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The vast product variety and product variation offered by online retailers provide an amazing amount of choice options to individuals, thus posing a big challenge to them finding and choosing interesting products which provide them the most utility. Consequently, consumers have to be satisfied with finding a product that provides them sufficient utility. Beyond that, individuals tend to even defer product choice, which is known as overchoice phenomenon.

Recommender systems have emerged in the past years as an effective method to help individuals with finding interesting products. As a result, the consumer welfare enhanced by $731 million to $1.03 billion in the year 2000 due to the increased product variety of online bookstores. Consumer welfare refers to consumers’ total satisfaction. This enhancement in consumer welfare is 7 to 10 times larger than the consumer welfare gain from increased competition and lower prices in the book market. 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, thus maximizing their utility. More precisely, the product ratings of like-minded individuals are considered by the recommender system to provide individuals recommendations. Commonly, like-minded individuals are retrieved by comparing their ratings for common rated products. This filtering technology is commonly referred to as collaborative filtering.

However, retrieving like-minded individuals based on their ratings for common rated products may be inappropriate because common rated products may not necessarily be a representative sample of two individuals’ preferences being compared. We show why and when this is the case.

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. Machine learning is a method to extract patterns to generalize from observations, thus being adequate to hypothesize individuals’ preferences from their product ratings. We present two different frameworks which retrieve like-minded individuals comparing the composition of hypothesized preferences and the predicted utilities individuals receive from products. Furthermore, we provide empirical evidence about the superiority of HCF to baseline collaborative filtering methods.

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