Building a Recommender Model for the Online Betting Shop Gamingtec Recommender Systems integrated within an Online Betting Shop presents a particularly intriguing area of study: there is almost no research in this field, and devising an appropriate cost function to optimize poses a non-trivial challenge. In this talk, I will explore recommender models that cater to […]
Recommender System models continue to grow in complexity, yet some relatively straightforward approaches, which might provide better solutions for most businesses, are often overlooked. In this talk, I will delve deeper into recommender models based on graph theory and graph algorithms, ranging from PageRank walks to Graph Neural Networks. This exploration will include an evaluation […]
Discover how graph algorithms are transforming content recommendation in this insightful talk. We'll journey from the basics of graph-based models, exploring simple graph walks, to the cutting-edge realm of Graph Neural Networks.Uncover the power of graph embeddings and learn when graph-based approaches excel in recommender systems. Read more at the Link PyConDE & PyData Berlin […]
Building an effective recommender system for Audio-on-Demand platforms presents unique challenges.Beyond the user-item interaction data, the inclusion of user and item metadata holds promise for enhancing recommendation quality. However, when faced with limited availability of metadata, can we still construct a robust recommender? This thought-provoking talk delves into the realm of recommender systems, exploring how […]
Explore the potential of graph algorithms, i.e. Graph Neural Networks (GNN), in predicting stock market prices. This approach, involving the analysis of relationships between hedge funds and companies, goes beyond single graphs to utilize a series of graphs for deeper insight. Think about link prediction, and link-weight prediction. Join us for insights and takeaways that […]
Why do we build Recommender Systems? Recommender Systems are central to nearly every web platform that offers things - movies, clothes, any kind of commodity - to users. Recommenders analyze patterns of user behavior to suggest items they might like but would not necessarily discover on their own, items similar to what they or users […]
How to leverage Graph Neural Networks’ solution for link prediction. Suppose you had information about the investments made by certain investors in the past few months. To make more informed investment decisions, you want to know which companies these investors are likely to invest in during the following months. You start analyzing the data but […]
Introduction Personalized product recommendations, according to a study by IBM, can lead to a 10-30% increase in revenue. But creating truly personalized experiences requires extensive user and product data, which is not always available. For example, providers with restrictions on collecting user data may only have basic product information, like genre and popularity. While collaborative […]
Shownotes +++Unsere 25. Podcast-Episode: Vom Algorithmus zur Anwendung: Ein Blick in die Welt von Data Science mit Falcony AI+++ +++ English Version below +++ In dieser Folge begrüßt René seinen Gast Mirza Klimenta den Gründer von Falcony AI, der uns tiefe Einblicke in spannende DataScience-Projekte geben wird. Wir tauchen gemeinsam ein in faszinierende Themen wie […]
In episode 15 of Recsperts, we delve into podcast recommendations with senior data scientist, Mirza Klimenta. Mirza discusses his work on the ARD Audiothek, a public broadcaster of audio-on-demand content, where he is part of pub. Public Value Technologies, a subsidiary of the two regional public broadcasters BR and SWR. We explore the use and […]
