Thematische Bibliographien / Graph-Based Recommendation Systems

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Auswahl der wissenschaftlichen Literatur zum Thema „Graph-Based Recommendation Systems“

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Veröffentlicht am 15. Februar 2025

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Zeitschriftenartikel zum Thema "Graph-Based Recommendation Systems"

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Pandey, Vivek, und Padma Bonde. „Graph based Recommendation for Distributed Systems“. International Journal of Computer Applications 168, Nr. 4 (15.06.2017): 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, Nr. 1 (05.07.2024): 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 und Dr Sudha Narang. „Recommendation Systems using Graph Neural Networks“. International Journal for Research in Applied Science and Engineering Technology 11, Nr. 1 (31.01.2023): 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, und Ziwei Chen. „SocialJGCF: Social Recommendation with Jacobi Polynomial-Based Graph Collaborative Filtering“. Applied Sciences 14, Nr. 24 (23.12.2024): 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 und Honghui Chen. „Graph Co-Attentive Session-based Recommendation“. ACM Transactions on Information Systems 40, Nr. 4 (31.10.2022): 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 und Zhikang Xu. „Financial news recommendation based on graph embeddings“. Decision Support Systems 125 (Oktober 2019): 113115. http://dx.doi.org/10.1016/j.dss.2019.113115.

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Tolety, Venkata Bhanu Prasad, und Evani Venkateswara Prasad. „Graph Neural Networks for E-Learning Recommendation Systems“. International Journal on Recent and Innovation Trends in Computing and Communication 11, Nr. 9s (31.08.2023): 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 und Shiyang Wang. „Hierarchical Social Recommendation Model Based on a Graph Neural Network“. Wireless Communications and Mobile Computing 2021 (31.08.2021): 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 und Junying Cui. „A Personalized Collaborative Filtering Recommendation System Based on Bi-Graph Embedding and Causal Reasoning“. Entropy 26, Nr. 5 (28.04.2024): 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 (14.07.2023): 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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Dissertationen zum Thema "Graph-Based Recommendation Systems"

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Huang, Zan. „GRAPH-BASED ANALYSIS FOR E-COMMERCE RECOMMENDATION“. Diss., Tucson, Arizona : University of Arizona, 2005. http://etd.library.arizona.edu/etd/GetFileServlet?file=file:///data1/pdf/etd/azu%5Fetd%5F1167%5F1%5Fm.pdf&type=application/pdf.

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Ozturk, Gizem. „A Hybrid Veideo Recommendation System Based On A Graph Based Algorithm“. Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612624/index.pdf.

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This thesis proposes the design, development and evaluation of a hybrid video recommendation system. The proposed hybrid video recommendation system is based on a graph algorithm called Adsorption. Adsorption is a collaborative filtering algorithm in which relations between users are used to make recommendations. Adsorption is used to generate the base recommendation list. In order to overcome the problems that occur in pure collaborative system, content based filtering is injected. Content based filtering uses the idea of suggesting similar items that matches user preferences. In order to use content based filtering, first, the base recommendation list is updated by removing weak recommendations. Following this, item similarities of the remaining list are calculated and new items are inserted to form the final recommendations. Thus, collaborative recommendations are empowered considering item similarities. Therefore, the developed hybrid system combines both collaborative and content based approaches to produce more effective suggestions.
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Bereczki, Márk. „Graph Neural Networks for Article Recommendation based on Implicit User Feedback and Content“. Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-300092.

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Recommender systems are widely used in websites and applications to help users find relevant content based on their interests. Graph neural networks achieved state- of-the- art results in the field of recommender systems, working on data represented in the form of a graph. However, most graph- based solutions hold challenges regarding computational complexity or the ability to generalize to new users. Therefore, we propose a novel graph- based recommender system, by modifying Simple Graph Convolution, an approach for efficient graph node classification, and add the capability of generalizing to new users. We build our proposed recommender system for recommending the articles of Peltarion Knowledge Center. By incorporating two data sources, implicit user feedback based on pageview data as well as the content of articles, we propose a hybrid recommender solution. Throughout our experiments, we compare our proposed solution with a matrix factorization approach as well as a popularity- based and a random baseline, analyse the hyperparameters of our model, and examine the capability of our solution to give recommendations to new users who were not part of the training data set. Our model results in slightly lower, but similar Mean Average Precision and Mean Reciprocal Rank scores to the matrix factorization approach, and outperforms the popularity- based and random baselines. The main advantages of our model are computational efficiency and its ability to give relevant recommendations to new users without the need for retraining the model, which are key features for real- world use cases.
Rekommendationssystem används ofta på webbplatser och applikationer för att hjälpa användare att hitta relevant innehåll baserad på deras intressen. Med utvecklingen av grafneurala nätverk nådde toppmoderna resultat inom rekommendationssystem och representerade data i form av en graf. De flesta grafbaserade lösningar har dock svårt med beräkningskomplexitet eller att generalisera till nya användare. Därför föreslår vi ett nytt grafbaserat rekommendatorsystem genom att modifiera Simple Graph Convolution. De här tillvägagångssätt är en effektiv grafnodsklassificering och lägga till möjligheten att generalisera till nya användare. Vi bygger vårt föreslagna rekommendatorsystem för att rekommendera artiklarna från Peltarion Knowledge Center. Genom att integrera två datakällor, implicit användaråterkoppling baserad på sidvisningsdata samt innehållet i artiklar, föreslår vi en hybridrekommendatörslösning. Under våra experiment jämför vi vår föreslagna lösning med en matrisfaktoriseringsmetod samt en popularitetsbaserad och en slumpmässig baslinje, analyserar hyperparametrarna i vår modell och undersöker förmågan hos vår lösning att ge rekommendationer till nya användare som inte deltog av träningsdatamängden. Vår modell resulterar i något mindre men liknande Mean Average Precision och Mean Reciprocal Rank poäng till matrisfaktoriseringsmetoden och överträffar de popularitetsbaserade och slumpmässiga baslinjerna. De viktigaste fördelarna med vår modell är beräkningseffektivitet och dess förmåga att ge relevanta rekommendationer till nya användare utan behov av omskolning av modellen, vilket är nyckelfunktioner för verkliga användningsfall.
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Attolou, Hervé-Madelein. „Explications pour des recommandations manquantes basées sur les graphes“. Electronic Thesis or Diss., CY Cergy Paris Université, 2024. http://www.theses.fr/2024CYUN1337.

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Cette thèse explore le domaine spécifique des explications du type "Pourquoipas" (recommandations manquantes), qui se concentrent sur l'explication del'absence de certains éléments dans la liste de recommandations. Le besoind'explications recommandations manquantes est particulièrement crucial dans desscénarios de recommandation complexes, où l'absence de certaines recommandationspeut entraîner l'insatisfaction ou la méfiance des utilisateurs. Par exemple, un util-isateur d'une plateforme de commerce en ligne pourrait se demander pourquoi unproduit spécifique n'a pas été recommandé malgré le fait qu'il remplissait certainscritères. En fournissant des explications sur les recommandations manquantes, nousvisons à améliorer la transparence, la satisfaction des utilisateurs, leur engagementet la fiabilité globale du système.La principale contribution de cette thèse est le développement d'EMiGRe (Ex-plainable Missing Graph REcommender), une infrastructure algorithmique novatricequi fournit des explications actionnables pour les recommandations manquantesdans les systèmes de recommandation basés sur les graphes. Contrairement auxméthodes d'explicabilité traditionnelles, qui se concentrent sur la justification deséléments recommandés, EMiGRe se concentre sur l'absence d'éléments spécifiquesdans les listes de recommandations. L'infrastructure fonctionne en analysant les in-teractions de l'utilisateur dans une modélisation de graphe d'information hétérogèned'un ensemble de données, en identifiant les actions ou relations clés qui, lorsqu'ellessont modifiées, auraient conduit à la recommandation de l'élément manquant. EMi-GRe propose deux modes d'explication :- Le mode Suppression identifie les actions ou interactions existantes qui em-pêchent le système de recommander l'élément souhaité et suggère de les supprimer.- Le mode Ajout propose des actions ou des éléments supplémentaires qui, s'ilsétaient utilisés, déclencheraient la recommandation de l'élément manquant.Pour générer des explications dans les deux modes Ajout et Suppression, nousexplorons l'espace de solutions à l'aide d'un ensemble d'heuristiques adaptées àdes objectifs spécifiques. Le cadre offre plusieurs heuristiques, chacune servant unobjectif : l'heuristique incrémentale privilégie un calcul plus rapide en augmentantprogressivement le nombre d'éléments sélectionnés, potentiellement au détrimentd'explications minimales. En revanche, l'heuristique combinatoire vise à trouver desexplications plus petites en explorant minutieusement l'espace de solutions. De plus,une heuristique de comparaison exhaustive est incluse pour évaluer la contributionprécise de chaque voisin à l'élément manquant par rapport à tous les autres éléments,augmentant ainsi le taux de succès.Pour valider l'efficacité du cadre EMiGRe des évaluations expérimentales ap-profondies ont été menées sur des jeux de données synthétiques et réels. Les jeux dedonnées incluent des données provenant de sources telles qu'Amazon, simulant unscénario de commerce en ligne réel, ainsi que le jeu de données Food.com représentantun problème de recommandation sur une plateforme de recettes. Les résultats ex-périmentaux montrent qu'EMiGRe est capable de fournir des explications de bonnequalité pour les recommandations manquantes avec un minimum de surcharge com-putationnelle. En particulier, le système démontre une amélioration significativedes taux de succès des explications par rapport aux méthodes traditionnelles deforce brute, tout en maintenant une taille d'explication et un temps de traitementacceptables.De plus, cette thèse introduit une nouvelle évaluation des explications des recom-mandations manquantes, en définissant des métriques telles que le taux de succès, lataille de l'explication et le temps de traitement pour mesurer la qualité et l'efficacitédes explications
In the era of big data, Recommendation Systems play a pivotal role in helping users navigate and discover relevant content from vast amounts of data. Whilemodern Recommendation Systems have evolved to provide accurate and relevant recommendations, they often fall short in explaining their decisions to users. Thislack of transparency raises important questions about trust and user engagement, especially in cases where certain expected items are not recommended. To addressthis, recent research has focused on developing explainable Recommendation Systems, which provide users with insights into why certain items are recommended oromitted.This thesis explores the specific area of Why-Not Explanations, which focuses on explaining why certain items are missing from the recommendation list. Theneed for Why-Not Explanations is particularly crucial in complex recommendation scenarios, where the absence of certain recommendations can lead to user dissatisfaction or mistrust. For instance, a user on an e-commerce platform might wonder why a specific product was not recommended despite fulfilling certain criteria. By providing explanations for missing recommendations, we aim to improve transparency, user satisfaction, engagement, and the overall trustworthiness of the system.The main contribution of this thesis is the development of EMiGRe (Explainable Missing Graph REcommender), a novel framework that provides actionable Why-Not Explanations for graph-based Recommendation Systems. Unlike traditional explainability methods, which focus on justifying why certain items were recommended, EMiGRe focuses on the absence of specific items from recommendation lists. The framework operates by analyzing the user's interactions within a Heterogeneous Information Graph (HIN) modelization of a dataset, identifying key actions or relations that, when modified, would have led to the recommendation of the missing item. EMiGRe provides two modes for explanation:• Remove Mode identifies existing actions or interactions that are preventing the system from recommending the desired item and suggests removing these.• Add Mode suggests additional actions or items that, if interacted with, would trigger the recommendation of the missing item.To generate explanations in both Add and Remove modes, we explore the solution space using a set of heuristics tailored for specific objectives. The framework offers multiple heuristics each serving a purpose: Incremental Powerset an Exhaustive Comparison . The Incremental heuristic prioritizes faster computation by gradually increasing the set of selected items, potentially overlooking minimal explanations. In contrast, the Powerset heuristic aims to find smaller explanations by thoroughly searching the solution space. Additionally, Exhaustive Comparison comparison heuristic is included to assess the precise contribution of each neighbor to the Why-Not Item (W NI) compared to all other items, increasing the success rate.To validate the effectiveness of the EMiGRe framework, extensive experimental evaluations were conducted on both synthetic and real-world datasets. The datasets include datasets from sources like Amazon, which simulates a real-world e-commerce scenario, and the Food dataset representing a recommendation problemin a recipe-based platform. The experimental results show that EMiGRe is able to provide good-quality Why-Not Explanations. Specifically, the system demonstratesan improvement in explanation success rates compared to traditional brute-force methods, while maintaining acceptable explanation size and processing time.Moreover, this thesis introduces a novel evaluation for Why-Not Explanations, defining metrics such as success rate, explanation size, and processing time to measure the quality and efficiency of explanations
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Lisena, Pasquale. „Knowledge-based music recommendation : models, algorithms and exploratory search“. Electronic Thesis or Diss., Sorbonne université, 2019. http://www.theses.fr/2019SORUS614.

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Représenter l'information décrivant la musique est une activité complexe, qui implique différentes sous-tâches. Ce manuscrit de thèse porte principalement sur la musique classique et étudie comment représenter et exploiter ses informations. L'objectif principal est l'étude de stratégies de représentation et de découverte des connaissances appliquées à la musique classique, dans des domaines tels que la production de base de connaissances, la prédiction de métadonnées et les systèmes de recommandation. Nous proposons une architecture pour la gestion des métadonnées de musique à l'aide des technologies du Web Sémantique. Nous introduisons une ontologie spécialisée et un ensemble de vocabulaires contrôlés pour les différents concepts spécifiques à la musique. Ensuite, nous présentons une approche de conversion des données, afin d’aller au-delà de la pratique bibliothécaire actuellement utilisée, en s’appuyant sur des règles de mapping et sur l’interconnexion avec des vocabulaires contrôlés. Enfin, nous montrons comment ces données peuvent être exploitées. En particulier, nous étudions des approches basées sur des plongements calculés sur des métadonnées structurées, des titres et de la musique symbolique pour classer et recommander de la musique. Plusieurs applications de démonstration ont été réalisées pour tester les approches et les ressources précédentes
Representing the information about music is a complex activity that involves different sub-tasks. This thesis manuscript mostly focuses on classical music, researching how to represent and exploit its information. The main goal is the investigation of strategies of knowledge representation and discovery applied to classical music, involving subjects such as Knowledge-Base population, metadata prediction, and recommender systems. We propose a complete workflow for the management of music metadata using Semantic Web technologies. We introduce a specialised ontology and a set of controlled vocabularies for the different concepts specific to music. Then, we present an approach for converting data, in order to go beyond the librarian practice currently in use, relying on mapping rules and interlinking with controlled vocabularies. Finally, we show how these data can be exploited. In particular, we study approaches based on embeddings computed on structured metadata, titles, and symbolic music for ranking and recommending music. Several demo applications have been realised for testing the previous approaches and resources
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„Graph-based recommendation with label propagation“. 2011. http://library.cuhk.edu.hk/record=b5894820.

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Wang, Dingyan.
Thesis (M.Phil.)--Chinese University of Hong Kong, 2011.
Includes bibliographical references (p. 97-110).
Abstracts in English and Chinese.
Abstract --- p.ii
Acknowledgement --- p.vi
Chapter 1 --- Introduction --- p.1
Chapter 1.1 --- Overview --- p.1
Chapter 1.2 --- Motivations --- p.6
Chapter 1.3 --- Contributions --- p.9
Chapter 1.4 --- Organizations of This Thesis --- p.11
Chapter 2 --- Background --- p.14
Chapter 2.1 --- Label Propagation Learning Framework --- p.14
Chapter 2.1.1 --- Graph-based Semi-supervised Learning --- p.14
Chapter 2.1.2 --- Green's Function Learning Framework --- p.16
Chapter 2.2 --- Recommendation Methods --- p.19
Chapter 2.2.1 --- Traditional Memory-based Methods --- p.19
Chapter 2.2.2 --- Traditional Model-based Methods --- p.20
Chapter 2.2.3 --- Label Propagation Recommendation Models --- p.22
Chapter 2.2.4 --- Latent Feature Recommendation Models . --- p.24
Chapter 2.2.5 --- Social Recommendation Models --- p.25
Chapter 2.2.6 --- Tag-based Recommendation Models --- p.25
Chapter 3 --- Recommendation with Latent Features --- p.28
Chapter 3.1 --- Motivation and Contributions --- p.28
Chapter 3.2 --- Item Graph --- p.30
Chapter 3.2.1 --- Item Graph Definition --- p.30
Chapter 3.2.2 --- Item Graph Construction --- p.31
Chapter 3.3 --- Label Propagation Recommendation Model with Latent Features --- p.33
Chapter 3.3.1 --- Latent Feature Analysis --- p.33
Chapter 3.3.2 --- Probabilistic Matrix Factorization --- p.35
Chapter 3.3.3 --- Similarity Consistency Between Global and Local Views (SCGL) --- p.39
Chapter 3.3.4 --- Item-based Green's Function Recommendation Based on SCGL --- p.41
Chapter 3.4 --- Experiments --- p.41
Chapter 3.4.1 --- Dataset --- p.43
Chapter 3.4.2 --- Baseline Methods --- p.43
Chapter 3.4.3 --- Metrics --- p.45
Chapter 3.4.4 --- Experimental Procedure --- p.45
Chapter 3.4.5 --- Impact of Weight Parameter u --- p.46
Chapter 3.4.6 --- Performance Comparison --- p.48
Chapter 3.5 --- Summary --- p.50
Chapter 4 --- Recommendation with Social Network --- p.51
Chapter 4.1 --- Limitation and Contributions --- p.51
Chapter 4.2 --- A Social Recommendation Framework --- p.55
Chapter 4.2.1 --- Social Network --- p.55
Chapter 4.2.2 --- User Graph --- p.57
Chapter 4.2.3 --- Social-User Graph --- p.59
Chapter 4.3 --- Experimental Analysis --- p.60
Chapter 4.3.1 --- Dataset --- p.61
Chapter 4.3.2 --- Metrics --- p.63
Chapter 4.3.3 --- Experiment Setting --- p.64
Chapter 4.3.4 --- Impact of Control Parameter u --- p.65
Chapter 4.3.5 --- Performance Comparison --- p.67
Chapter 4.4 --- Summary --- p.69
Chapter 5 --- Recommendation with Tags --- p.71
Chapter 5.1 --- Limitation and Contributions --- p.71
Chapter 5.2 --- Tag-Based User Modeling --- p.75
Chapter 5.2.1 --- Tag Preference --- p.75
Chapter 5.2.2 --- Tag Relevance --- p.78
Chapter 5.2.3 --- User Interest Similarity --- p.80
Chapter 5.3 --- Tag-Based Label Propagation Recommendation --- p.83
Chapter 5.4 --- Experimental Analysis --- p.84
Chapter 5.4.1 --- Douban Dataset --- p.85
Chapter 5.4.2 --- Experiment Setting --- p.86
Chapter 5.4.3 --- Metrics --- p.87
Chapter 5.4.4 --- Impact of Tag and Rating --- p.88
Chapter 5.4.5 --- Performance Comparison --- p.90
Chapter 5.5 --- Summary --- p.92
Chapter 6 --- Conclusions and Future Work --- p.94
Chapter 6.0.1 --- Conclusions --- p.94
Chapter 6.0.2 --- Future Work --- p.96
Bibliography --- p.97
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Silva, Ricardo Manuel Gonçalves da. „Knowledge Graph-Based Recipe Recommendation System“. Master's thesis, 2020. https://hdl.handle.net/10216/132659.

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Silva, Ricardo Manuel Gonçalves da. „Knowledge Graph-Based Recipe Recommendation System“. Dissertação, 2020. https://hdl.handle.net/10216/132659.

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Salamat, Amirreza. „Heterogeneous Graph Based Neural Network for Social Recommendations with Balanced Random Walk Initialization“. Thesis, 2020. http://hdl.handle.net/1805/24769.

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Indiana University-Purdue University Indianapolis (IUPUI)
Research on social networks and understanding the interactions of the users can be modeled as a task of graph mining, such as predicting nodes and edges in networks. Dealing with such unstructured data in large social networks has been a challenge for researchers in several years. Neural Networks have recently proven very successful in performing predictions on number of speech, image, and text data and have become the de facto method when dealing with such data in a large volume. Graph NeuralNetworks, however, have only recently become mature enough to be used in real large-scale graph prediction tasks, and require proper structure and data modeling to be viable and successful. In this research, we provide a new modeling of the social network which captures the attributes of the nodes from various dimensions. We also introduce the Neural Network architecture that is required for optimally utilizing the new data structure. Finally, in order to provide a hot-start for our model, we initialize the weights of the neural network using a pre-trained graph embedding method. We have also developed a new graph embedding algorithm. We will first explain how previous graph embedding methods are not optimal for all types of graphs, and then provide a solution on how to combat those limitations and come up with a new graph embedding method.
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(9740444), Amirreza Salamat. „Heterogeneous Graph Based Neural Network for Social Recommendations with Balanced Random Walk Initialization“. Thesis, 2021.

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Annotation:
Research on social networks and understanding the interactions of the users can be modeled as a task of graph mining, such as predicting nodes and edges in networks.Dealing with such unstructured data in large social networks has been a challenge for researchers in several years. Neural Networks have recently proven very successful in performing predictions on number of speech, image, and text data and have become the de facto method when dealing with such data in a large volume. Graph NeuralNetworks, however, have only recently become mature enough to be used in real large-scale graph prediction tasks, and require proper structure and data modeling to be viable and successful. In this research, we provide a new modeling of the social network which captures the attributes of the nodes from various dimensions. We also introduce the Neural Network architecture that is required for optimally utilizing the new data structure. Finally, in order to provide a hot-start for our model, we initialize the weights of the neural network using a pre-trained graph embedding method. We have also developed a new graph embedding algorithm. We will first explain how previous graph embedding methods are not optimal for all types of graphs, and then provide a solution on how to combat those limitations and come up with a new graph embedding method.
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Buchteile zum Thema "Graph-Based Recommendation Systems"

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Padmaja, B., G. Sucharitha und E. Krishna Rao Patro. „KGRecSys: Knowledge graph-based recommendation systems“. In Artificial Intelligence Technologies for Engineering Applications, 261–82. Boca Raton: CRC Press, 2024. https://doi.org/10.1201/9781003565529-18.

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Ye, Wenwen, Zheng Qin, Zhuoye Ding und Dawei Yin. „Game Recommendation Based on Dynamic Graph Convolutional Network“. In Database Systems for Advanced Applications, 335–51. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59410-7_24.

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Xie, Min, Hongzhi Yin, Fanjiang Xu, Hao Wang und Xiaofang Zhou. „Graph-Based Metric Embedding for Next POI Recommendation“. In Web Information Systems Engineering – WISE 2016, 207–22. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-48743-4_17.

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Kurt, Zühal, Ömer Nezih Gerek, Alper Bilge und Kemal Özkan. „A Multi Source Graph-Based Hybrid Recommendation Algorithm“. In Trends in Data Engineering Methods for Intelligent Systems, 280–91. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-79357-9_28.

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Feng, Siling, Xunyang Ji und Mengxing Huang. „Design of Trademark Recommendation System Based on Knowledge Graph“. In Web Information Systems and Applications, 143–52. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-20309-1_13.

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Wang, Dongjing, Shuiguang Deng und Guandong Xu. „GEMRec: A Graph-Based Emotion-Aware Music Recommendation Approach“. In Web Information Systems Engineering – WISE 2016, 92–106. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-48740-3_7.

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Wang, Huiying, Yue Kou, Derong Shen und Tiezheng Nie. „An Explainable Recommendation Method Based on Multi-timeslice Graph Embedding“. In Web Information Systems and Applications, 84–95. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60029-7_8.

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Ren, Yang, Xiaoming Wang, Guangyao Pang, Yaguang Lin und Pengfei Wan. „Dual Attention Network Based on Knowledge Graph for News Recommendation“. In Wireless Algorithms, Systems, and Applications, 364–75. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85928-2_29.

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Wang, YuBin, SiYao Gao, WeiPeng Li, TingXu Jiang und SiYing Yu. „Research and Application of Personalized Recommendation Based on Knowledge Graph“. In Web Information Systems and Applications, 383–90. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87571-8_33.

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Munna, Tahsir Ahmed, und Radhakrishnan Delhibabu. „Cross-Domain Co-Author Recommendation Based on Knowledge Graph Clustering“. In Intelligent Information and Database Systems, 782–95. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73280-6_62.

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Konferenzberichte zum Thema "Graph-Based Recommendation Systems"

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Özlü, Özgür Anıl, Günce Keziban Orman und Sultan N. Turhan. „Exploring Graph-Based Techniques in Job Recommendation Systems“. In 2024 IEEE 12th International Conference on Intelligent Systems (IS), 1–6. IEEE, 2024. http://dx.doi.org/10.1109/is61756.2024.10705169.

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Shao, Bo, Zhichun Jia, Hongda Wang, Yiwen Wang, Xiyu Zhang und Xing Xing. „Sequential POI Recommendation Based on Graph Neural Networks“. In 2024 IEEE 13th Data Driven Control and Learning Systems Conference (DDCLS), 321–26. IEEE, 2024. http://dx.doi.org/10.1109/ddcls61622.2024.10606607.

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Lei, Fei, Qian Cao, Xiaofeng Wang und Dun Ao. „SentiGCN: Graph Contrastive Learning for Recommendation Based on Sentiment Analysis“. In 2024 12th International Conference on Information Systems and Computing Technology (ISCTech), 1–6. IEEE, 2024. https://doi.org/10.1109/isctech63666.2024.10845409.

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Sun, Tianhao, Xiaodong Zhang, Yanke Chen, Huhai Zou und Quanwang Wu. „A Multi-Level Contrastive Learning Framework for Knowledge Graph-Based Recommendation Systems“. In 2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 456–62. IEEE, 2024. https://doi.org/10.1109/smc54092.2024.10831511.

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Wang, Tianci, Yantong Lai, Yiyuan Wang und Ji Xang. „Adaptive Graph-Based Uncertain Trajectory Data Augmentation Network for Next POI Recommendation“. In 2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 4724–29. IEEE, 2024. https://doi.org/10.1109/smc54092.2024.10831939.

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Li, Sumin, Jiaming Hou, Xiuqin Pan und Yijin Gang. „A Study of Recommendation Algorithm Based on Graph Transformer and Contrastive Learning“. In 2024 12th International Conference on Information Systems and Computing Technology (ISCTech), 1–6. IEEE, 2024. https://doi.org/10.1109/isctech63666.2024.10845540.

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Li, Yunhan, Chunyan An, Conghao Yang und Mingyuan Wang. „Enhancing Session-Based Recommendation via Inter-Session Similar Intent Modeling and Graph Neural Networks“. In 2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 944–51. IEEE, 2024. https://doi.org/10.1109/smc54092.2024.10831110.

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Li, Huan, Senpeng Chen, Wenhong Wei, Ani Dong und Qingxia Li. „Self-Attention Residual Connection and Graph Neural Hawkes Bilayer Model for Session-Based Recommendation“. In 2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2870–75. IEEE, 2024. https://doi.org/10.1109/smc54092.2024.10832107.

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Eisert, Kilian, Maximilian Krähschütz und Viet The Nguyen. „A Graph Neural Network-Based Recommendation System for Product Manufacturing Information in 3D-CAD Models“. In 2024 IEEE 12th International Conference on Intelligent Systems (IS), 1–6. IEEE, 2024. http://dx.doi.org/10.1109/is61756.2024.10705200.

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Long, Hua, Jiaqiang Lu und BingWen Huang. „Integrating Graph Neural Networks with Multi-Head Attention for Multi-Task Learning in Session-Based Recommendation“. In 2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 3013–18. IEEE, 2024. https://doi.org/10.1109/smc54092.2024.10831897.

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