kevinng77 / game_recommen_frontend

实现了一些推荐系统玄学功能，如根据用户当前浏览点击记录推荐相似商品，算法粗暴。Basic but popular recommender system functionality design. such as user click feedback.

Game recommender

WARNING - This is a toy project for course. The tech or tools used here might out of date!! But it is fun to use simple KNN and SVD to build a dynamic recommender system.

A simple Steam Game recommender website. The recommendation result is dynamic, based on the game user puchased and what user click on the web page.

.
├── app.py
├── config
│   └── config.py
├── data
├── model  //recommender models
├── natapp.sh  // nat tool. You might select other nat tools
├── readme.md
├── static
│   ├── index.css
│   └── list.png
├── templates
│   └── index.html
├── user_init_rate // initial recommend list gerenate from user purchase history
├── user_item_rate // dynamic recommend list generated from user click
└── utils
    └── web_utils.py

To run

python app.py --temperature 0.5 --num_lines_load 4 --num_lines_init 8
# or flask run

--temperature how much your recommender system react to the user click behavior.

--num_lines_load how much lines of games to load when you click loadmore

--num_lines_init how much lines of games to load at initial.

How like or dislike work?

If you click like for one item, the rating for other similar items will be increased, such that they are more likely to be recommended. Click detail for one item will result in slightly increase on similar item score.

Screen shot

The design style is similar to steam official, click details to view details in steam official website.

user id update bar

Reference

CSDN - 右侧边栏right bar

Codepen - jQuery. Fly to cart effect.

game_recommen_frontend's Issues

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