Research on personalized product search

In the product search model , The introduction of personalization will not only improve users' search experience , It will also provide higher income for e-commerce companies . In recent years , Researchers have done a lot of work on how to introduce personalization into product search . This paper arranges and analyzes the relevant research of personalized commodity search .

     In recent years , With the vigorous development of the Internet , E-commerce is becoming more and more popular . When users buy goods on shopping websites , The search engine will be based on the query submitted by the user , Search out the sorted list of related products , Help users choose products . However , Queries submitted by users usually consist of only a few keywords , The intention expressed is often ambiguous , Unable to reflect the user's potential search intention , Which leads to unsatisfactory search results . in addition to , Users' preferences for goods may be diverse , If affected by age , Gender , And the impact of the current environment . So for the same query , It is obviously inappropriate to return the same search results to different users . To improve search performance , Search engines should take advantage of users' historical behavior （ Such as comments , Score, etc ） Modeling user preferences , So as to retrieve the goods that meet the user's search intention , That is, personalized product search . This paper summarizes some research progress of personalized commodity search model , We welcome criticism and exchange .

 

Including the following papers ：

1. Learning a Hierarchical Embedding Model for Personalized Product Search (SIGIR 2017)
2. Attentive Long Short-Term Preference Modeling for Personalized Product Search (TOIS 2019)
3. A Zero Attention Model for Personalized Product Search (CIKM 2019)
4. A Transformer-based Embedding Model for Personalized Product Search (SIGIR 2020)
5. Explainable Product Search with a Dynamic Relation Embedding Model (TOIS 2019)
6. Structural Relationship Representation Learning with Graph Embedding for Personalized Product Search (CIKM 2020)
7. IHGNN: Interactive Hypergraph Neural Network for Personalized Product Search (WWW 2022)
8. Learning a Fine-Grained Review-based Transformer Model for Personalized Product Search (SIGIR 2021)