I am trying to build a user - item recommendation engine based on the Stackoverflow favourite vote questions.
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
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 https://www.kaggle.com/iancuv/lightfm-demo?scriptVersionId=3670161
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.
https://arxiv.org/pdf/1205.2618.pdf - Bayesian Personalised Ranking MF