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Journal articles on the topic 'Graph-Based Recommendation Systems'
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Pandey, Vivek, and Padma Bonde. "Graph based Recommendation for Distributed Systems." International Journal of Computer Applications 168, no. 4 (June 15, 2017): 41–43. http://dx.doi.org/10.5120/ijca2017914376.
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Ye, Yutao. "An overview of knowledge graph-based recommendation systems." Applied and Computational Engineering 73, no. 1 (July 5, 2024): 57–68. http://dx.doi.org/10.54254/2755-2721/73/20240363.
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Recommendation systems have emerged as effective tools for mitigating information overload. Traditionally, recommendation systems employ various models such as Collaborative Filtering, Matrix Decomposition, and Logic Decomposition. Among these, Collaborative Filtering stands out due to its high efficiency. However, it encounters challenges related to cold start and sparse data. To address these challenges, the integration of Knowledge Graphs with recommendation systems has demonstrated significant advantages. This paper classifies Knowledge Graph-based recommendation systems into two categories: enhanced classical recommendation models and novel recommendation models integrated with Knowledge Graphs. We provide explanations for each category and compare them with traditional methods to draw conclusions. To inspire future research endeavors, this article identifies potential research areas and highlights unresolved issues.
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Khanna, Dhairya, Rishabh Bhushan, Khushboo Goel, and Dr Sudha Narang. "Recommendation Systems using Graph Neural Networks." International Journal for Research in Applied Science and Engineering Technology 11, no. 1 (January 31, 2023): 448–51. http://dx.doi.org/10.22214/ijraset.2023.48539.
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Abstract: On the internet as the technology improves, the number of choices is overwhelming due to which there is need to filter, prioritize and efficiently deliver relevant information in order to alleviate the problem of information overload, which has created a potential problem to many Internet users. Recommender systems solve this problem by searching through large volume of dynamically generated information to provide users with personalized content and services. Recently, graph neural network (GNN) techniques have been widely utilized in recommender systems since most of the information in recommender systems essentially has graph structure and GNN has superiority in graph representation learning. This paper aims to provide a comprehensive application of GNN-based recommender systems.
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Lu, Heng, and Ziwei Chen. "SocialJGCF: Social Recommendation with Jacobi Polynomial-Based Graph Collaborative Filtering." Applied Sciences 14, no. 24 (December 23, 2024): 12070. https://doi.org/10.3390/app142412070.
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With the flourishing of social media platforms, data in social networks, especially user-generated content, are growing rapidly, which makes it hard for users to select relevant content from the overloaded data. Recommender systems are thus developed to filter user-relevant content for better user experiences and also the commercial needs of social platform providers. Graph neural networks have been widely applied in recommender systems for better recommendation based on past interactions between users and corresponding items due to the graph structure of social data. Users might also be influenced by their social connections, which is the focus of social recommendation. Most works on recommendation systems try to obtain better representations of user embeddings and item embeddings. Compared with recommendation systems only focusing on interaction graphs, social recommendation has an additional task of combining user embedding from the social graph and interaction graph. This paper proposes a new method called SocialJGCF to address these problems, which applies Jacobi-Polynomial-Based Graph Collaborative Filtering (JGCF) to the propagation of the interaction graph and social graph, and a graph fusion is used to combine the user embeddings from the interaction graph and social graph. Experiments are conducted on two real-world datasets, epinions and LastFM. The result shows that SocialJGCF has great potential in social recommendation, especially for cold-start problems.
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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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Ren, Jiangtao, Jiawei Long, and Zhikang Xu. "Financial news recommendation based on graph embeddings." Decision Support Systems 125 (October 2019): 113115. http://dx.doi.org/10.1016/j.dss.2019.113115.
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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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Bi, Zhongqin, Lina Jing, Meijing Shan, Shuming Dou, and Shiyang Wang. "Hierarchical Social Recommendation Model Based on a Graph Neural Network." Wireless Communications and Mobile Computing 2021 (August 31, 2021): 1–10. http://dx.doi.org/10.1155/2021/9107718.
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With the continuous accumulation of social network data, social recommendation has become a widely used recommendation method. Based on the theory of social relationship propagation, mining user relationships in social networks can alleviate the problems of data sparsity and the cold start of recommendation systems. Therefore, integrating social information into recommendation systems is of profound importance. We present an efficient network model for social recommendation. The model is based on the graph neural network. It unifies the attention mechanism and bidirectional LSTM into the same framework and uses a multilayer perceptron. In addition, an embedded propagation method is added to learn the neighbor influences of different depths and extract useful neighbor information for social relationship modeling. We use this method to solve the problem that the current research methods of social recommendation only extract the superficial level of social networks but ignore the importance of the relationship strength of the users at different levels in the recommendation. This model integrates social relationships into user and project interactions, not only capturing the weight of the relationship between different users but also considering the influence of neighbors at different levels on user preferences. Experiments on two public datasets demonstrate that the proposed model is superior to other benchmark methods with respect to mean absolute error and root mean square error and can effectively improve the quality of recommendations.
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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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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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Cai, Congyu, Hong Chen, Yunxuan Liu, Daoquan Chen, Xiuze Zhou, and Yuanguo Lin. "Graph-Based Feature Crossing to Enhance Recommender Systems." Mathematics 13, no. 2 (January 18, 2025): 302. https://doi.org/10.3390/math13020302.
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In recommendation tasks, most existing models that learn users’ preferences from user–item interactions ignore the relationships between items. Additionally, ensuring that the crossed features capture both global graph structures and local context is non-trivial, requiring innovative techniques for multi-scale representation learning. To overcome these difficulties, we develop a novel neural network, CoGraph, which uses a graph to build the relations between items. The item co-occurrence pattern assumes that certain items consistently appear in pairs in users’ viewing or consumption logs. First, to learn relationships between items, a graph whose distance is measured by Normalised Point-Wise Mutual Information (NPMI) is applied to link items for the co-occurrence pattern. Then, to learn as many useful features as possible for higher recommendation quality, a Convolutional Neural Network (CNN) and the Transformer model are used to parallelly learn local and global feature interactions. Finally, a series of comprehensive experiments were conducted on several public data sets to show the performance of our model. It provides valuable insights into the capability of our model in recommendation tasks and offers a viable pathway for the public data operation.
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Guez Dellove, Ondongo Aucibi Adrard, and Kamalaraj R. "Natural Language Processing (NLP) in Recommendation Systems." International Journal of Innovative Research in Computer and Communication Engineering 12, no. 05 (May 17, 2024): 5974–76. http://dx.doi.org/10.15680/ijircce.2024.1205140.
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This paper delves into the application of Natural Language Processing (NLP) techniques in recommendation systems, specifically focusing on novel approaches to enhance recommendation accuracy and user satisfaction. The utilization of NLP algorithms has revolutionized how content is recommended to users, leveraging linguistic analysis and machine learning to understand user preferences and provide tailored suggestions. Our research explores various NLP methodologies, including sentiment analysis, topic modelling, and semantic analysis, to extract meaningful insights from textual data. Furthermore, we investigate the integration of deep learning models, such as neural networks and transformer architectures, to capture complex patterns and improve recommendation precision. A key highlight of our study is the introduction of a novel recommendation method termed "Knowledge Graph Embedding for Contextual Recommendation." This innovative approach combines knowledge graph representation with contextual understanding, allowing for more nuanced and personalized recommendations based on user interactions, historical data, and contextual relevance. We delve into the intricacies of this technique, detailing its implementation, training process, and evaluation metrics.
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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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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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Rong Jing, Qi Guo, Bin Wei, Ailin Li,. "Paper Recommendation Method based on Attention Mechanism and Graph Neural Network." Journal of Electrical Systems 20, no. 2 (April 4, 2024): 88–95. http://dx.doi.org/10.52783/jes.1101.
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At present, most recommendation technologies only consider text or citation information, which suffers from data sparseness and cold start problems. Therefore, an academic paper recommendation method based on attention mechanism and heterogeneous graph CAH is proposed. This method considers textual information and heterogeneous graph structure information to obtain a richer and more complete feature representation. Finally, cosine similarity is calculated to generate recommendations. The results show that compared with the content-based recommendation method, the accuracy rate, recall rate and f value of CAH method are increased by nearly 5.6%, 5.8% and 8.7%, respectively, which are significantly improved compared with the basic method. This method is expected to promote the in-depth application of recommendation systems in the field of artificial intelligence.
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Bhoi, Suman, Mong Li Lee, Wynne Hsu, Hao Sen Andrew Fang, and Ngiap Chuan Tan. "Personalizing Medication Recommendation with a Graph-Based Approach." ACM Transactions on Information Systems 40, no. 3 (July 31, 2022): 1–23. http://dx.doi.org/10.1145/3488668.
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The broad adoption of electronic health records (EHRs) has led to vast amounts of data being accumulated on a patient’s history, diagnosis, prescriptions, and lab tests. Advances in recommender technologies have the potential to utilize this information to help doctors personalize the prescribed medications. However, existing medication recommendation systems have yet to make use of all these information sources in a seamless manner, and they do not provide a justification on why a particular medication is recommended. In this work, we design a two-stage personalized medication recommender system called PREMIER that incorporates information from the EHR. We utilize the various weights in the system to compute the contributions from the information sources for the recommended medications. Our system models the drug interaction from an external drug database and the drug co-occurrence from the EHR as graphs. Experiment results on MIMIC-III and a proprietary outpatient dataset show that PREMIER outperforms state-of-the-art medication recommendation systems while achieving the best tradeoff between accuracy and drug-drug interaction. Case studies demonstrate that the justifications provided by PREMIER are appropriate and aligned to clinical practices.
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Mansoury, Masoud, Himan Abdollahpouri, Mykola Pechenizkiy, Bamshad Mobasher, and Robin Burke. "A Graph-Based Approach for Mitigating Multi-Sided Exposure Bias in Recommender Systems." ACM Transactions on Information Systems 40, no. 2 (April 30, 2022): 1–31. http://dx.doi.org/10.1145/3470948.
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Fairness is a critical system-level objective in recommender systems that has been the subject of extensive recent research. A specific form of fairness is supplier exposure fairness, where the objective is to ensure equitable coverage of items across all suppliers in recommendations provided to users. This is especially important in multistakeholder recommendation scenarios where it may be important to optimize utilities not just for the end user but also for other stakeholders such as item sellers or producers who desire a fair representation of their items. This type of supplier fairness is sometimes accomplished by attempting to increase aggregate diversity to mitigate popularity bias and to improve the coverage of long-tail items in recommendations. In this article, we introduce FairMatch, a general graph-based algorithm that works as a post-processing approach after recommendation generation to improve exposure fairness for items and suppliers. The algorithm iteratively adds high-quality items that have low visibility or items from suppliers with low exposure to the users’ final recommendation lists. A comprehensive set of experiments on two datasets and comparison with state-of-the-art baselines show that FairMatch, although it significantly improves exposure fairness and aggregate diversity, maintains an acceptable level of relevance of the recommendations.
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Yu, Jie, Chenle Pan, Yaliu Li, and Junwei Wang. "An Academic Text Recommendation Method Based on Graph Neural Network." Information 12, no. 4 (April 16, 2021): 172. http://dx.doi.org/10.3390/info12040172.
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Academic text recommendation, as a kind of text recommendation, has a wide range of application prospects. Predicting texts of interest to scholars in different fields based on anonymous sessions is a challenging problem. However, the existing session-based method only considers the sequential information, and pays more attention to capture the session purpose. The relationship between adjacent items in the session is not noticed. Specifically in the field of session-based text recommendation, the most important semantic relationship of text is not fully utilized. Based on the graph neural network and attention mechanism, this paper proposes a session-based text recommendation model (TXT-SR) incorporating the semantic relations, which is applied to the academic field. TXT-SR makes full use of the tightness of semantic connections between adjacent texts. We have conducted experiments on two real-life academic datasets from CiteULike. Experimental results show that TXT-SR has better effectiveness than existing session-based recommendation methods.
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Zhang, Zhuohao, Jinghua Zhu, and Chenbo Yue. "Session-Based Graph Attention POI Recommendation Network." Wireless Communications and Mobile Computing 2022 (July 21, 2022): 1–9. http://dx.doi.org/10.1155/2022/6557936.
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Point-of-interest (POI) recommendation which aims at predicting the locations that users may be interested in has attracted wide attentions due to the development of Internet of Things and location-based services. Although collaborative filtering based methods and deep neural network have gain great success in POI recommendation, data sparsity and cold start problem still exist. To this end, this paper proposes session-based graph attention network (SGANet for short) for POI recommendation by making use of regional information. Specifically, we first extract users’ features from the regional history check-in data in session windows. Then, we use graph attention network to learn users’ preferences for both POI and regional POI, respectively. We learn the long-term and short-term preferences of users by fusing the user embedding and POI ancillary information through gate recurrent unit. Finally, we conduct experiments on two real world location-based social network datasets Foursquare and Gowalla to verify the effectiveness of the proposed recommendation model and the experiments results show that SGANet outperformed the compared baseline models in terms of recommendation accuracy, especially in sparse data and cold start scenario.
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Boddupally, Sadwika Sri, Vangaveeti Kavya Sree, Valugula Sathwik, and Neha Thakur. "A Novel Time-Aware Food Recommender System based on Deep Learning and Graph Clustering." International Journal of Scientific Methods in Intelligence Engineering Networks 02, no. 03 (March 31, 2024): 26–33. http://dx.doi.org/10.58599/ijsmien.2024.2304.
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In the era of personalized recommendation systems, there exists a burgeoning need for algorithms capable of adapting to users’ preferences dynamically. Food recommendation systems face unique challenges due to the temporal nature of mealtime preferences and seasonal variations. This paper introduces a novel TimeAware Food Recommender System that integrates deep learning techniques (to capture individual preferences) with graph clustering methodologies to provide personalized and temporally relevant food suggestions. By amalgamating these approaches, the Time-Aware Food Recommender System offers recommendations that align with users’ tastes, mealtime preferences, and seasonal variations. Evaluation using real-world datasets demonstrates the superiority of the Time-Aware Food Recommender System over traditional recommendation methods, showcasing its potential for enhancing user satisfaction in food recommendation platforms.
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Chen, Hong, Ming Xin Gan, and Meng Zhao Song. "An Improved Recommendation Algorithm Based on Graph Model." Applied Mechanics and Materials 380-384 (August 2013): 1266–69. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.1266.
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According to the problem that the traditional search algorithms dont consider the needs of individuals, various recommender systems employing different data representations and recommendation methods are currently used to cope with these challenges. In this paper, inspired by the network-based user-item rating matrix, we introduce an improved algorithm which combines the similarity of items with a dynamic resource allocation process. To demonstrate its accuracy and usefulness, this paper compares the proposed algorithm with collaborative filtering algorithm using data from MovieLens. The evaluation shows that, the improved recommendation algorithm based on graph model achieves more accurate predictions and more reasonable recommendation than collaborative filtering algorithm or the basic graph model algorithm does.
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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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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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Pan, Zhiqiang, Fei Cai, Wanyu Chen, Chonghao Chen, and Honghui Chen. "Collaborative Graph Learning for Session-based Recommendation." ACM Transactions on Information Systems 40, no. 4 (October 31, 2022): 1–26. http://dx.doi.org/10.1145/3490479.
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Session-based recommendation (SBR) , which mainly relies on a user’s limited interactions with items to generate recommendations, is a widely investigated task. Existing methods often apply RNNs or GNNs to model user’s sequential behavior or transition relationship between items to capture her current preference. For training such models, the supervision signals are merely generated from the sequential interactions inside a session, neglecting the correlations of different sessions, which we argue can provide additional supervisions for learning the item representations. Moreover, previous methods mainly adopt the cross-entropy loss for training, where the user’s ground truth preference distribution towards items is regarded as a one-hot vector of the target item, easily making the network over-confident and leading to a serious overfitting problem. Thus, in this article, we propose a Collaborative Graph Learning (CGL) approach for session-based recommendation. CGL first applies the Gated Graph Neural Networks (GGNNs) to learn item embeddings and then is trained by considering both the main supervision as well as the self-supervision signals simultaneously. The main supervisions are produced by the sequential order while the self-supervisions are derived from the global graph constructed by all sessions. In addition, to prevent overfitting, we propose a Target-aware Label Confusion (TLC) learning method in the main supervised component. Extensive experiments are conducted on three publicly available datasets, i.e., Retailrocket, Diginetica, and Gowalla. The experimental results show that CGL can outperform the state-of-the-art baselines in terms of Recall and MRR.
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Tang, Jianchen, Bing Huang, and Mingshan Xie. "Anticancer Recipe Recommendation Based on Cancer Dietary Knowledge Graph." European Journal of Cancer Care 2023 (October 18, 2023): 1–13. http://dx.doi.org/10.1155/2023/8816960.
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Many recipes contain ingredients with various anticancer effects, which can help users to prevent cancer, as well as provide treatment for cancer patients, effectively slowing the disease. Existing recipe knowledge graph recommendation systems obtain entity feature representations by mining latent connections between recipes and between users and recipes to enhance the performance of the recommendation system. However, it ignores the influence of time on user taste preferences, fails to capture the dependency between them from the user’s dietary records, and is unable to more accurately predict the user’s future recipes. We use the KGAT to obtain the embedding representation of entities, considering the influence of time on users, and recipe recommendation can be viewed as a long-term sequence prediction, introducing LSTM networks to dynamically adjust users’ personal taste preferences. Based on the user’s dietary records, we infer the user’s preference for the future diet. Combined with the cancer knowledge graph, we provide the user with diet recommendations that are beneficial to disease prevention and rehabilitation. To verify the effectiveness and rationality of PPKG, we compared it with three other recommendation algorithms on the self-created datasets, and the extensive experimental results demonstrate that our algorithm performance performs other algorithms, which confirmed the effectiveness of PPKG in dealing with sequence recommendation.
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Yin, Pei, Ya Chen, Huan Wang, Hongcheng Gan, and Ye Zhou. "Recommending Crowdfunding Project: A Graph Kernel-Based Link Prediction Method for Extremely Sparse Implicit Feedback." Computational Intelligence and Neuroscience 2022 (July 19, 2022): 1–13. http://dx.doi.org/10.1155/2022/5126140.
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It is a critical task to provide recommendation on implicit feedback, and one of the biggest challenges is extreme data sparsity. To tackle the problem, a graph kernel-based link prediction method is proposed in this paper for recommending crowdfunding projects combining graph computing with collaborative filtering. First of all, an investor-project bipartite graph is established based on transaction histories. Then, a random walk graph kernel is constructed and computed, and a one-class SVM classifier is built for link prediction based on implicit feedback. At last, top N recommendations are made according to the ranking of investor-project pairs. Comparative experiments are conducted and the results show that the proposed method achieves the best performance on extremely sparse implicit feedback and outperforms baselines. This paper is of help to improve the success rate of crowdfunding by personalized recommendation and is of significance to enrich the research in recommendation systems.
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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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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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Duan, Ganglong, Shanshan Xie, and Yutong Du. "Study on a User Preference Conversational Recommender Based on a Knowledge Graph." Electronics 14, no. 3 (February 6, 2025): 632. https://doi.org/10.3390/electronics14030632.
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In the era of information explosion, as a form of personalized recommendation, dialogue recommendation systems provide users with personalized recommendation services through natural language interaction. However, in the face of complex user preferences, the traditional dialogue recommendation system has the problem of a poor recommendation effect. To solve these problems, this paper proposes a user preference dialogue recommendation algorithm (KGCR) based on a knowledge graph, which aims to enhance the understanding of user preferences through the semantic information of the knowledge graph and improve the relevance and accuracy of recommendations. This paper proposes a personalized conversation recommendation algorithm framework for user preference modeling. The framework uses a bilinear model attention mechanism and self-attention hierarchical coding structure to model user preferences to rank and recommend candidate items. By introducing rich user-related information, the recommendation results are not only more in line with users’ individual preferences but also have better diversity, effectively reducing the negative impact of information cocoons and other phenomena. At the same time, the experimental results on the open dataset prove the effectiveness and accuracy of the proposed model in the personalized conversation recommendation task.
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Zhang, Haojie, and Zhidong Shen. "News Recommendation Based on User Topic and Entity Preferences in Historical Behavior." Information 14, no. 2 (January 18, 2023): 60. http://dx.doi.org/10.3390/info14020060.
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A news-recommendation system is designed to deal with massive amounts of news and provide personalized recommendations for users. Accurately modeling of news and users is the key to news recommendation. Researchers usually use auxiliary information such as social networks or item attributes to learn about news and user representation. However, existing recommendation systems neglect to explore the rich topics in the news. This paper considered the knowledge graph as the source of side information. Meanwhile, we used user topic preferences to improve recommendation performance. We proposed a new framework called NRTEH that was based on topic and entity preferences in user historical behavior. The core of our approach was the news encoder and the user encoder. Two encoders in NRTEH handled news titles from two perspectives to obtain news and user representation embedding: (1) extracting explicit and latent topic features from news and mining user preferences for them; and (2) extracting entities and propagating users’ potential preferences in the knowledge graph. Experiments on a real-world dataset validated the effectiveness and efficiency of our approach.
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Syed, Muzamil Hussain, Tran Quoc Bao Huy, and Sun-Tae Chung. "Context-Aware Explainable Recommendation Based on Domain Knowledge Graph." Big Data and Cognitive Computing 6, no. 1 (January 20, 2022): 11. http://dx.doi.org/10.3390/bdcc6010011.
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With the rapid growth of internet data, knowledge graphs (KGs) are considered as efficient form of knowledge representation that captures the semantics of web objects. In recent years, reasoning over KG for various artificial intelligence tasks have received a great deal of research interest. Providing recommendations based on users’ natural language queries is an equally difficult undertaking. In this paper, we propose a novel, context-aware recommender system, based on domain KG, to respond to user-defined natural queries. The proposed recommender system consists of three stages. First, we generate incomplete triples from user queries, which are then segmented using logical conjunction (∧) and disjunction (∨) operations. Then, we generate candidates by utilizing a KGE-based framework (Query2Box) for reasoning over segmented logical triples, with ∧, ∨, and ∃ operators; finally, the generated candidates are re-ranked using neural collaborative filtering (NCF) model by exploiting contextual (auxiliary) information from GraphSAGE embedding. Our approach demonstrates to be simple, yet efficient, at providing explainable recommendations on user’s queries, while leveraging user-item contextual information. Furthermore, our framework has shown to be capable of handling logical complex queries by transforming them into a disjunctive normal form (DNF) of simple queries. In this work, we focus on the restaurant domain as an application domain and use the Yelp dataset to evaluate the system. Experiments demonstrate that the proposed recommender system generalizes well on candidate generation from logical queries and effectively re-ranks those candidates, compared to the matrix factorization model.
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Wu, Chao, Sannyuya Liu, Zeyu Zeng, Mao Chen, Adi Alhudhaif, Xiangyang Tang, Fayadh Alenezi, Norah Alnaim, and Xicheng Peng. "Knowledge graph-based multi-context-aware recommendation algorithm." Information Sciences 595 (May 2022): 179–94. http://dx.doi.org/10.1016/j.ins.2022.02.054.
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Durand, Guillaume, Nabil Belacel, and François LaPlante. "Graph theory based model for learning path recommendation." Information Sciences 251 (December 2013): 10–21. http://dx.doi.org/10.1016/j.ins.2013.04.017.
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Guo, Taolin, Junzhou Luo, Kai Dong, and Ming Yang. "Differentially private graph-link analysis based social recommendation." Information Sciences 463-464 (October 2018): 214–26. http://dx.doi.org/10.1016/j.ins.2018.06.054.
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Chen, Dan Er, and Yu Long Ying. "A Collaborative Filtering Recommendation Algorithm Based on Bipartite Graph." Advanced Materials Research 756-759 (September 2013): 3865–68. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.3865.
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With the rapid growth and wide application of Internet, everyday there are many of information generated and the existence of a large amount of information makes it hardly to mining the wanted information. The recommendation algorithm is the process to alleviative the problem. Collaborative filtering algorithm is one successful personalized recommendation technology, and is widely used in many fields. But traditional collaborative filtering algorithm has the problem of sparsity, which will influence the efficiency of prediction. In this paper, a collaborative filtering recommendation algorithm based on bipartite graph is proposed. The algorithm takes users, items and tags into account, and also studies the degree of tags which may affect the similarity of users. The collaborative filtering recommendation algorithm based on bipartite graph can alleviate the sparsity problem in the electronic commerce recommender systems.
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Tan, Yunfei, Shuyu Li, and Zehua Li. "A privacy preserving recommendation and fraud detection method based on graph convolution." Electronic Research Archive 31, no. 12 (2023): 7559–77. http://dx.doi.org/10.3934/era.2023382.
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<abstract> <p>As a typical deep learning technique, Graph Convolutional Networks (GCN) has been successfully applied to the recommendation systems. Aiming at the leakage risk of user privacy and the problem of fraudulent data in the recommendation systems, a Privacy Preserving Recommendation and Fraud Detection method based on Graph Convolution (PPRFD-GC) is proposed in the paper. The PPRFD-GC method adopts encoder/decoder framework to generate the synthesized graph of rating information which satisfies edge differential privacy, next applies graph-based matrix completion technique for rating prediction according to the synthesized graph. After calculating user's Mean Square Error (MSE) of rating prediction and generating dense representation of the user, then a fraud detection classifier based on AdaBoost is presented to identify possible fraudsters. Finally, the loss functions of both rating prediction module and fraud detection module are linearly combined as the overall loss function. The experimental analysis on two real datasets shows that the proposed method has good recommendation accuracy and anti-fraud attack characteristics on the basis of preserving users' link privacy.</p> </abstract>
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Wang, Youwei, Weihui Dai, and Yufei Yuan. "Website browsing aid: A navigation graph-based recommendation system." Decision Support Systems 45, no. 3 (June 2008): 387–400. http://dx.doi.org/10.1016/j.dss.2007.05.006.
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Liu, Xi, Rui Song, Yuhang Wang, and Hao Xu. "A Multi-Granular Aggregation-Enhanced Knowledge Graph Representation for Recommendation." Information 13, no. 5 (April 29, 2022): 229. http://dx.doi.org/10.3390/info13050229.
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Knowledge graph (KG) helps to improve the accuracy, diversity, and interpretability of a recommender systems. KG has been applied in recommendation systems, exploiting graph neural networks (GNNs), but most existing recommendation models based on GNNs ignore the influence of node types and the loss of information during aggregation. In this paper, we propose a new model, named A Multi-Granular Aggregation-Enhanced Knowledge Graph Representation for Recommendation (MAKR), that relieves the sparsity of the network and overcomes the limitation of information loss of the traditional GNN recommendation model. Specifically, we propose a new graph, named the Improved Collaborative Knowledge Graph (ICKG), that integrates user–item interaction and a knowledge graph into a huge heterogeneous network, divides the nodes in the heterogeneous network into three categories—users, items, and entities, and connects the edges according to the similarity between the users and items so as to enhance the high-order connectivity of the graph. In addition, we used attention mechanisms, the factorization machine (FM), and transformer (Trm) algorithms to aggregate messages from multi-granularity and different types to improve the representation ability of the model. The empirical results of three public benchmarks showed that MAKR outperformed state-of-the-art methods such as Neural FM, RippleNet, and KGAT.
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Gu, Junlin, Yihan Xu, and Weiwei Liu. "RWESA-GNNR: Fusing Random Walk Embedding and Sentiment Analysis for Graph Neural Network Recommendation." Information Technology and Control 53, no. 1 (March 22, 2024): 146–59. http://dx.doi.org/10.5755/j01.itc.53.1.33495.
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A graph neural network-based recommendation system treats the relationship between user items as a graph, and achieves deep feature mining by modelling the graph nodes. However, the complexity of the features of graph neural network-based recommendation systems brings poor interpretability and suffers from data sparsity problems. To address the above problems, a graph convolutional neural network recommendation model (RWESA-GNNR) based on random walk embedding combined with sentiment analysis is proposed. Firstly, a random walk-based matrix factorization is designed as the initial embedding. Secondly, the user and item nodes are modelled using a convolutional neural network with an injected attention mechanism. Then, sentiment analysis is performed on the review text, and attention mechanism is introduced to fuse text sentiment features and semantic features. Finally, node features and text features are aggregated to generate recommendation results. The experimental results show that our proposed algorithm outperforms traditional recommendation algorithms and other graph neural network-based recommendation algorithms in terms of recommendation results, with an improvement of about 2.43%-5.75%.
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Li, Yue. "A Graph Convolution Network Based on Improved Density Clustering for Recommendation System." Information Technology and Control 51, no. 1 (March 26, 2022): 18–31. http://dx.doi.org/10.5755/j01.itc.51.1.28720.
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Recommendation systems have been widely used in various applications to solve information overload andimprove user experience. Traditional recommendation algorithms mainly used Euclidean data for calculationand abandoned the graph structure features in user and item data. Aiming at the problems in the current recommendation algorithms, this paper proposes an improved user density clustering method and extracts userfeatures through optimized graph neural network. Firstly, the improved density clustering method is used toform the clustering subgraph of users based on the influence value of users. Secondly, the user data and itemdata features of cluster subgraph are extracted by graph convolution network. Finally, the features of clustersubgraphs are processed by global graph convolution network and the recommendation results are generatedaccording to the global graph features. This model not only improves the efficiency of decomposing large graphinto small graph through the improved user density clustering algorithm, but also extracts the features of usergroups through graph convolution neural network to improve the recommendation effect. The experiment alsoproves the validity of this model.
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Jiang, Feng, Yang Cao, Huan Wu, Xibin Wang, Yuqi Song, and Min Gao. "Social Recommendation Based on Multi-Auxiliary Information Constrastive Learning." Mathematics 10, no. 21 (November 5, 2022): 4130. http://dx.doi.org/10.3390/math10214130.
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Social recommendation can effectively alleviate the problems of data sparseness and the cold start of recommendation systems, attracting widespread attention from researchers and industry. Current social recommendation models use social relations to alleviate the problem of data sparsity and improve recommendation performance. Although self-supervised learning based on user–item interaction can enhance the performance of such models, multi-auxiliary information is neglected in the learning process. Therefore, we propose a model based on self-supervision and multi-auxiliary information using multi-auxiliary information, such as user social relationships and item association relationships, to make recommendations. Specifically, the user social relationship and item association relationship are combined to form a multi-auxiliary information graph. The user–item interaction relationship is also integrated into the same heterogeneous graph so that multiple pieces of information can be spread in the same graph. In addition, we utilize the graph convolution method to learn user and item embeddings, whereby the user embeddings reflect both user–item interaction and user social relationships, and the item embeddings reflect user–item interaction and item association relationships. We also design multi-view self-supervising auxiliary tasks based on the constructed multi-auxiliary views. Signals generated by self-supervised auxiliary tasks can alleviate the problem of data sparsity, further improving user/item embedding quality and recommendation performance. Extensive experiments on two public datasets verify the superiority of the proposed model.
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Yong, Jiu, Jianguo Wei, Xiaomei Lei, Jianwu Dang, Wenhuan Lu, and Meijuan Cheng. "A Learning Resource Recommendation Method Based on Graph Contrastive Learning." Electronics 14, no. 1 (January 1, 2025): 142. https://doi.org/10.3390/electronics14010142.
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The existing learning resource recommendation systems suffer from data sparsity and missing data labels, leading to the insufficient mining of the correlation between users and courses. To address these issues, we propose a learning resource recommendation method based on graph contrastive learning, which uses graph contrastive learning to construct an auxiliary recommendation task combined with a main recommendation task, achieving the joint recommendation of learning resources. Firstly, the interaction bipartite graph between the user and the course is input into a lightweight graph convolutional network, and the embedded representation of each node in the graph is obtained after compilation. Then, for the input user–course interaction bipartite graph, noise vectors are randomly added to each node in the embedding space to perturb the embedding of graph encoder node, forming a perturbation embedding representation of the node to enhance the data. Subsequently, the graph contrastive learning method is used to construct auxiliary recommendation tasks. Finally, the main task of recommendation supervision and the constructed auxiliary task of graph contrastive learning are jointly learned to alleviate data sparsity. The experimental results show that the proposed method in this paper has improved the Recall@5 by 5.7% and 11.2% and the NDCG@5 by 0.1% and 6.4%, respectively, on the MOOCCube and Amazon-Book datasets compared with the node enhancement methods. Therefore, the proposed method can significantly improve the mining level of users and courses by using a graph comparison method in the auxiliary recommendation task and has better noise immunity and robustness.
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Wenige, Lisa, and Johannes Ruhland. "Similarity-based knowledge graph queries for recommendation retrieval." Semantic Web 10, no. 6 (October 28, 2019): 1007–37. http://dx.doi.org/10.3233/sw-190353.
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Zhang, Dehai, Linan Liu, Qi Wei, Yun Yang, Po Yang, and Qing Liu. "Neighborhood Aggregation Collaborative Filtering Based on Knowledge Graph." Applied Sciences 10, no. 11 (May 30, 2020): 3818. http://dx.doi.org/10.3390/app10113818.
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In recent years, the research of combining a knowledge graph with recommendation systems has caused widespread concern. By studying the interconnections in knowledge graphs, potential connections between users and items can be discovered, which provides abundant and complementary information for recommendation of items. However, most existing studies have not effectively established the relation between entities and users. Therefore, the recommendation results may be affected by some unrelated entities. In this paper, we propose a neighborhood aggregation collaborative filtering (NACF) based on knowledge graph. It uses the knowledge graph to spread and extract the user’s potential interest, and iteratively injects them into the user features with attentional deviation. We conducted a large number of experiments on three public datasets; we verifyied that NACF is ahead of the most advanced models in top-k recommendation and click-through rate (CTR) prediction.
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Tang, Hao, Guoshuai Zhao, Xuxiao Bu, and Xueming Qian. "Dynamic evolution of multi-graph based collaborative filtering for recommendation systems." Knowledge-Based Systems 228 (September 2021): 107251. http://dx.doi.org/10.1016/j.knosys.2021.107251.
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Kefalas, Pavlos, Panagiotis Symeonidis, and Yannis Manolopoulos. "A Graph-Based Taxonomy of Recommendation Algorithms and Systems in LBSNs." IEEE Transactions on Knowledge and Data Engineering 28, no. 3 (March 1, 2016): 604–22. http://dx.doi.org/10.1109/tkde.2015.2496344.
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Yi, Huawei, Jingtong Liu, Wenqian Xu, Xiaohui Li, and Huihui Qian. "A Graph Neural Network Social Recommendation Algorithm Integrating the Multi-Head Attention Mechanism." Electronics 12, no. 6 (March 21, 2023): 1477. http://dx.doi.org/10.3390/electronics12061477.
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Collaborative filtering recommendation systems are facing the data sparsity problem associated with interaction data, and social recommendations introduce user social information to alleviate this problem. Existing social recommendation methods cannot express the user interaction interest and social influence deeply, which limits the recommendation performance of the system. To address this problem, in this paper we propose a graph neural network social recommendation algorithm integrating multi-head attention mechanism. First, based on the user-item interaction graph and social network graph, the graph neural network is used to learn the high-order relationship between users and items and deeply extract the latent features of users and items. In the process of learning user embedding vector representation based on the social network graph, the multi-head attention mechanism is introduced to increase the importance of friends with high influence. Then, we make rating predictions for the target users according to the learned user embedding vector representation and item embedding vector. The experimental results on the Epinions dataset show that the proposed method outperforms the existing methods in terms of both Recall and Normalized Discounted Cumulative Gain.
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Long, Fei. "Research of the Context Recommendation Algorithm Based on the Tripartite Graph Model in Complex Systems." Complexity 2020 (October 5, 2020): 1–11. http://dx.doi.org/10.1155/2020/7945417.
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With the rapid development of information technology, the information overload has become a very serious problem in web information environment. The personalized recommendation came into being. Current recommending algorithms, however, are facing a series of challenges. To solve the problem of the complex context, a new context recommendation algorithm based on the tripartite graph model is proposed for the three-dimensional model in complex systems. Improving the accuracy of the recommendation by the material diffusion, through the heat conduction to improve the diversity of the recommended objects, and balancing the accuracy and diversity through the integration of resources thus realize the personalized recommendation. The experimental results show that the proposed context recommendation algorithm based on the tripartite graph model is superior to other traditional recommendation algorithms in recommendation performance.
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MURALI MOHANA KRISHNA DANDU, Vishwasrao Salunkhe, Shashwat Agrawal, Prof.(Dr) Punit Goel, and Vikhyat Gupta. "Knowledge Graphs for Personalized Recommendations." Innovative Research Thoughts 9, no. 1 (March 30, 2023): 450–79. http://dx.doi.org/10.36676/irt.v9.i1.1497.
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Knowledge graphs have emerged as a transformative tool in enhancing personalized recommendation systems. By integrating diverse datasets into a structured semantic network, knowledge graphs offer a holistic view of relationships and entities that can significantly improve the relevance and accuracy of recommendations. Unlike traditional recommendation algorithms that rely primarily on user behaviour and item similarity, knowledge graphs leverage contextual information and complex interconnections among entities to deliver more nuanced and context-aware suggestions. This abstract explores the pivotal role of knowledge graphs in advancing personalized recommendation systems, focusing on their ability to capture intricate relationships between users, items, and attributes. By mapping out these relationships, knowledge graphs facilitate a deeper understanding of user preferences and item characteristics, enabling the generation of more tailored and precise recommendations. Additionally, the incorporation of external knowledge sources into the graph can further enrich the recommendation process, leading to enhanced user satisfaction and engagement. The paper reviews various methodologies for integrating knowledge graphs into recommendation systems, including graph-based algorithms and machine learning techniques. It also examines real-world applications and case studies where knowledge graphs have demonstrated substantial improvements in recommendation quality. Ultimately, the utilization of knowledge graphs represents a significant leap forward in personalizing user experiences, offering a promising avenue for future research and development in the field of recommendation systems.
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Aldayel, Mashael, Abeer Al-Nafjan, Waleed M. Al-Nuwaiser, Ghadeer Alrehaili, and Ghadi Alyahya. "Collaborative Filtering-Based Recommendation Systems for Touristic Businesses, Attractions, and Destinations." Electronics 12, no. 19 (September 27, 2023): 4047. http://dx.doi.org/10.3390/electronics12194047.
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The success of touristic businesses, attractions, and destinations heavily relies on travel agents’ recommendations, which significantly impact client satisfaction. However, the underlying recommendation process employed by travel agents remains poorly understood. This study presents a conceptual model of the recommendation process and empirically investigates the influence of tourism categories on agents’ destination recommendations. By employing collaborative filtering-based recommendation systems and comparing various algorithms, including matrix factorization and deep learning models, such as the bilateral variational autoencoder (BiVAE) and light graph convolutional neural network, this research provides insights into the performance of different techniques in the context of tourism. The models were evaluated using a tourism dataset and assessed through a range of metrics. The results indicate that the BiVAE algorithm outperformed others in terms of ranking and prediction metrics, underscoring the significance of considering multiple measurements and exploring diverse techniques. The findings have practical implications for tourism marketers seeking to influence travel agents and offer valuable insights for researchers investigating this domain. Additionally, the proposed model holds potential for applications in travel recommendation systems, including attraction recommendations.