Hypothesis Based Collaborative Filtering
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Hypothesis-Based Collaborative Filtering
Recommender systems have emerged to help individuals with finding interesting products. As a result, the consumer welfare enhances due to the increased product variety. In other words, recommender systems are essential for increasing consumers welfare, which ultimately leads to an increase of economic and social welfare.Typically, recommender systems use the collective wisdom of individuals for exposing individuals to products which best fits their preferences. More precisely, the most like-minded individuals are considered by the recommender system to provide individuals recommendations. This is commonly referred to as collaborative filtering.In this dissertation, we present hypothesis-based collaborative filtering (HCF) to expose individuals to products which best fits their preferences. HCF retrieves like-minded individuals based on the similarity of their hypothesized preferences by means of machine learning algorithms hypothesizing individuals' preferences.
Influence of Recommender Systems on Consumer Behavior
In the dynamic landscape of digital platforms, recommender systems silently guide our decisions, shaping what we watch, read, and buy. But how do these algorithms influence our behavior beyond convenience and personalization? This dissertation delves into the psychological undercurrents of recommender systems, uncovering how subtle design choices trigger behavioral biases that affect consumer preferences, beliefs, and decisions. Through a series of rigorous field experiments, this work explores three critical dimensions of recommender systems: item selection, ranking, and recommendation design. It reveals how phenomena like assimilation and contrast effects, as well as visual cues such as product badges, can subtly yet powerfully steer consumer behavior. Challenging traditional recommender system design priorities, the findings highlight that similarity can sometimes outweigh diversity and that strategic positioning can defy conventional assumptions about user attention. Combining a systematic literature review with empirical evidence from real-world settings, this dissertation offers insights into the interplay between technology and human cognition. It is an essential read for scholars, practitioners, and platform designers seeking to understand - and ethically harness - the behavioral dynamics of recommender systems.
Data Science from Scratch
Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. With this updated second edition, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out.