Cold Start with Recommendation API RRS feed

  • Question

  • Hi,

    I have collection of services and would like to recommend:

    * other services when displaying given service (item to item)

    * services for current user (user to item)

    My problem is "cold start" I'll quite likely have no usage data...(or very limited but let's assume I have none).

    How should I start recommending items using Azure Recommendation API?

    Would suggested / acceptable way would be to:

    1) Display random services and start getting usage this way.

    2) Don't recommend anything until I have enough usage data based on user's normally browsing the site. (not ideal). Perhaps use custom clustering of services to at least start with some shape of item-to-item recommendation (or rather similarity). 

    3) Enter fictional / test user data for some services e.g. get few users / testers to use website a bit and generate some usage that would kick-off recommendation engine. (perhaps later even re-upload usage data and remove test entries?)

    Which way would be recommended approach?

    2 additional questions:

    a) When / how do I find out when I have enough usage data? Assuming I have 1000 services, what is the amount of usage that I would have to have for recommendation engine to start? Is this something that can be calculated? Without usage data (or 1 entry in usage data sample application for recommendation API will not complete building the model..).

    b) documentation shows that I can add Catalog data with features and Usage data that is userId-ItemId pairs (with optional even info). Why user feature data cannot be added, wouldn't it help with recommendation?

    Thanks in advance for help :)


    Tuesday, September 29, 2015 6:35 AM


  • Hi Lucas!

    If you have user & item profile data, you could build a regular regression model using those as features to bootstrap your model. Then, as you get more usage data you can switch over to collaborative filtering.

    Hope this helps,


    • Marked as answer by neerajkh_MSFT Saturday, October 17, 2015 7:41 PM
    Monday, October 5, 2015 5:02 PM