Overview

This is an implementation of the Category-Aware Multi-Interest model (CAMI) for personalized product search. Please cite the following paper if you plan to use it for your project：

• Jiongnan Liu, Zhicheng Dou, Qiannan Zhu, Ji-rong Wen. A Category-aware Multi-interest Model for Personalized Product Search. WWW 2022

Requirements:

1. To run the cami model in ./ProductSearch/ and the python scripts in ./utils/, python 3.0+ and Tensorflow v1.3+ are needed. (In the paper, we used python 3.6 and Tensorflow v1.4.0)
2. To run the jar package in ./utils/AmazonDataset/jar/, JDK 1.7 is needed
3. To compile the java code in ./utils/AmazonDataset/java/, galago from lemur project (https://sourceforge.net/p/lemur/wiki/Galago%20Installation/) is needed. 

Data Preparation

Download the code and follow the ''Data Preparation'' section Download the code and follow the ''Data Preparation'' section in this link

Example Parameter Settings

We provide running bashes for all the four datasets we used in our paper.

Reproduce Results

We provide the data partition used in our paper and the trainied model parameters for CAMI-h in .