighbor interactions, capturing a more sophisticated local structure. Besides, the entities and relations are also semantically embedded. And with the cross&compress unit, items in the recommendation system and entities in the knowledge graph can share latent features, and thus high-order interactions can be investigated. Through extensive experiments on real-world datasets, we demonstrate that MNI outperforms some of the state-of-the-art baselines both for CTR prediction and top-N recommendation.
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Khalid Khoshnaw, Karwan Hoshyar, Zardasht Abdulaziz Abdulkarim Shwany, Twana Mustafa, and Shayda Khudhur Ismail. "Mobile recommender system based on smart city graph." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 3 (March 1, 2022): 1771. http://dx.doi.org/10.11591/ijeecs.v25.i3.pp1771-1776.
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<span>Mobile recommender systems have changed the way people find items, purposes of intrigue, administrations, or even new companions. The innovation behind mobile recommender systems has developed to give client inclinations and social impacts. This paper introduces a first way to build a mobile recommendation system based on smart city graphs that appear topic features, user profiles, and impacts acquired from social connections. It exploits graph centrality measures to expand customized recommendations from the semantic information represented in the graph. The graph shows and chooses graph algorithms for computing chart centrality that is the center of the mobile recommender system are exhibited. Semantic ideas, for example, semantic transcendence and likeness measures, are adjusted to the graph model. Usage challenges confronted to settle execution issues are additionally examined.</span>
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Huang, Xiaoli, Junjie Wang, and Junying Cui. "A Personalized Collaborative Filtering Recommendation System Based on Bi-Graph Embedding and Causal Reasoning." Entropy 26, no. 5 (April 28, 2024): 371. http://dx.doi.org/10.3390/e26050371.
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The integration of graph embedding technology and collaborative filtering algorithms has shown promise in enhancing the performance of recommendation systems. However, existing integrated recommendation algorithms often suffer from feature bias and lack effectiveness in personalized user recommendation. For instance, users’ historical interactions with a certain class of items may inaccurately lead to recommendations of all items within that class, resulting in feature bias. Moreover, accommodating changes in user interests over time poses a significant challenge. This study introduces a novel recommendation model, RCKFM, which addresses these shortcomings by leveraging the CoFM model, TransR graph embedding model, backdoor tuning of causal inference, KL divergence, and the factorization machine model. RCKFM focuses on improving graph embedding technology, adjusting feature bias in embedding models, and achieving personalized recommendations. Specifically, it employs the TransR graph embedding model to handle various relationship types effectively, mitigates feature bias using causal inference techniques, and predicts changes in user interests through KL divergence, thereby enhancing the accuracy of personalized recommendations. Experimental evaluations conducted on publicly available datasets, including “MovieLens-1M” and “Douban dataset” from Kaggle, demonstrate the superior performance of the RCKFM model. The results indicate a significant improvement of between 3.17% and 6.81% in key indicators such as precision, recall, normalized discount cumulative gain, and hit rate in the top-10 recommendation tasks. These findings underscore the efficacy and potential impact of the proposed RCKFM model in advancing recommendation systems.
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Zhang, Ye, Yanqi Gao, Yupeng Zhou, Jianan Wang, and Minghao Yin. "MRMLREC: A Two-Stage Approach for Addressing Data Sparsity in MOOC Video Recommendation (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 21 (March 24, 2024): 23709–11. http://dx.doi.org/10.1609/aaai.v38i21.30536.
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With the abundance of learning resources available on massive open online courses (MOOCs) platforms, the issue of interactive data sparsity has emerged as a significant challenge.This paper introduces MRMLREC, an efficient MOOC video recommendation which consists of two main stages: multi-relational representation and multi-level recommendation, aiming to solve the problem of data sparsity. In the multi-relational representation stage, MRMLREC adopts a tripartite approach, constructing relational graphs based on temporal sequences, courses-videos relation, and knowledge concepts-video relation. These graphs are processed by a Graph Convolution Network (GCN) and two variant Graph Attention Networks (GAT) to derive representations. A variant of the Long Short-Term Memory Network (LSTM) then integrates these multi-dimensional data to enhance the overall representation. The multi-level recommendation stage introduces three prediction tasks at varying levels—courses, knowledge concepts, and videos—to mitigate data sparsity and improve the interpretability of video recommendations. Beam search (BS) is employed to identify top-β items at each level, refining the subsequent level's search space and enhancing recommendation efficiency. Additionally, an optional layer offers both personalization and diversification modes, ensuring variety in recommended videos and maintaining learner engagement. Comprehensive experiments demonstrate the effectiveness of MRMLREC on two real-world instances from Xuetang X.
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Wu, Jiang, Shaojie Jiang, and Lei Shi. "A Next POI Recommendation Based on Graph Convolutional Network by Adaptive Time Patterns." Electronics 12, no. 5 (March 4, 2023): 1241. http://dx.doi.org/10.3390/electronics12051241.
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Users’ activities in location-based social networks (LBSNs) can be naturally transformed into graph structural data, and more advanced graph representation learning techniques can be adopted for analyzing user preferences, which benefits a variety of real-world applications. This paper focuses on the next point-of-interest (POI) recommendation task in LBSNs. We argue that existing graph-based POI recommendation methods only consider user preferences from several individual contextual factors, ignoring the influence of interactions between different contextual information. This practice leads to the suboptimal learning of user preferences. To address this problem, we propose a novel method called hierarchical attention-based graph convolutional network (HAGCN) for the next POI recommendation, a technique which leverages graph convolutional networks to extract the representations of POIs from predefined graphs via different time patterns and develops a hierarchical attention mechanism to adaptively learn user preferences from the interactions between different contextual data. Moreover, HAGCN uses a dynamic preference estimation to precisely learn user preferences. We conduct extensive experiments on real-world datasets to evaluate the performance of HAGCN against representative baseline models in the field of next POI recommendation. The experimental results demonstrate the superiority of our proposed method on the next POI recommendation task.
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Wang, Daocheng, Chao Chen, Chong Di, and Minglei Shu. "Exploring Behavior Patterns for Next-POI Recommendation via Graph Self-Supervised Learning." Electronics 12, no. 8 (April 20, 2023): 1939. http://dx.doi.org/10.3390/electronics12081939.
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Next-point-of-interest (POI) recommendation is a crucial part of location-based social applications. Existing works have attempted to learn behavior representation through a sequence model combined with spatial-temporal-interval context. However, these approaches ignore the impact of implicit behavior patterns contained in the visit trajectory on user decision making. In this paper, we propose a novel graph self-supervised behavior pattern learning model (GSBPL) for the next-POI recommendation. GSBPL applies two graph data augmentation operations to generate augmented trajectory graphs to model implicit behavior patterns. At the same time, a graph preference representation encoder (GPRE) based on geographical and social context is proposed to learn the high-order representations of trajectory graphs, and then capture implicit behavior patterns through contrastive learning. In addition, we propose a self-attention based on multi-feature embedding to learn users’ short-term dynamic preferences, and finally combine trajectory graph representation to predict the next location. The experimental results on three real-world datasets demonstrate that GSBPL outperforms the supervised learning baseline in terms of performance under the same conditions.