using a relevance model, the query embedding and each of the image embeddings into a joint p-dimensional embedding space; calculating, for each identified image object, a relevance-score based on a similarity metric between the transformed query embedding and the transformed image embedding; generatin...
论文:A Relational Memory-based Embedding Model for Triple Classification and Search Personalization 首发链接: R-MeN: 个性化搜索的关系记忆网络Embeddingmp.weixin.qq.com/s/amieTwOhrlNp-45G5lTV8w R-MeN: 个性化搜索的关系记忆网络Embedding 导语 本文是ACL20的一篇表示学习work。文末附 Github Repo. 知...
作者还进行了一项subspace training的实验,来比较SWEM和CNN/LSTM的model complexity。上图中direct为直接对整个词向量矩阵进行更新,subspace dim 为前面说到的另一种词向量更新方式,MLP的维度。可以看到direct的方式对于不同的模型的效果一样,原因是对于原词向量矩阵拥有全自由度,即有非常大的搜索空间,始终能找到很好的...
Model Architecture The overall model structure is divided into three parts: the input layer, the representation layer, and the output layer. Firstly, the input layer manages the data and features for the deep learning models. In two-tower models, user-story interaction logs are separated into us...
To this end, we propose a Timespan-aware Graph Attention-based Embedding Model named T-GAE to tackle the TKGC task. To the best of our knowledge, T-GAE is the first KGE model in which Graph-Attention-Networks (GATs) and Long Short-Term Memory (LSTM) Networks are simultaneously applied ...
Tune ANN parameters when there is non-trivial model change. 我们观察到,ANN的性能与模型特征有关。例如,当我们在non-click impressions训练的模型中使用ensemble技术时,我们发现,虽然模型显示出比基线更好的召回,但在对两者进行量化后,召回比基线更差。当模型训练任务发生重大变化时,例如,增加更多的hard 负例时,...
Sohn BS, Jung JE (2015) A novel ranking model for a large-scale scientific publication. Mob Netw Appl 20(4):508–520 Article Google Scholar Sun Y, Han J (2012) Mining heterogeneous information networks: principles and methodologies. Synthesis Lectures on Data Min Knowl Disc 3(2):1–159...
Cascade Model:将模型串联起来,例如:第一阶段是简单样本训练的召回模型,第二阶段是困难样本训练的召回模型。 上面两种做法中,困难样本都是使用挖掘方法得到的,使用曝光未点击的数据作为困难负样本,效果没有提升。 通过以上的种种实验,发现曝光未点击样本作为正样本或者作为负样本都不合适,原因可能是,曝光未点击样本夹杂...
为了让embedding model可以考虑上location的信息,我们在query侧和document侧都加入了location feature。在query侧,我们取出了城市地区国家和语言。document侧,我们加入了一些公开信息如,group location tagged by admin。加入了text feature,模型可以学习到了一些内在的location match。Table2 对比了text embedding 模型和 ...
The embedding-based large-scale query-document retrieval problem is a hot topic in the information retrieval (IR) field. Considering that pre-trained language models like BERT have achieved great success in a wide variety of NLP tasks, we present a QuadrupletBERT model for effective and ...