hexiangnan / Neural-Attentive-Item-Similarity-Model

TensorFlow Implementation of Neural Attentive Item Similarity Model for Recommendation

NAIS: Neural Attentive Item Similarity Model

This is our official implementation for the paper:

NAIS: Neural Attentive Item Similarity Model for Recommendation Xiangnan He, Zhankui He, Jingkuan Song, Zhenguang Liu, Yu-Gang Jiang, & Tat-Seng Chua IEEE Transactions on Knowledge and Data Engineering (under reviewing)

Two collaborative filtering models: NAIS_concat and NAIS_prod. To target the models for implicit feedback and ranking task, we optimize them using log loss with negative sampling.

Also, we implement the baseline: FISM, which is the well-known item-based recommendation model.

Please cite our paper if you use our codes. Thanks!

Corresponding Author: Dr. Xiangnan He (http://www.comp.nus.edu.sg/~xiangnan/)

Quick to Start

Run NAIS_prod:

python NAIS.py --dataset pinterest-20 --pretrain 0 --weight_size 16 --embed_size 16 --data_alpha 0 --regs [0,0,1e-6] --alpha 0--beta 0.5 --lr 0.05 --algorithm 0

Run NAIS_concat:

python NAIS.py --dataset pinterest-20 --pretrain 0 --weight_size 16 --embed_size 16 --data_alpha 0 --regs [0,0,1e-6] --alpha 0--beta 0.5 --lr 0.05 --algorithm 1

Run FISM:

python FISM.py --dataset pinterest-20 --pretrain 0 --embed_size 16 --alpha 0 --lr 0.01

For more argument details, you can use python FISM.py -h and python NAIS.py -h to obtain them.

Environment

Python 2.7

TensorFlow >= r1.0

Numpy >= 1.12

Dataset

We provide two processed datasets: MovieLens 1 Million (ml-1m) and Pinterest (pinterest-20) in Data/

train.rating:

• Train file.
• Each Line is a training instance: userID\t itemID\t rating\t timestamp (if have)

test.rating:

• Test file (positive instances).
• Each Line is a testing instance: userID\t itemID\t rating\t timestamp (if have)

test.negative

• Test file (negative instances).
• Each line corresponds to the line of test.rating, containing 99 negative samples.
• Each line is in the format: (userID,itemID)\t negativeItemID1\t negativeItemID2 ...

Update: February 5, 2018