“知识神经元网络”KNN(Knowledge neural network)是一种以“神经元网络”模型 为基础的知识组织方法。 在“知识神经元网络”KNN 中,所谓的“知识”,是描述一个“知识”的文本,如一个网页、Word、PDF 文档等。

VIP内容

简介: “知识神经元网络”KNN(Knowledge neural network)是一种以“神经元网络”模型 为基础的知识组织方法。在现实中,“知识”通常是用文字进行叙述,知识蕴藏在自然语言叙述的内容和逻辑关系中。即便是抽象的数学知识,虽然采用数学符号语言进行定义和推导,但仍然离不开自然语言进行说明,否则很难被人理解。 在“知识神经元网络”KNN 中,所谓的“知识”,是描述一个“知识”的文本,如一个网页、Word、PDF 文档等。可从多维度(或仅一个)来描述“知识”。如,对一个疾病知识的描述,可有:症状、发病原因、检查手段、治疗方法等。 建立 KNN,首先将文本信息(网页、word、pdf 等)进行“知识化”处理,形成半结构化的“知识记录” ;然后,对“知识”进行相关性计算,使相关的“知识”建立连接,将杂乱无章、零星、无序的“知识” ,按相关性进行聚类,形成相互联通的“知识神经元网络”。

“知识神经元网络”KNN 的五个重要组成部分:

(1)“知识神经元”KN;

(2)“知识神经元连接”KNS;

(3)“知识神经元网络”KNN;

(4)“知识神经元网络桥”KNNB;

(5)“知识神经元网络系统”KNNS。

成为VIP会员查看完整内容
知识神经网络.pdf
0
9

最新内容

Alternative recommender systems are critical for ecommerce companies. They guide customers to explore a massive product catalog and assist customers to find the right products among an overwhelming number of options. However, it is a non-trivial task to recommend alternative products that fit customer needs. In this paper, we use both textual product information (e.g. product titles and descriptions) and customer behavior data to recommend alternative products. Our results show that the coverage of alternative products is significantly improved in offline evaluations as well as recall and precision. The final A/B test shows that our algorithm increases the conversion rate by 12 percent in a statistically significant way. In order to better capture the semantic meaning of product information, we build a Siamese Network with Bidirectional LSTM to learn product embeddings. In order to learn a similarity space that better matches the preference of real customers, we use co-compared data from historical customer behavior as labels to train the network. In addition, we use NMSLIB to accelerate the computationally expensive kNN computation for millions of products so that the alternative recommendation is able to scale across the entire catalog of a major ecommerce site.

0
0
下载
预览

最新论文

Alternative recommender systems are critical for ecommerce companies. They guide customers to explore a massive product catalog and assist customers to find the right products among an overwhelming number of options. However, it is a non-trivial task to recommend alternative products that fit customer needs. In this paper, we use both textual product information (e.g. product titles and descriptions) and customer behavior data to recommend alternative products. Our results show that the coverage of alternative products is significantly improved in offline evaluations as well as recall and precision. The final A/B test shows that our algorithm increases the conversion rate by 12 percent in a statistically significant way. In order to better capture the semantic meaning of product information, we build a Siamese Network with Bidirectional LSTM to learn product embeddings. In order to learn a similarity space that better matches the preference of real customers, we use co-compared data from historical customer behavior as labels to train the network. In addition, we use NMSLIB to accelerate the computationally expensive kNN computation for millions of products so that the alternative recommendation is able to scale across the entire catalog of a major ecommerce site.

0
0
下载
预览
Top