Share products you use at work,explore what others are using
Start new thread
Kevin William David

Product Recommendations AI - Personalized software recommendations based on your stack

by•

Personalized Product Recommendations AI answers the question "What software product should I use?

A machine learning solution that can be used at scale to make software recommendations tailored to each user. Powering the models is data on nearly 33,000 products and over 375,000 companies that use and recommend them.

Add a comment

Replies

Best
Hrant
Good job! Abstract thinking at its best.
Swaroop Hegde
This is awesome! I've been an early user and it's great to see this new seamless way to get recommendations. One of the companies I contract with was able to make a better decision on their marketing software thanks to Siftery!
Jesse Ditson
I gotta be honest, these recommendations are literally the opposite of what they should be: Also, this is misleading - when it suggested JIRA as a backtrace replacement, I was surprised that JIRA had added crash tracking and analytics until I realized the engine was just arbitrarily grouping bug-tracking-related tools. This is **bad** because I already field requests from management to use tools that are not good fits. Imagine how much worse my day could be if they now had "AI recommendations" for tools that don't even do the job they think they do. If the goal is to use incumbent products maybe this would be useful for companies trying to estimate size, but without quantification it's not even that useful in that sense. The "AI" just appears to show me the most used product. I would posit that it's a flawed thesis to correlate "most used" and "best". There are also some clear misses (suggests modernizr as a replacement for react native, which doesn't even come close to making sense). It looks like a lot of work was put in to the product, which is unfortunate given that the underlying models are fundamentally wrong, and ultimately the tool is most useful as ammunition for management that is willfully ignorant or resistance to change.
Gerry Giacoman Colyer
@jesseditson Thanks. Harsh feedback is often the most helpful, but I also hope you'll give this its fair shake. From your screenshot, it looks like you're going through the Basic recommendations. The Advanced Recommendations flow is going to be much better - literally 4x more models at work. We cannot do much to personalize recommendations for your company if we don't know to which company you're affiliated. In the Advanced engine you'll also be able to mark recommendations as "Not Helpful", which is a super valuable signal for us. Example of that button below: Based on the feedback we're seeing here and in the data, it looks like our Developer role recommendations need the most work, while others are getting better-than-expected feedback. I also want to set reasonable expectations: We see this engine as a starting point in the product discovery journey, not the end. Given that there are so many products being created (36k in our database now, more to come soon), it's becoming increasingly difficult for most people to be even aware of everything that's out there. While most discovery resources are still focused on maintaining an increasingly-long list of products, this is our effort to help winnow that list down for most users in just a couple of minutes. Anyway, I do appreciate you taking the time to share these thoughts!!
Natali Molko
set up, so will see)
Samarth Jajoo
Uh HTML5 isn't an alternative to python
Gerry Giacoman Colyer
@jajoosam Hey Samarth, yeah - looks like this happened because one of our models is based on popular alternatives within a category. In this case both are grouped as "Languages". We'd solve this by getting more granular categorization (we have roughly 750 categories at this point). Did you find any of the other recommendations useful? If you bookmark or mark any of the recommendations as "Not Helpful" in the app, the algorithm uses that data to make better recommendations for the next user.
First
Previous
12