Update README.md

README.md

@@ -63,19 +63,45 @@ Since the raw text is quite noisy and processing is not perfect, we do observe s
         ```
         0 # -> It is the first turn in this conversation. Typically, for conversations from Reddit, the number of turns is usually not very large.
         ```
+    - `user_id (string)`: The unique user id. For example:
+        ```
+        "t2_fweij" # -> user id
+        ```
     - `is_seeker (bool)`: Whether the speaker at the current turn is the seeker for recommendation or not. For example
         ```
         true # -> It is the seeker (seeker starts a movie requesting conversation on Reddit).
         ```
     - `utc_time (int64)`: The UTC timestamp when this conversation turn happend. For example:
         ```
-        1641234238 # -> Try datetime.fromtimestamp(1641234238)
+        1641234238 # -> Try `datetime.fromtimestamp(1641234238)`
         ```
     - `upvotes (int64)`: The number of upvotes from other reddit users (it is `null` if this post is the first post in this thread, because upvotes only work for replies.). For example:
         ```
         6 # -> 6 upvotes from other Reddit users.
         ```
-
+    - `processed (string)`: The role and text at this conversation turn (processed version). For example:
+        ```
+        "['USER', 'We decided on tt3501632. They love it so far— very funny!']" # -> [ROLE, Processed string] after `eval()`, where we can match `tt3501632` to real item name using `id2name.json`.
+        ```
+    - `raw (int64)`: The role and text at conversation turn (raw-text version). For example:
+        ```
+        "['USER', 'We decided on Thor: Ragnarok. They love it so far— very funny!']" # -> [ROLE, Raw string] after `eval()`, where it is convinient to form it as "USER: We decided on Thor: Ragnarok. They love it so far— very funny!".
+        ```
+    - `context_processed (string)`: The role and text pairs as the historical conversation context (processed version). For example:
+        ```
+        "[['USER', 'It’s summer break ... Some of the films we have watched (and they enjoyed) in the past are tt3544112, tt1441952, tt1672078, tt0482571, tt0445590, tt0477348...'], ['SYSTEM', "I'm not big on super hero movies, but even I loved the tt2015381 movies ..."]]"
+        # -> [[ROLE, Processed string], [ROLE, Processed string], ...] after `eval()`, where we can match `tt******` to real item name using `id2name.json`.
+        ```
+    - `context_raw (string)`: The role and text pairs as the historical conversation context (raw version). For example:
+        ```
+        "[['USER', 'It’s summer break ... Some of the films we have watched (and they enjoyed) in the past are Sing Street, Salmon Fishing in the Yemen, The Life of Pi, The Prestige, LOTR Trilogy, No Country for Old Men...'], ['SYSTEM', "I'm not big on super hero movies, but even I loved the guardians of the Galaxy movies ..."]]"
+        # -> [[ROLE, Processed string], [ROLE, Processed string], ...] after `eval()`, where we can form "USER: ...\n SYSTEM: ...\n USER:..." easily.
+        ```
+    - `context_turn_ids (string)`: The conversation context turn_ids associated with context [ROLE, Processed string] pairs. For example:
+        ```
+        "['t3_8voapb', 't1_e1p0f5h'] # -> This is the `turn_id`s for the context ['USER', 'It’s summer break ...'], ['SYSTEM', "I'm not big on super hero movie...']. They can used to retrieve more related information like `utc_time` after combining with `conv_id`.
+        ```
+        
 ### Data Splits
 
 We hold the last 20% data (in chronological order according to the created time of the conversation) as testing set. Others can be treated as training samples. We provided a suggested split to split Train into Train and Validation but you are free to try your splits.
@@ -91,7 +117,16 @@ We hold the last 20% data (in chronological order according to the created time
 ### Citation Information
 
 
-Please cite these two papers if you used this raw data, thanks!
+Please cite these two papers if you used this dataset, thanks!
+
+```bib
+@inproceedings{he23large,
+  title = Large language models as zero-shot conversational recommenders",
+  author = "Zhankui He and Zhouhang Xie and Rahul Jha and Harald Steck and Dawen Liang and Yesu Feng and Bodhisattwa Majumder and Nathan Kallus and Julian McAuley",
+  year = "2023",
+  booktitle = "CIKM"
+}
+```
 
 ```bib
 @inproceedings{baumgartner2020pushshift,
@@ -104,14 +139,6 @@ Please cite these two papers if you used this raw data, thanks!
 }
 ```
 
-```bib
-@inproceedings{he23large,
-  title = Large language models as zero-shot conversational recommenders",
-  author = "Zhankui He and Zhouhang Xie and Rahul Jha and Harald Steck and Dawen Liang and Yesu Feng and Bodhisattwa Majumder and Nathan Kallus and Julian McAuley",
-  year = "2023",
-  booktitle = "CIKM"
-}
-```
 
 Please contact [Zhankui He](https://aaronheee.github.io) if you have any questions or suggestions.