init

.gitattributes

@@ -53,3 +53,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.jpg filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text
+*.jsonl filter=lfs diff=lfs merge=lfs -text
+*.tsv filter=lfs diff=lfs merge=lfs -text

IMDB-database/clean.py

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+""" IMDB database (https://www.imdb.com/interfaces/) is not clean, which can be improved by some heuristic rules
+rule 1. titleType == 'movie' (from ['short', 'movie', 'tvShort', 'tvMovie', 'tvSeries', 'tvEpisode', 'tvMiniSeries', 'tvSpecial', 'video', 'videoGame', 'tvPilot'])
+rule 2. runtimeMinutes == '60' - '360' (1h - 6h)
+rule 3. sorted by numVotes (descending)
+the cleaned dataset is called “imdb/movie_clean.tsv”
+"""
+
+import pandas as pd
+
+df_basics = pd.read_csv("title.basics.tsv", sep="\t")
+df_ratings = pd.read_csv("title.ratings.tsv", sep="\t")
+
+print("Before merging: ", df_basics.shape, df_ratings.shape)
+
+# merge
+df = pd.merge(df_basics, df_ratings, on="tconst", how="left")
+
+# Fill missing values with -1
+df.fillna(-1, inplace=True)
+
+print("Before cleaning: ", df.shape)
+
+# rule 1
+df = df[df['titleType'] == 'movie']
+
+print("After Rule 1: ", df.shape)
+
+# rule 2
+df = df[df['runtimeMinutes'].apply(lambda x: x.isnumeric() and 60 <= int(x) <= 360)]
+
+print("After Rule 2: ", df.shape)
+
+# rule 3
+df = df.sort_values(by="numVotes", ascending=False)
+
+# save
+df.to_csv("movie_clean.tsv", sep="\t", index=False)

IMDB-database/movie_clean.tsv

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IMDB-database/title.basics.tsv

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IMDB-database/title.ratings.tsv

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README.md

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+---
+task_categories:
+- conversational
+language:
+- en
+tags:
+- recommendation
+viewer: false
+---
+
+# Dataset Card for `Reddit-Movie-raw`
+
+## Dataset Description
+
+- **Homepage:** https://github.com/AaronHeee/LLMs-as-Zero-Shot-Conversational-RecSys
+- **Repository:** https://github.com/AaronHeee/LLMs-as-Zero-Shot-Conversational-RecSys
+- **Paper:** To appear
+- **Point of Contact:** [email protected]
+
+### Dataset Summary
+
+This dataset provides the raw text from [Reddit](https://reddit.com) related to movie recommendation conversations. 
+The dataset is extracted from the data dump of [pushshift.io](https://arxiv.org/abs/2001.08435) and only for research use.
+
+### Disclaimer
+
+⚠️ **Please note that conversations processed from Reddit raw data may include content that is not entirely conducive to a positive experience (e.g., toxic speech). Exercise caution and discretion when utilizing this information.**
+
+### Folder Structure
+
+We explain our data folder as follows:
+
+```bash
+reddit_movie_raw
+├── IMDB-database
+│   ├── clean.py # script to obtain clean IMDB movie titles, which can be used for movie name matching if needed.
+│   ├── movie_clean.tsv # results after movie title cleaning 
+│   ├── title.basics.tsv # original movie title information from IMDB
+│   └── title.ratings.tsv # # original movie title and rating information from IMDB
+├── Reddit-Movie-large
+│   ├── sentences.jsonl # raw sentences from the subreddit/* data, it can be used for following processing
+│   └── subreddit # raw text from different subreddits from Jan. 2012 to Dec. 2022 (large)
+│       ├── bestofnetflix.jsonl
+│       ├── movies.jsonl
+│       ├── moviesuggestions.jsonl
+│       ├── netflixbestof.jsonl
+│       └── truefilm.jsonl
+└── Reddit-Movie-small
+    ├── sentences.jsonl # raw sentences from the subreddit/* data, it can be used for following processing
+    └── subreddit # raw text from different subreddits from Jan. 2022 to Dec. 2022 (small)
+        ├── bestofnetflix.jsonl
+        ├── movies.jsonl
+        ├── moviesuggestions.jsonl
+        ├── netflixbestof.jsonl
+        └── truefilm.jsonl
+```
+
+### Data Processing
+
+We also provide first-version processed Reddit-Movie datasets as [Reddit-Movie-small-V1]() and [Reddit-Movie-large-V1]().
+Join us if you want to improve the processing quality as well!
+
+### Citation Information
+
+Please cite these two papers if you used this raw data, thanks!
+
+```bib
+@inproceedings{baumgartner2020pushshift,
+  title={The pushshift reddit dataset},
+  author={Baumgartner, Jason and Zannettou, Savvas and Keegan, Brian and Squire, Megan and Blackburn, Jeremy},
+  booktitle={Proceedings of the international AAAI conference on web and social media},
+  volume={14},
+  pages={830--839},
+  year={2020}
+}
+```
+
+```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.

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