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Top-N Personalized Recommendation with Graph Neural Networks in MOOCs - Lingnan Scholars
scholars.ln.edu.hk/en/publications/top-n-personalized-recommendation-with... Private View
Second, most of these models typically obtain a user’s general preference and neglect the recency of items. This paper proposes a Top-N personalized Recommendation with Graph Neural Network (TP-GNN) in the Massive Open Online Course (MOOCs) as a solution to tackle this problem. We explore two different aggregate functions to deal with the ...
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Second, most of these models typically obtain a user’s general preference and neglect the recency of items. This paper proposes a Top-N personalized Recommendation with Graph Neural Network (TP-GNN) in the Massive Open Online Course (MOOCs) as a solution to tackle this problem. We explore two different aggregate functions to deal with the ...
Top-N personalized recommendation with graph neural networks in MOOCs - Hong Kong Metropolitan University
scholars.hkmu.edu.hk/en/publications/top-n-personalized-recommendation-with... Private View
This paper proposes a Top-N personalized Recommendation with Graph Neural Network (TP-GNN) in the Massive Open Online Course (MOOCs) as a solution to tackle this problem. We explore two different aggregate functions to deal with the user's sequence neighbors and then use an attention mechanism to generate the final item representations.
This paper proposes a Top-N personalized Recommendation with Graph Neural Network (TP-GNN) in the Massive Open Online Course (MOOCs) as a solution to tackle this problem. We explore two different aggregate functions to deal with the user's sequence neighbors and then use an attention mechanism to generate the final item representations.
Top-N personalized recommendation with graph neural networks in MOOCs
www.sciencedirect.com/science/article/pii/S2666920X21000047 Private View
Top-N personalized recommendation has been extensively studied in assisting learners in finding interesting courses in MOOCs. Although existing Top-N personalized recommendation methods have achieved comparable performance, these models have two major shortcomings. First, these models seldom learn an explicit representation of the structural ...
Top-N personalized recommendation has been extensively studied in assisting learners in finding interesting courses in MOOCs. Although existing Top-N personalized recommendation methods have achieved comparable performance, these models have two major shortcomings. First, these models seldom learn an explicit representation of the structural ...
Top-N personalized recommendation with graph neural networks in MOOCs - Semantic Scholar
www.semanticscholar.org/paper/Top-N-personalized-recommendation-with-graph... Private View
Computer Science. IEEE Transactions on Learning Technologies. 2024. TLDR. A novel DRL-based personalized ICR scheme enhanced with the heterogeneous graph, HGCR, which smoothly combines the graph neural network with advanced deep Q-learning neural network, which outperforms the state-of-the-art DRL-based methods.
Computer Science. IEEE Transactions on Learning Technologies. 2024. TLDR. A novel DRL-based personalized ICR scheme enhanced with the heterogeneous graph, HGCR, which smoothly combines the graph neural network with advanced deep Q-learning neural network, which outperforms the state-of-the-art DRL-based methods.
GRAM-SMOT: Top-N Personalized Bundle Recommendation via Graph Attention Mechanism and Submodular Optimization
dl.acm.org/doi/10.1007/978-3-030-67664-3_18 Private View
Bundle recommendation – recommending a group of products in place of individual products to customers is gaining attention day by day. It presents two interesting challenges – (1) how to personalize and recommend existing bundles to users, and (2) how to generate personalized novel bundles targeting specific users.
Bundle recommendation – recommending a group of products in place of individual products to customers is gaining attention day by day. It presents two interesting challenges – (1) how to personalize and recommend existing bundles to users, and (2) how to generate personalized novel bundles targeting specific users.
GRAM-SMOT: Top-N Personalized Bundle Recommendation via Graph Attention Mechanism and Submodular Optimization - Springer
link.springer.com/chapter/10.1007/978-3-030-67664-3_18 Private View
Similarly, we denote by \(\varOmega _{UI}\) for interactions between users and items, and \(\varOmega _{BI}\) for interactions between bundles and items. Definition 1 Top-N existing bundle recommendation. Given \(\mathcal {G}(\mathcal {V},\mathcal {E})\), the objective of the top-N existing bundle recommendation is to enumerate a list of N existing bundles that a user is most likely to ...
Similarly, we denote by \(\varOmega _{UI}\) for interactions between users and items, and \(\varOmega _{BI}\) for interactions between bundles and items. Definition 1 Top-N existing bundle recommendation. Given \(\mathcal {G}(\mathcal {V},\mathcal {E})\), the objective of the top-N existing bundle recommendation is to enumerate a list of N existing bundles that a user is most likely to ...
Neural graph personalized ranking for Top-N Recommendation | Knowledge-Based Systems - ACM Digital Library
dl.acm.org/doi/10.1016/j.knosys.2020.106426 Private View
To fill this gap, we propose neural graph personalized ranking (NGPR) which directly makes use of the user–item interaction information in embedding learning by incorporating the user–item interaction graph in embedding learning. Specifically, we construct the user–item interaction graph using de facto interaction between a user and an item.
To fill this gap, we propose neural graph personalized ranking (NGPR) which directly makes use of the user–item interaction information in embedding learning by incorporating the user–item interaction graph in embedding learning. Specifically, we construct the user–item interaction graph using de facto interaction between a user and an item.
Top-N Personalized Recommendation with Graph Neural Networks in MOOCs - ResearchGate
www.researchgate.net/publication/348720793_Top-N_Personalized_Recommendation... Private View
Journal Pre-proof Top-N Personalized Recommendation with Graph Neural Networks in MOOCs Jingjing Wang, Haoran Xie, Fu Lee Wang, Lap-Kei Lee, Oliver Tat Sheung Au PII: S2666-920X(21)00004-7 DOI ...
Journal Pre-proof Top-N Personalized Recommendation with Graph Neural Networks in MOOCs Jingjing Wang, Haoran Xie, Fu Lee Wang, Lap-Kei Lee, Oliver Tat Sheung Au PII: S2666-920X(21)00004-7 DOI ...
Top-N personalized recommendation with graph neural networks in MOOCs - ScienceGate
www.sciencegate.app/document/10.1016/j.caeai.2021.100010 Private View
Graph Neural Networks . The Relationship. Personalized recommendation has become more and more important for users to quickly find relevant items. The key issue of the recommender system is how to model user preferences. Previous work mostly employed user historical data to learn users’ preferences, but faced with the data sparsity problem.
Graph Neural Networks . The Relationship. Personalized recommendation has become more and more important for users to quickly find relevant items. The key issue of the recommender system is how to model user preferences. Previous work mostly employed user historical data to learn users’ preferences, but faced with the data sparsity problem.
Top-N personalized recommendation with graph neural networks in MOOCs
xueshu.baidu.com/usercenter/paper/show?paperid=1g110xr0jj4004j0rc460ad0sk... Private View
Top-N personalized recommendation has been extensively studied in assisting learners in finding interesting courses in MOOCs. Although existing Top-N personalized recommendation methods have achieved comparable performance, these models have two major shortcomings. First, these models seldom learn an explicit representation of the structural ...
Top-N personalized recommendation has been extensively studied in assisting learners in finding interesting courses in MOOCs. Although existing Top-N personalized recommendation methods have achieved comparable performance, these models have two major shortcomings. First, these models seldom learn an explicit representation of the structural ...
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GRAM-SMOT: Top-N Personalized Bundle Recommendation via Graph Attention Mechanism and Submodular Optimization - Springer
link.springer.com/content/pdf/10.1007/978-3-030-67664-3_18.pdf Private View
objective of the top-N existing bundle recommendation is to enumerate a list of N existing bundles that a user is most likely to interact with in the future. Definition 2. Bundle generation. Given G(V,E), the objective of bundle gen-eration is to generate personalized
objective of the top-N existing bundle recommendation is to enumerate a list of N existing bundles that a user is most likely to interact with in the future. Definition 2. Bundle generation. Given G(V,E), the objective of bundle gen-eration is to generate personalized
[PDF] GRAM-SMOT: Top-N Personalized Bundle Recommendation via Graph Attention Mechanism and Submodular Optimization - Semantic Scholar
www.semanticscholar.org/paper/GRAM-SMOT:-Top-N-Personalized-Bundle... Private View
This work proposes GRAM-SMOT – a graph attention-based framework to address the bundle recommendation problem and proposes a loss function based on the metric-learning approach to learn the embeddings of entities efficiently. . Bundle recommendation – recommending a group of products in place of individual products to customers is gaining attention day by day. It presents two interesting ...
This work proposes GRAM-SMOT – a graph attention-based framework to address the bundle recommendation problem and proposes a loss function based on the metric-learning approach to learn the embeddings of entities efficiently. . Bundle recommendation – recommending a group of products in place of individual products to customers is gaining attention day by day. It presents two interesting ...
Fast Top-N Personalized Recommendation on Item Graph
ieeexplore.ieee.org/document/9006387 Private View
In the era of big data, traditional supply chain systems can not match the requirement of e-commerce. The analysis of customers’ demands and behaviors are necessary to exploit the potential insights and to build intelligent supply chain systems, which can be achieved by recommender systems. Graph-based recommendation models work well for top-N recommender systems due to their capability to ...
In the era of big data, traditional supply chain systems can not match the requirement of e-commerce. The analysis of customers’ demands and behaviors are necessary to exploit the potential insights and to build intelligent supply chain systems, which can be achieved by recommender systems. Graph-based recommendation models work well for top-N recommender systems due to their capability to ...
Personalization in personalized marketing: Trends and ways forward - Chandra - 2022 - Psychology & Marketing - Wiley Online Library
onlinelibrary.wiley.com/doi/full/10.1002/mar.21670 Private View
Furthering the personalization classification, Kwon and Kim classified personalization based on the level of personalization as one-to-all/market-level, one-to-n/segment-level, and one-to-one/individual-level personalization.
Furthering the personalization classification, Kwon and Kim classified personalization based on the level of personalization as one-to-all/market-level, one-to-n/segment-level, and one-to-one/individual-level personalization.
Personalized Necklace & Jewelry - MYKA
www.myka.com Private View
Celestial Sun & Moon Personalized Necklace in Sterling Silver. $115. Gia Drop Initial Bangle Bracelet with Birthstones in 18K Gold Plating. $110. Jack Lava and Personalized Bead Bracelet for Men in Sterling Silver. $140. 12 Month Calendar Heart Necklace with Birthstones in Sterling Silver. $110.
Celestial Sun & Moon Personalized Necklace in Sterling Silver. $115. Gia Drop Initial Bangle Bracelet with Birthstones in 18K Gold Plating. $110. Jack Lava and Personalized Bead Bracelet for Men in Sterling Silver. $140. 12 Month Calendar Heart Necklace with Birthstones in Sterling Silver. $110.
A Survey on Personalized PageRank Computation Algorithms
ieeexplore.ieee.org/document/8895775 Private View
Personalized PageRank (PPR) is an important variation of PageRank, which is a widely applied popularity measure for Web search. Unlike the original PageRank, PPR is a node proximity measure that represents the degree of closeness among multiple nodes within a graph. It is also widely applied to diverse domains, such as information retrieval, recommendations, and knowledge discovery, due to its ...
Personalized PageRank (PPR) is an important variation of PageRank, which is a widely applied popularity measure for Web search. Unlike the original PageRank, PPR is a node proximity measure that represents the degree of closeness among multiple nodes within a graph. It is also widely applied to diverse domains, such as information retrieval, recommendations, and knowledge discovery, due to its ...
Custom Cursor
custom-cursor.com Private View
Softpedia guarantees that Custom Cursor is 100% Clean. This software product was tested thoroughly and was found absolutely clean; therefore, it can be installed with no concern by any computer user. 2017 - 2024, Blife Team Content provided on this website is ...
Softpedia guarantees that Custom Cursor is 100% Clean. This software product was tested thoroughly and was found absolutely clean; therefore, it can be installed with no concern by any computer user. 2017 - 2024, Blife Team Content provided on this website is ...
(PDF) Top-N Personalized Recommendation with Graph Neural Networks in MOOCs - ResearchGate
www.researchgate.net/publication/348720793_Top-N_Personalized_Recommendation... Private View
Top-N Personalized Recommendation with Graph Neural Networks in MOOCs Jingjing Wang, Haoran Xie, Fu Lee Wang, Lap-Kei Lee, Oliver Tat Sheung Au PII: S2666-920X(21)00004-7
Top-N Personalized Recommendation with Graph Neural Networks in MOOCs Jingjing Wang, Haoran Xie, Fu Lee Wang, Lap-Kei Lee, Oliver Tat Sheung Au PII: S2666-920X(21)00004-7
Personalized medicine: Time for one-person trials | Nature
www.nature.com/articles/520609a Private View
In N-of-1 trials, all sorts of relevant data will need to be collected for one person, as frequently as possible — perhaps every day or periodically over months or years.
In N-of-1 trials, all sorts of relevant data will need to be collected for one person, as frequently as possible — perhaps every day or periodically over months or years.
[2304.03411] InstantBooth: Personalized Text-to-Image Generation without Test-Time Finetuning - arXiv.org
arxiv.org/abs/2304.03411 Private View
We propose InstantBooth, a novel approach built upon pre-trained text-to-image models that enables instant text-guided image personalization without any test-time finetuning. We achieve this with several major components. First, we learn the general concept of the input images by converting them to a textual token with a learnable image encoder.
We propose InstantBooth, a novel approach built upon pre-trained text-to-image models that enables instant text-guided image personalization without any test-time finetuning. We achieve this with several major components. First, we learn the general concept of the input images by converting them to a textual token with a learnable image encoder.