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1st Solution for 2019-CIKM-Analyticup: Efficient and Novel Item Retrieval for Large-scale Online Shopping Recommendation

License: MIT License

Python 15.42% Shell 0.01% Jupyter Notebook 84.57%
cikm datamining recommender-system kaggle-competition tianchi

cikm-2019-analyticup's Introduction

This repository contains the 1-st solution for the CIKM 2019 EComm AI
- Efficient and Novel Item Retrieval for Large-scale Online Shopping Recommendation Challenge.

Email: [email protected]

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cikm-2019-analyticup's Issues

相似度矩阵计算以疑问

楼主你好,想问一下,在item相似度计算那块:
for index, i1 in enumerate(item_log):
for i2 in item_log[(index+1): ]:
weight = 1/(math.log(1+user_times_map[u[0]]))
mat[i1, i2] += weight
mat[i2, i1] += weight
按照方案中描述:使用了用户活跃度的置信度计算 Item CF,这里的sim(i,j)!=sim(j,i),但是代码中这样看应该是相等的?另外,改进的相似度方法中,公式和代码对不上。是需要进一步推导嘛?

先谢过。

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