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+# NOTE
+In the PAPER, we pair the ground-truth item 
+with 99 randomly sampled negative items that the user
+has not interacted with, and report the results of 
+HR@{1, 5, 10}, NDCG@{5, 10} and MRR.
+
+In the repo, we rank the ground-truth item with ALL the items.
+We omit the FM and AutoInt because they need 
+enumerate all user-item pairs, which take a very long time. 
+The results are shown in the following pic(all_rank.PNG).
+
+Performance comparison of different methods on six datasets. The best performance and the second best performance
+methods are denoted in bold and underlined fonts respectively.
+
+![avatar](all_rank.PNG)
+
+### requirements
+```shell script
+pip install -r requirements.txt
+```
+
+### data preprocess
+```shell script
+./data/data_process.py
+
+data-name.txt
+one user per line
+user_1 item_1 item_2 ...
+user_2 item_1 item_2 ...
+
+data-name_item2attributes.json
+{item_1:[attr, ...], item_2:[attr, ...], ... }
+```
+
+### pretrain
+```shell script
+python run_pretrain.py \
+--data_name Beauty
+```
+
+### finetune
+```shell script
+python run_finetune.py \
+--data_name Beauty \
+--ckp 100
+```
diff --git a/all_rank.PNG b/all_rank.PNG
diff --git a/data/Beauty.txt b/data/Beauty.txt
diff --git a/data/Beauty_item2attributes.json b/data/Beauty_item2attributes.json
diff --git a/data/LastFM.txt b/data/LastFM.txt
diff --git a/data/LastFM_item2attributes.json b/data/LastFM_item2attributes.json
diff --git a/data/Sports_and_Outdoors.txt b/data/Sports_and_Outdoors.txt
diff --git a/data/Sports_and_Outdoors_item2attributes.json b/data/Sports_and_Outdoors_item2attributes.json
diff --git a/data/Toys_and_Games.txt b/data/Toys_and_Games.txt
diff --git a/data/Toys_and_Games_item2attributes.json b/data/Toys_and_Games_item2attributes.json
diff --git a/data/Yelp.txt b/data/Yelp.txt
diff --git a/data/Yelp_item2attributes.json b/data/Yelp_item2attributes.json