基于注意力机制的群组推荐系统实现.zip

# Attentive Group Recommendation 基于注意力机制的群组推荐系统实现 使用注意力机制动态的从数据中去学习聚合策略 ## Environment Settings We use the framework pytorch. - pytorch version: '0.3.0' - python version: '3.5' ## Example to run the codes. Run AGREE: ``` python main.py ``` After training process, the value of HR and NDCG in the test dataset will be printed in command window after each optimization iteration. Output: ``` AGREE at embedding size 32, run Iteration:30, NDCG and HR at 5 ... User Iteration 10 [449.8 s]: HR = 0.6216, NDCG = 0.4133, [1.0 s] Group Iteration 10 [471.9 s]: HR = 0.5910, NDCG = 0.4005, [23.0 s] ``` ## Parameter Tuning we put all the papameters in the config.py ## Dataset We provide one processed dataset: CAMRa2011. Because we have another paper use the MaFengWo dataset are under reviewing, so we can't release MaFengWo dataset now. group(user) train.rating: * Train file. * Each Line is a training instance: groupID(userID)\t itemID\t rating\t timestamp (if have) test.rating: * group(user) Test file (positive instances). * Each Line is a testing instance: groupID(userID)\t itemID\t rating\t timestamp (if have) test.negative * group(user) Test file (negative instances). * Each line corresponds to the line of test.rating, containing 100 negative samples. * Each line is in the format: (groupID(userID),itemID)\t negativeItemID1\t negativeItemID2 ...