shunk031 / EXTRA

SIGIR'21, EXTRA: Explanation Ranking Datasets for Explainable Recommendation

EXTRA (EXplanaTion RAnking) Datasets

Paper

Lei Li, Yongfeng Zhang, Li Chen. EXTRA: Explanation Ranking Datasets for Explainable Recommendation. SIGIR'21 Resource. [Paper]

Datasets to download

• Amazon Movies & TV
• TripAdvisor Hong Kong
• Yelp 2019

Data format

• IDs.pickle can be loaded via the pickle package as a python list, where each record is a python dict in the form of

{
	"user": "7B555091EC0818119062CF726B9EF5FF",  # str
	"item": "1068719",       # str
	"rating": 5,             # int, not important to the ranking task
	"time": "2017-05-06",    # str in the format of YYYY-MM-DD, not available on TripAdvisor
	"exp_idx": ["34", "85"], # a list of str, they are the indices of explanations after sentence grouping via LSH
	"oexp_idx": ["91", "15"] # a list of str, they are the indices of original sentences, corresponding to senID in the following
}  

• Open id2exp.json via a text editor, e.g., Sublime, if you are curious about what the explanation indices correspond to. Or you can load it via testing.py by updating the parameters (line 5-7).

---

• IDs.txt and id2exp.txt are compatible with IDs.pickle and id2exp.json. It would be easier to check the content with plain text files.
• Each line in IDs.txt is in the format of userID::itemID::rating::timestamp::expID:expID::senID:senID, where timestamp is not available on TripAdvisor, and expID/senID are separated by ":" when there are multiple explanations.
• Each line in id2exp.txt is in the format of expID::explanation sentence.
• You can load the two files via movielens_load.py by updating the paths (line 1-2).

---

• Folders named 1, 2, 3, 4 and 5 are data splits.
• Each folder contains train.index and test.index which indicate the indices of their records in the list of IDs.pickle/IDs.txt.
• train.index/test.index contain a line of numbers (indices), e.g., 5 8 9 10.

Creation steps

• Run the scripts in the following order:

python 01_format_amazon.py \
	--raw-path SIGIR21-EXTRA-Datasets/reviews_Movies_and_TV_5.json.gz \
	--review-path outputs/reviews.jsonl

python 02_process_sentence.py \
	--review-path outputs/reviews.jsonl \
	--sentence-path sentences.jsonl --n-processes 8

python 03_group_sentence.py \
	--sentence-path outputs/sentences.jsonl \
	--directory outputs/ \
	--sim-thresholds 0.9 \
	--shingle-size 2 \
	--group-size 5 \
	--n-processes 8

• Update the paths (line 5-9) in keep_valid.py.
• Update the paths (line 6-9) in movielens.py, if you want to process the data into the MovieLens format.

Friendly reminder

• Run the program on a machine with sufficient memory
• Creating the datasets may take some time (e.g., hours for Yelp)

Code dependency

• Python 3.6
• NLTK
• Datasketch

Citation

@inproceedings{SIGIR21-EXTRA,
	title={EXTRA: Explanation Ranking Datasets for Explainable Recommendation},
	author={Li, Lei and Zhang, Yongfeng and Chen, Li},
	booktitle={SIGIR},
	year={2021}
}