Article Recommender System
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Author: anenov
Datathon 2020 – Article recommendation
Article recommendation Team Fire Initial Analysis: The task of recommending and predicting a next best article is modeled as a function of the users POI (point-of-interest), the articles content and contextual information about it such as its popularity. A new user session can be decomposed of the following two parts: Initially selecting an article – […]
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.
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 their name and chat name in the beginning of their texts, so that there are no mix-ups with the other menthors. By rules it is essential to follow CRISP-DM methodology (http://www.sv-europe.com/crisp-dm-methodology/). The DSS […]
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 their name and chat name in the beginning of their texts, so that there are no mix-ups with the other menthors. By rules it is essential to follow CRISP-DM methodology (http://www.sv-europe.com/crisp-dm-methodology/). The DSS […]
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 their name and chat name in the beginning of their texts, so that there are no mix-ups with the other menthors. By rules it is essential to follow CRISP-DM methodology (http://www.sv-europe.com/crisp-dm-methodology/). The DSS […]
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 their name and chat name at the beginning of their texts so that there are no mix-ups with the other mentors. By rules, it is essential to follow CRISP-DM methodology (http://www.sv-europe.com/crisp-dm-methodology/). The DSS […]
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.
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.
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.
NewsCo: rapid non-parametric recommender algorithm for NetInfo news articles
Business understanding¶Online news reading has become very popular as the web provides access to news articles around the world. A key challenge of news websites is to help users find the articles that are interesting to read. The purpose of a recommender system is to suggest relevant items to users. Recommender systems can generate a […]