A recommendation engine in your PHP applications

Michele Orselli (27.Jan.2017 at 17:10, 1 hr )
Talk at PHPBenelux Conference 2017 (English - UK)

Rating: 3 of 5

Nowadays a lot of websites try to guess what we could like:\n”Recommendation for you in books”\n”People you may like”\nSounds familiar, isn’t it? Wouldn’t be cool if you could do the same in your application? Well, this session is for you! In the first part of this talk recommendation systems will be introduced, focusing on collaborative filtering algorithms (CR). After that we’ll dive in Prediction.io, an open source machine learning server for software developers to create predictive features, such as personalization, recommendation and content discovery. In the last part we’ll cover the integration details with a PHP application

Who are you?

Claim talk

Talk claims have been moved to the new Joind.in site.

Please login to the new site to claim your talk

 
Comments closed.

Comments

Rating: 4 of 5

27.Jan.2017 at 18:08 by Eric Rodriguez (20 comments) via Web2 LIVE

.

Rating: 2 of 5

28.Jan.2017 at 14:47 by Koen Cornelis (41 comments) via Web2 LIVE

Theoretical explanation started out great, but then kind of got lost in details. Too little practical information @ a novice level.

Rating: 3 of 5

28.Jan.2017 at 15:12 by Lineke Kerckhoffs-Willems (85 comments) via Web2 LIVE

Interesting talk, maybe could use more of a story to explain the engine better instead of just examples. Try to also focus on the English pronunciation because it was hard to follow sometimws

Rating: 3 of 5

29.Jan.2017 at 10:32 by Mathew Hucks (9 comments) via Web2 LIVE

Good talk. Well structured. A got a good idea of what I can do with Prediction.io.
As already mentioned, English should improve a little to make the presentation perfect.

Rating: 4 of 5

29.Jan.2017 at 11:29 by Daniel Nögel (2 comments) via Web2 LIVE

I especially liked the first part, as some general concepts and classifications of recommendation engines were discussed. You did also pretty good in introducing some common problems such as sparsity / cold start.
As a suggestion: I found the example part with the tables a bit long: There were quite some slides about calculating the similarity between "item1 and item2" or "alice and bob". The prediction.io part covered the general server setup as well as the API - that was good to get an overview. Perhaps you could add a short section, where you discuss the benefits and shortcomings of prediction.io in comparision to e.g. graph databases or naive usage of SQL.

Rating: 3 of 5

29.Jan.2017 at 12:52 by Kenneth Schabrechts (55 comments)

I'm with the rest of the crowd here.
Very fun intro but could have gone a little deeper into it.

Pronunciation was a little off at times but that is something that can be worked on :)

Rating: 5 of 5

29.Jan.2017 at 22:41 by Muhammed (28 comments) via Web2 LIVE

The theoratical part was really strong.

© Joind.in 2019