license and documentation

LICENSE.txt

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README.md

@@ -1,3 +1,18 @@
 ---
 license: apache-2.0
 ---
+
+# TREC 2025 Product Recommendation Data
+
+This is the data for the [recommendation task](https://trec-product-search.github.io/recommendations.html) for the TREC 2025 Product Search and Recommendation Task.
+
+The `initial` directory contains the initial corpus and training data release.
+This may be updated as we get further along in the timeline.
+
+> [!NOTE]
+>
+> This data is derived from the Amazon [ESCI] and [M2] data sets, each under the
+> Apache license (version 2.0).
+
+[ESCI]: https://amazonkddcup.github.io/
+[M2]: https://kddcup23.github.io/#data

initial/README.md

@@ -1,6 +1,24 @@
-# Initial Product Corpus
+# Initial Product Data
 
-(Initial because somethings may change in the future)
+This folder contains the initial release of the corpus and training/validation data for the TREC 2025 Product Recommendation track.
+See the [track instructions][] for task details.
+
+Included here are:
+
+- The **corpus** of product data
+- Training and preliminary **evaluation data** consisting of product labels
+
+You are not limited to the data in this repository for your modeling — you are
+free to draw from the M2 data, the ESCI data, and/or the [Amazon Reviews][] data
+set for further training for your models and systems.  We cannot redistribute or
+archive the review data, so it is not included here.
+
+[track instructions]: https://trec-product-search.github.io/recommendations.html
+[Amazon Reviews]: https://huggingface.co/datasets/McAuley-Lab/Amazon-Reviews-2023
+
+(Initial because some things may change in the future.)
+
+## Corpus
 
 This dataset contains a **combined and deduplicated product metadata corpus**, created by merging two sources:
 
@@ -74,7 +92,34 @@ The merged dataset contains product records that match a corresponding entry in
   "author": null,
   "product_bullet_point": "Noise cancellation; Long battery life; Compact design"
 }
-````
+```
+
+## Training and Evaluation
+
+We provide initial queries and qrels for training and initial validation of your system.
+These are derived from the [ESCI][] data set, processed to encode product relationships.
+They can be used to evaluate the Substitute and Complementary runs from a system.
+
+There are two files:
+
+-   `trec-esci.queries.gz` contains the “queries”, consisting of a query
+    identifier (e.g. 1S) and a reference product ASIN.
+-   `trec-esci.qrels.gz` contains the qrels, including the query identifier,
+    candidate product ASINs, and 0/1 relevance labels (currently always 1).
+
+These are derived from ESCI by using products marked as `E` for some query as
+the reference items, and considering other items annotated for the same query as
+target items.  The annotations are derived as follows:
+
+-   A target item is relevant for the S query if it is labeled as either `S` or
+    `E` for the same query for which the reference item is labeled `E`.
+-   A target item is relevant for the C query if it is labeled `C` for some
+    query for which the reference item is labeled `E`.
+
+The data is filtered to only include reference items with at least 5 items in
+each of the substitute and complement lists.
+
+[ESCI]: https://amazonkddcup.github.io/
 
 ---