AI-Ready Product Recommendation Logs

This dataset provides detailed, session-level logs of product recommendations served to e-commerce users, capturing user interactions such as clicks and purchases, along with contextual information like device type, location, and experiment group. It is ideal for analyzing recommendation engine performance, optimizing personalization strategies, and conducting A/B testing to improve conversion rates.

  • last updated 20 Aug 2025
  • by GoMask
The brief that made it

Analyzing recommendation engine effectiveness and conversion rates

Sample rows

preview · 8 of 200 rows · all 15 columns
session_idstringrecommendation_contextstringproduct_pricefloatpurchasedbooleanalgorithm_versionstringuser_idstringtimestampdatetimerecommended_product_idstringrecommendation_rankintegerclickedbooleanproduct_categorystringsession_device_typestringsession_location_countrystringsession_location_citystringexperiment_groupstring
sess_10001homepage299.99truealgov1.2user_200012024-06-01T09:03:21Zprod_XC1Z91trueelectronicsdesktopUSNew Yorkcontrol
sess_10001homepage89.95falsealgov1.2user_200012024-06-01T09:05:14Zprod_Z1Y2F2truefashiondesktopUSNew Yorkcontrol
sess_10002homepage24.95falsealgov1.3user_200022024-06-01T10:14:52Zprod_BK3Y41truebooksmobileCATorontovariant_A
sess_10003cart149.99truealgov2.0-betauser_200032024-06-01T11:23:17Zprod_FG7H81truebeautytabletGBLondonvariant_B
sess_10004homepage499.95falsealgov1.0user_200042024-06-01T12:34:44Zprod_PQ2W11truehome_appliancedesktopAUSydneycontrol
sess_10005product page0falsealgov1.1user_200052024-06-01T13:45:02Zprod_TY6U73falsesportsmobileJPTokyoblank
sess_10006homepage0.01falseblankblank2024-06-01T14:14:03Zprod_EDGECASE10falseblanktabletCNBeijingblank
sess_10007cart5000truealgov2.2user_200062024-06-01T15:51:11Zprod_GC5V61trueluxurydesktopAEDubaivariant_C

What the 200 rows show

from the 200-row sample

Cart (recommendation context) stands out: 38 of its 43 rows have purchased = true, against 1 of 157 for the rest.

  • 20%purchased = true
  • 40.0median product_price
  • 3session device types
  • 4experiment groups
  • 15product categories
  • 16session location countries
Purchased rate by recommendation_contextpurchased = true
0%50%100%1%homepage1 of 970%product page0 of 6088%cart38 of 43

Cart (recommendation context)'s mean product_price is 13,205, against 72.8 for homepage and 64.7 for product page.

product_price200 rows, in bands of 10k
0100200193201000004050k100kproduct_price →

Median 40.0, from 0.0 to 100,000.

algorithm_version180 rows with a value · 20 left blank
  1. algov1.232
  2. algov1.024
  3. algov1.323
  4. algov2.0-beta21
  5. algov_unknown20
  6. algov2.117
  7. algov2.215
  8. algov1.114
  9. algov2.314
15 columns by typefrom the column list below
  • string 10
  • integer 1
  • float 1
  • datetime 1
  • boolean 2

Columns

15 columns in four groups
blueprint · 15 columns
columntypedescriptionexample
Text 10 columns
session_idstringUnique identifier for the user session in which recommendations were served.sess_10001
user_idstringUnique identifier for the user (can be anonymous or registered).optionaluser_20001
recommended_product_idstringIdentifier for the product that was recommended to the user.prod_XC1Z9
algorithm_versionstringVersion or name of the recommendation algorithm used.9 versions · optionalalgov1.2
product_categorystringCategory of the recommended product.optionalelectronics
session_device_typestringType of device used in the session (e.g., desktop, mobile, tablet).desktop · mobile · tablet · optionaldesktop
session_location_countrystringCountry from which the session originated.optionalUS
session_location_citystringCity from which the session originated.optionalNew York
experiment_groupstringName or label of the experiment group (e.g., control, variant A) for A/B testing.4 groups · optionalcontrol
recommendation_contextstringContext in which the recommendation was made (e.g., homepage, product page, cart).3 contexts · optionalhomepage
Numbers 2 columns
recommendation_rankintegerPosition of the product in the recommendation list (e.g., 1 for top recommendation).1 or more1
product_pricefloatPrice of the recommended product at the time of recommendation.0 or more · optional299.99
Dates and times 1 column
timestampdatetimeDate and time when the recommendation event occurred.2024-06-01T09:03:21Z
True or false 2 columns
clickedbooleanIndicates whether the user clicked on the recommended product.true
purchasedbooleanIndicates whether the user purchased the recommended product in the session.true

Use it for

  • purchased20%39 of 200 rowsmean product price by…72.8homepa…64.7produc…13.2kcart

    An e-commerce dashboard

    The purchased rate, product_price by recommendation_context and a breakdown of algorithm_version. Excel, Power BI or Tableau.

  • Why do 39 of 200 rows have purchased = true?

    A root-cause class exercise

    Hand out the rows and one question. The answer is in the data, not in the brief.

  • A software demo

    Believable sessions with user_id, timestamp and recommended_product_id to fill a screen in front of a buyer.

Not quite right?

Make it yours.

Same 15 columns, your size and your rules. See 20 rows before you pay.

Preview 20 rows free

1,000 rows: $1.99 · 10,000: $12.99One-time. No subscription. All prices

This dataset200 rows15 columns
Yours1,000 rows15 columnssession_location_country: UK only

blueprint · ai-ready-product-recommendation-logs

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 15 columns with generation rules behind each one. Open it in Data Factory to retune a column, add your own, wire in foreign keys, and run it at the size you actually need.

Rules it was built with
  • Track recommendation context and algorithm version
  • Session anonymization
  • Flag conversions and ignored recommendations
  • Include product category and device type
Rows
Open the blueprint in Data Factory

1 credit per row. Preview 20 rows free. Buy rows for the full run.

Exports
CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence
yours to use, including commercially
API slug
ai-ready-product-recommendation-logs

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