A simple version of VBPR.

Two files are needed to run "VBPR.py"

1. "image_feature.csv" The first column of the csv file is item id, while each line is the corresponding feature of the item.

   This file is to build a dictionary of item_id and item_feature : self.imageFeatures.

2. "feedback_file.json" The file stores the feedback data of users & items, which has the structure as follows:

    { "user_id1" : { "item_id_1", "item_id_2", ... },
      "user_id2" : { "item_id_i", "item_id_j", ... },
       ... }
   

   This file is to build the user-item relation map: self.R.

You need to complete the code in line 113-114 with the path of the above files.

baidu drive link: https://pan.baidu.com/s/1FIsLca0TZW_I4wGmfDwpDw cue：5j3g

google drive link: https://drive.google.com/open?id=1yOjxhqI19OL2r8Rsf_SU8XTPBGE4F-Rg

An end to end VBPR, which is no need to load all image features at begining.

Three files are needed to run "VBPR_update.py"

1. "user_idx.json" & "item_idx.json". Structures are as follows,

   { "user_0": 0, "user_1": 1, ..., "user_n": n-1 } { "item_0": 0, "item_1": 1, ..., "item_m": m-1 }

2. Your own rating file. The interaction between users and items are needed.

Step to run "VBPR_update.py"

1. model = VBPR(K=?, K2=?)

2. model.load_training_data() # filled with "user_idx.json" & "item_idx.json"

3. build data fed in placeholders, and feed their to the network.