MIcrosoft News Dataset (MIND) is a large-scale dataset for news recommendation research. It was collected from anonymized behavior logs of Microsoft News website. The mission of MIND is to serve as a benchmark dataset for news recommendation and facilitate the research in news recommendation and recommender systems area.
MIND contains about 160k English news articles and more than 15 million impression logs generated by 1 million users. Every news article contains rich textual content including title, abstract, body, category and entities. Each impression log contains the click events, non-clicked events and historical news click behaviors of this user before this impression. To protect user privacy, each user was de-linked from the production system when securely hashed into an anonymized ID.
Use case
Optimization of the “Top Stories” feed of a news portal
| Example observations | user_id (anonymous) history: list ≤ 50 IDs of previously read articles candidate_set: ≤ 20 articles to recommend (for each: ID, category, subcategory, title encoding, publication timestamp) context: device, time, day, approximate location |
| Example actions | Select the order (or one) of articles to serve from candidate_set: – Simple encoding for offline training: index of the article displayed in the first position. – Variant: produce a complete ranking (permutation). |
| Example reward | click ∈ {0,1} (variable clicked provided) Optional: weight by dwell_time (reading duration, field provided in MIND-large) or apply −0.1 for a non-click to encourage exploration. |