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Stackoverflow implicit feedback recommendation system


I am trying to build a user - item recommendation engine based on the Stackoverflow favourite vote questions.

The objective:

To build a webpage / IDE plugin where the user receives his top N recommended questions based on:

- his previous favourite votes on Stackoverflow

- the programming language he is currently using (this will be a filter using the question tag, ex. only # java questions)

The input data:

I am using the Stackexchange data dump which can be found here: stackexchange directory listing; from there I've extracted the data that I thought would be useful:

Votes table (each User - Question pair represents a favourite vote for the question from the user):

UserId - QuestionId

Tags table:

QuestionId - TagId

I also have a lot details about each user/question which would make sense in a content-based approach. The only content I used so far are the question tags.

Problems/Properties of the data:

- the data consists of implicit feedback -> a user either marked a question as favourite or he didn't (binary problem 0/1)

- the data set is quite large, training and evaluating the a model takes a lot of time (votes CSV file has a few GB)

Progress so far:

So far I've tried a few different approaches, most of them are some sort of collaborative filtering:

- the first thing I tried was using cosine similarity to get top N question - question recommendations, just to test if the results are better than random

- then I've tried using Spark's Alternating Least Squares Matrix Factorisation model but the results were also mediocre, because I am using implicit feedback data and the ALS technique is built for Explicit Data

- I've also tried using another MF model with Bayesian Personalised Ranking loss function, which is better suited for implicit data. The library I used here is LightFM and the metric for evaluation is ROC AUC

Open questions / suggestions:

Do you have any suggestions of some other approaches I should use?

How would you approach this problem?

What preprocessing of the data makes sense to achieve better results?

Is any of the mentioned techniques a good choice for this problem?

Would a only content-based approach make sense?

If yes, how can I improve the results?

I should also mention ( you probably figured it out ) that I'm a CS student, new to the AI/machine learning field. The only applications I've done in the past are related to either simple regression or classification, nothing as complicated as implicit feedback recommendation systems. I know the problem/questions I've mentioned above are very specific but any help is very much appreciated.

Useful links: Welcome to LightFM's documentation! — LightFM 1.14 documentation Welcome to Spark Python API Docs! — PySpark master documentation Alternating Least Squares – Data Science Made Simpler - Bayesian Personalised Ranking MF


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We are closing this discussion since we do not handle these types of question in our community.


If you have a question about Intel specific AI frameworks/tools, we would be happy to address your queries.


Thanks & Regards,


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