Author: anenov

Posted totopopov, anenovLeave a commentPosted in NLPNLP

Net Info 2020 Case about articles

In the dawn of the digital age, everybody’s voice is empowered so it can be heard by everyone. However when everyone speaks their voice out regarding a subject, the situation awareness is drowned in a sea of personal views. This in turn makes it harder to make effective informative decisions due to vague situation awareness. One way to improve this is to cluster the information into “world views”. Net Info has shared news articles in light of developing a semantic cluster analysis algorithm.

Posted junior, stephen, zenpanik, alex-efremov, pepe, tomislavk, liad, preslav, tony, laura, mladensavov, anenov, metodinikolov, drceenish, leomrsic, boryana, mcneto, jjagusch, drsubhabahapal, sist, jans, gkmitov, metodinikolov, gloria, quintshekhar1 CommentPosted in GD2018 Mentors, MentorsGD2018 Mentors, Mentors

Datathon NSI Mentors’ Guidelines – Economic Time Series Prediction

In this article the mentors give some preliminary guidelines, advice and suggestions to the participants for the case. Every mentor should write […]

Posted junior, zenpanik, alex-efremov, pepe, tomislavk, liad, preslav, tony, laura, mladensavov, anenov, metodinikolov, drceenish, leomrsic, boryana, mcneto, jjagusch, drsubhabahapal, sist, jans, gkmitov, metodinikolov, gloria, quintshekharLeave a commentPosted in GD2018 Mentors, MentorsGD2018 Mentors, Mentors

Datathon Telenor Mentors’ Guidelines – On TelCo predictions

In this article the mentors give some preliminary guidelines, advice and suggestions to the participants for the case. Every mentor should write […]

Posted junior, zenpanik, alex-efremov, pepe, tomislavk, liad, preslav, tony, laura, mladensavov, anenov, metodinikolov, drceenish, leomrsic, boryana, mcneto, jjagusch, drsubhabahapal, sist, jans, gkmitov, metodinikolov, gloria, quintshekharLeave a commentPosted in GD2018 Mentors, MentorsGD2018 Mentors, Mentors

Datathon Sofia Air Mentors’ Guidelines – On IOT Prediction

In this article the mentors give some preliminary guidelines, advice and suggestions to the participants for the case. Every mentor should write […]

Posted junior, zenpanik, alex-efremov, pepe, tomislavk, liad, preslav, tony, laura, mladensavov, anenov, metodinikolov, drceenish, leomrsic, boryana, mcneto, jjagusch, drsubhabahapal, sist, jans, gkmitov, metodinikolov, gloria, quintshekharLeave a commentPosted in GD2018 Mentors, MentorsGD2018 Mentors, Mentors

Datathon Kaufland Mentors’ Guidelines – On Predictive Maintenance

In this article, the mentors give some preliminary guidelines, advice, and suggestions to the participants for the case. Every mentor should write […]

Posted shanu16, melons Mentors: airsrg, anenov, junior10 CommentsPosted in Datathon 2020 Solutions, Recommendation systemsDatathon 2020 Solutions, Recommendation systems

Datathon 2020 – NetInfo Article Recommender – Newbies

The project aims to build a recommender system for the website called Vesti.bg. As the company runs to serve its huge customer base (as clear from the given data!) completely and for their best interests. And in order to do that it wants to recommend its users with articles that they should read next (based on mimicking their reading pattern). This is expected to save a lot of its users’ time in thinking. Also, with better and faster recommendations come people’s interest and that results in the company’s growth. The Company actually has a huge customer base. So, providing them with what they might like can really help it in making good money.

Posted ss3151, ss3151 Mentors: anenov3 CommentsPosted in Datathon 2020 Solutions, Recommendation systemsDatathon 2020 Solutions, Recommendation 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.

Posted totopopov, anenov1 CommentPosted in Datathon casesDatathon cases

Datathon2020 – Article recommender case – provided by NetInfo

For the past two decades we have been witnessing a never seen before access to information on one hand and at the same time the volume of the information has been exponentially growing. The rule: 90% of all data has been created in the past 2 years is still standing. This has lead to information overloading and the rise of recommendation systems. Guiding the user in this pool of data has proven to be critical for business success as we can see from YouTube, Amazon, Netflix and many others. Net Info has prepared another challenge: The next best article.