Commodity recommendation verbal skill generation method and system based on commodity selling points
A technology for product recommendation and selling point, applied in the computer field, it can solve the problem of difficult to achieve the smoothness of sentences, and achieve the effect of high controllability
- Summary
- Abstract
- Description
- Claims
- Application Information
AI Technical Summary
Problems solved by technology
Method used
Image
Examples
Embodiment 1
[0059] A product recommendation speech generation method based on product selling points, such as figure 1 As shown, it mainly includes the following steps:
[0060] The first step is to learn the knowledge base composed of prod and attr_value from the basic information of the product. The knowledge base is some basic knowledge, which is very helpful for effect optimization.
[0061] In the second step, we extract descriptions from recommended articles and chat records. The main function is to filter the language and only keep the language that is the recommended language as a description. At the same time, we use the grammar knowledge of nlp to make syntactic adjustments to form a description library.
[0062] The third step is to form a selling point mining model, which can extract relevant selling points from a sentence description. It is also possible to extract relevant selling points from the basic information of the product, and at the same time, there is a ranking of ...
Embodiment 2
[0066] This embodiment is optimized on the basis of Embodiment 1, knowledge base mining:
[0067] The purpose of the present invention is actually to extract what the product is and what selling points it has from the product information. It will be better if we can have a systematic understanding of this aspect. Therefore, it is necessary to mine the knowledge base in advance. The knowledge base we want has prod and attr_value. The demand for attr_name is relatively weak. It is only needed when some attr_values are repeated. For example, the attr_name of clothing is "fabric material" and "lining material", and attr_value can be "pure cotton". , it is necessary to add attr_name at this time. Such as figure 2 As shown, in addition to digging out the complete set of prod and attr_value, the mining of the knowledge base also hopes to dig out the relationship between them. For example, the synonym and implication relationship between prod and prod, attr_value and attr_value...
Embodiment 3
[0078] This embodiment is optimized on the basis of Embodiment 1 or 2, and describes library mining:
[0079] Here, our goal is to find out which sentences are really recommended words. Therefore, it can be regarded as a binary classification model. Such as image 3 As shown, the process is as follows:
[0080] a. Speech segmentation module, which is used to divide a long sentence into several paragraphs and process the corpus more locally. It can be simply split according to punctuation, or some algorithms (such as dependency analysis) can be applied to make the split more reasonable.
[0081] b. The speech classification module uses the short sentences obtained above as our set to be judged, and finds out the beautiful speeches that have recommended functions. A batch of data needs to be labeled first, and then judged by deep learning. Here, we use the BiLSTM+MLP model, which can better combine contextual information through BiLSTM. At the same time, we use word2vector ...
PUM
Abstract
Description
Claims
Application Information
- R&D Engineer
- R&D Manager
- IP Professional
- Industry Leading Data Capabilities
- Powerful AI technology
- Patent DNA Extraction
Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.
© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com