Knowledge_graph_based_intent_network
WebApr 14, 2024 · Autonomous indoor service robots are affected by multiple factors when they are directly involved in manipulation tasks in daily life, such as scenes, objects, and actions. It is of self-evident importance to properly parse these factors and interpret intentions according to human cognition and semantics. In this study, the design of a semantic … WebAug 24, 2024 · A graph neural network is constructed with multi-hop propagation in the KG and EUIG to learn the representation of entities, relations and user intents. Moreover, we distill information on...
Knowledge_graph_based_intent_network
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WebIntroduction Knowledge Graph-based Intent Network (KGIN) is a recommendation framework, which consists of three components: (1)user Intent modeling, (2)relational … WebApr 14, 2024 · Hence, we combine the intent and the context based on node aggregation representation. Technically, context awareness is an attentive design of relation embeddings, where the important intent is assigned with a larger weight factor. ... Similarly, we adopt a knowledge-based graph convolution neural network (KGCN) to capture the …
WebApr 15, 2024 · Networks themselves are a kind of graph structure, and GNNs can be used to learn the complex network behavior from the data. The advantage of GNN is its ability to … Knowledge Graph-based Intent Network (KGIN) is a recommendation framework, which consists of three components: (1)user Intent modeling, (2)relational path-aware aggregation, (3)indepedence modeling. See more The code has been tested running under Python 3.6.5. The required packages are as follows: 1. pytorch == 1.5.0 2. numpy == 1.15.4 3. scipy == … See more To demonstrate the reproducibility of the best performance reported in our paper and faciliate researchers to track whether the model status is consistent with ours, we provide the best parameter settings (might be different for … See more Any scientific publications that use our datasets should cite the following paper as the reference: Nobody guarantees the correctness of the data, its suitability for any particular purpose, or the validity of results based on the … See more We provide three processed datasets: Amazon-book, Last-FM, and Alibaba-iFashion. 1. You can find the full version of recommendation datasets via Amazon-book, Last-FM, and … See more
WebWe propose a new model, Knowledge Graph-based Intent Network(KGIN), which consists of two components to solve the foregoing limitations correspondingly: (1) User Intent Modeling. Each user-item interaction is enriched with the underlying intents. While we can express these intents as latent vectors, their semantics are opaque to understand. WebKnowledge graph (KG) plays an increasingly important role in recommender systems. A recent technical trend is to develop end-to-end models founded on graph neural networks (GNNs). However, existing GNN-based models are coarse-grained in relational modeling, failing to (1) identify user-item relation at a fine-grained
WebAbstract Topic-based communities have gradually become a considerable medium for netizens to disseminate and acquire knowledge. These communities consist of entities (actual objects, e.g., a real a...
WebOct 1, 2024 · This study explores intents behind a user-item interaction by using auxiliary item knowledge, and proposes a new model, Knowledge Graph-based Intent Network (KGIN), which achieves significant improvements over the state-of-the-art methods like KGAT, KGNN-LS, and CKAN. 120 PDF SamWalker++: Recommendation With Informative … foremost albertaWebKnowledge graph (KG) plays an increasingly important role in recommender systems. A recent technical trend is to develop end-to-end models founded on graph neural networks … foremost alberta newsdid the world end in 2012WebFeb 5, 2024 · The knowledge graph-based intent network (KGIN) method, proposed by Wang X. et al. [ 6 ], uses auxiliary item knowledge to explore the users’ intention behind the user-item interactions, and uses an information aggregation mechanism to refine the information related to the users’ intention, and finally encodes this information in the user … foremost agents near meWebApr 15, 2024 · Networks themselves are a kind of graph structure, and GNNs can be used to learn the complex network behavior from the data. The advantage of GNN is its ability to model non-linear relationships and adapt to different types of data, improving the expressiveness and granularity of network modeling. did the works toilet bowl cleaner changeWebA new method, knowledge graph attention network for recommendation (KGAT), is proposed based on knowledge map and attention mechanism (Wang et al. Citation 2024). The attribute information between the item and the user connects the instances of the user’s item together, and explains that the user and the item are not independent of each other. did the world end in 2012 redditWebA knowledge graph, also known as a semantic network, represents a network of real-world entities—i.e. objects, events, situations, or concepts—and illustrates the relationship … did the world end in 2012 mandela effect