# FPMC

**Repository Path**: branchlets/FPMC

## Basic Information

- **Project Name**: FPMC
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-10-11
- **Last Updated**: 2024-10-11

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# FPMC

FPMC[1] implementation for python3 with Numba.

## Dependencies
- Python3
- Numpy
- Numba >= 25.0

## How to run
Just type

    python3 run.py data/

If Numba is not installed, implementation in generic python will be used. Numba version is 10x faster than generic version.

## Notes
This implemtation is the same as original paper except:
- Number of negative sample: default is 10
- Use one basket to predict one item. That is, size of "next basket - i" is 1.

## Data format
Please refer to data/idxseq.txt.


The format is:

    [user index] [item index] ... [item index]
The last one item is regarded as next item (next basket), and is what our FPMC will predict.


## Reference

-  [1] Rendle, S., Freudenthaler, C., & Schmidt-Thieme, L. (2010). Factorizing personalized Markov chains for next-basket recommendation. Proceedings of the 19th International Conference on World Wide Web - WWW ’10, 811. http://doi.org/10.1145/1772690.1772773