Datathon 2020 SolutionsRecommendation systems

Datathon2020 – NetInfo Article Recommender – Solution – nextClick

Giving a recommendation to the user to catch his eye on and meet his preferences is essential task for a recommendation system. The amount of data is increasing significantly and the idea is to get some knowledge from it. Taking advantage of user similarities or news similarities will provide useful information to predict which article will the user find interesting.


3 thoughts on “Datathon2020 – NetInfo Article Recommender – Solution – nextClick

  1. 0

    1. I would suggest to remove duplicates over article and user – it does not make sense to me to count how many times a user read an article
    2. I would suggest to use NLP to derive topics or categories from Titles
    3. What is “maunfinishedtrix”
    4. What is PQ decomposition?
    3. Why dont you use some matrix factorization implementation out of the box

    I do not see results from your model.

  2. 0

    The article goes in the right direction: discusses content-based vs. collaborative filtering. It feels a bit short though…

    At the end, the authors are in favor of collaborative filtering. Why is that? How about combining both? Shameless self-promotion:

    Having a neural model is nice these days, but it is unclear to me what model exactly. What is “maunfinishedtrix factorization”?

    Were there any experiments actually performed? I see no results.

    Any ideas? Any insights from the data? Any graphs? What is next?

  3. 0

    Hi guys, very nice work, and indeed in the right direction.

    I understand that “maunfinishedtrix factorization” is a copy-paste mistake. It should have been “unfinished matrix factorization” 😉
    I join my colleague reviewers suggesting to use an out of the box implementation of such things in the future. Pay attention that matrix factorization on scale might be costly. There are approaches such as “word2Vec” which are exactly matrix factorization (

    You are correct – there is no clear measurement of whether the user liked the article or not: no amount of time in the URL, nor rating. Using recurrent visits is a clever idea (!). But, please consider that it could also be that returning to a previously read article is because the information in it is timeless.

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