DRGR

DRGR: Deep Reinforcement learning based Group Recommender system.

A course project for Georgia Tech CSE 6240 Web Search and Text Mining Spring 2021.

The project report is available on arXiv.

Environment Settings

• python version: '3.7.10'
• pytorch version: '1.8.1'

Dataset

The original MovieLens dataset can be downloaded from MovieLens, and the one we use is MovieLens 1M Dataset. You can generate the group data MovieLens-Rand by using data/MovieLens-1M.zip and drgr/generator.py. (See "Run the DRGR".)

The data/MovieLens-Rand directory should include the following files:

movies.dat:

• Movie information file from MovieLens-1M.

users.dat:

• User information file from MovieLens-1M.

groupMember.dat:

• File including group members.
• Each line is a group instance: groupID userID1,userID2,...

group(user)RatingTrain.dat:

• Train file.
• Each line is a training instance: groupID(userID) itemID rating timestamp

group(user)RatingVal(Test).dat:

• group (user) validation (test) file (positive instances).
• Each line is a validation (test) instance: groupID(userID) itemID rating timestamp

group(user)RatingVal(Test)Negative.dat:

• group (user) validation (test) file (negative instances).
• Each line corresponds to the line of group(user)RatingVal(Test).dat, containing 100 negative samples.
• Each line is in the format: (groupID(userID),itemID) negativeItemID1 negativeItemID2 ...

Run the DRGR

Change to the code directory (if not in):

cd drgr

Generate the group data from the MovieLens-1M dataset (data/MovieLens-1M.zip):

python generator.py

Run the DRGR:

python main.py

Parameters

We put all parameters in config.py.

References

During the code implementation, we reference the following repositories and articles:

• AGREE: https://github.com/LianHaiMiao/Attentive-Group-Recommendation
• LIRD: https://github.com/egipcy/LIRD
• Recsys-RL: https://github.com/shashist/recsys-rl
• GroupIM: https://github.com/CrowdDynamicsLab/GroupIM
• Deep Deterministic Policy Gradients Explained: https://towardsdatascience.com/deep-deterministic-policy-gradients-explained-2d94655a9b7b