A simple version of VBPR.
Two files are needed to run "VBPR.py"
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"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.
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"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.
file link: https://pan.baidu.com/s/1FIsLca0TZW_I4wGmfDwpDw cue:5j3g
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"
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"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 }
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Your own rating file. The interaction between users and items are needed.
Step to run "VBPR_update.py"
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model = VBPR(K=?, K2=?)
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model.load_training_data() # filled with "user_idx.json" & "item_idx.json"
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build data fed in placeholders, and feed their to the network.