1、Table of ContentsHow do NLP-based Recommendations Work?Choosing One of 5 NLP-Based Recommendation Approaches1.Text Similarity2.Named Entity Recognition3.Topic Extraction4.Keyword Extraction5.Text SummarizationWhere to Apply Recommendations Using NLP1Recommendation systems built with machine learning

2、 can solve one of the mosttedious tasks of gathering customer data,which is to study their preferencesand suggest relevant information in the future.Besides searches,recommendations,or whats also called discovery,provides customers with anendless stream of information that is relevant to their searc

3、h history,preferences,and which generally helps them to find what they want muchfaster.Machine learning recommendations are based on keywords,user activity,andother similar measures that help us define what a person may like.But theybecome ineffective if the user preference involves thousands of fil

4、ters andsubjective criteria that are specific to the user.So here,we discuss NaturalLanguage Processing recommendation systems.Based on our previousexperience,well outline the general idea and limitations of this model,andexplain some of the best approaches for how to build a NLP-basedrecommender sy

5、stem.How do NLP-based Recommendations Work?Natural Language Processing,or NLP,is good at handling plain text andcolloquial speech.You can find tons of sentiment analysis or documentprocessing cases that rely on NLP to solve the task of working with writtenlanguage.These capabilities can be applied t

6、o recommendations as well,if weunderstand our inputs and outputs right.Any recommendation system performs a basic function for a user.It matchesuser expectations with the discovered content,no matter if it was an intendedrequest or not.Recommendations are formed by learning the previous activityof t