Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba Graph Embedding 能够保留一些 CF 忽略了的复杂的高阶相似网络关系,但是它有一个致命的弱点,就是 “冷启动” 问题。如果单纯使用用户行为生成的物品相关图,固然可以生成物品的 Embedding,但是如果遇到新加入的物品,或者没有过多互动信息的长尾...
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However, there are still challenges in how to integrate different information flexibly and effectively. Therefore, we propose a multi-task embedding based personalized POI recommendation method (MTEPR). On one hand, multi-information (sequential, social, temporal, geographical, semantic, gender, and ...
that question is also converted to embeddings. The RAG system then searches the database for an ...
In addition, to validate the effectiveness of our model across the entire lifecycle of geographic entities, we introduced a link prediction task based on unknown time and conducted extensive experiments on two real-world datasets, demonstrating the superiority of our model. The main contributions of ...
This simplifies the design of GCNs, making them more concise and suitable for recommendation tasks. Bojchevski et al.27 presented PPRGo, which uses an effective approximation of information diffusion in GNNs, thereby significantly speeding up computation while improving predictive performance. xGCN28 ...
fetch('/task/gpt', { method: 'POST', headers: { 'Content-Type': 'application/json', }, body: JSON.stringify({prompt: userInput}) }) .then(response => response.json()) .then(data => { // Create a container for the user's question and the AI's answer const qaContainer = docu...
An embedding is any numerical representation of data that captures its relevant qualities in a way that ML algorithms can process. The data is embedded in n-dimensional space. In theory, data doesn’t need to be embedded as a vector. For example, some types of data can be embedded in tup...
HeMGNN is a general heterogeneous network embedding model that can handle the embedding tasks for any type of heterogeneous network. HeMGNN can automatically generate and filter out the metapaths closely related to the domain tasks, so as to avoid the limitation of the manual selection of metapat...
The pruning-based methods first set a maximum embedding dimension and then dynamically prune the “redundant” elements in the embedding vectors during the training process for the specific recommendation task so as to achieve automatic embedding dimension reduction. Considering the pros and cons of ...