How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility

Recommendation systems are ubiquitous and impact many domains; they have the potential to influence product consumption, individuals' perceptions of the world, and life-altering decisions. These systems are often evaluated or trained with data from users already exposed to algorithmic recommendations; this creates a pernicious feedback loop. Using simulations, we demonstrate how using data confounded in this way homogenizes user behavior without increasing utility

A food recipe recommendation system based on nutritional factors in the Finnish food communit

Abstract. This thesis presents a comprehensive study on the relationships between user feedback, recipe content, and additional factors in the context of a recipe recommendation system. The aim was to investigate the influence of various factors on user ratings and comments related to nutritional variables, while also exploring the potential for personalized recipe suggestions. Statistical analysis, clustering techniques, and sentiment analysis were employed to analyze a dataset of food recipes and user feedback. We determined that user feedback is a complex phenomenon influenced by subjective factors beyond recipe content alone. Cluster analysis identified four distinct clusters within the dataset, highlighting variations in nutritional values and sentiment among recipes. However, due to an imbalanced distribution within the clusters, these relationships were not considered in the recommendation system. To address the absence of user-related data, a content-based filtering approach was implemented, utilizing nutritional factors and a health factor calculation. The system provides personalized recipe recommendations based on nutritional similarity and health considerations. A maximum limit of 20 recommended recipes was set, allowing users to specify the desired number of recommendations. The accompanying API also provides a mean squared error metric to assess recommendation quality. This research contributes to a better understanding of user preferences, recipe content, and the challenges in developing effective recommendation systems for food recipes

Collaborative recommendations with content-based filters for cultural activities via a scalable event distribution platform

Nowadays, most people have limited leisure time and the offer of (cultural) activities to spend this time is enormous. Consequently, picking the most appropriate events becomes increasingly difficult for end-users. This complexity of choice reinforces the necessity of filtering systems that assist users in finding and selecting relevant events. Whereas traditional filtering tools enable e.g. the use of keyword-based or filtered searches, innovative recommender systems draw on user ratings, preferences, and metadata describing the events. Existing collaborative recommendation techniques, developed for suggesting web-shop products or audio-visual content, have difficulties with sparse rating data and can not cope at all with event-specific restrictions like availability, time, and location. Moreover, aggregating, enriching, and distributing these events are additional requisites for an optimal communication channel. In this paper, we propose a highly-scalable event recommendation platform which considers event-specific characteristics. Personal suggestions are generated by an advanced collaborative filtering algorithm, which is more robust on sparse data by extending user profiles with presumable future consumptions. The events, which are described using an RDF/OWL representation of the EventsML-G2 standard, are categorized and enriched via smart indexing and open linked data sets. This metadata model enables additional content-based filters, which consider event-specific characteristics, on the recommendation list. The integration of these different functionalities is realized by a scalable and extendable bus architecture. Finally, focus group conversations were organized with external experts, cultural mediators, and potential end-users to evaluate the event distribution platform and investigate the possible added value of recommendations for cultural participation

Quantifying Biases in Online Information Exposure

Our consumption of online information is mediated by filtering, ranking, and recommendation algorithms that introduce unintentional biases as they attempt to deliver relevant and engaging content. It has been suggested that our reliance on online technologies such as search engines and social media may limit exposure to diverse points of view and make us vulnerable to manipulation by disinformation. In this paper, we mine a massive dataset of Web traffic to quantify two kinds of bias: (i) homogeneity bias, which is the tendency to consume content from a narrow set of information sources, and (ii) popularity bias, which is the selective exposure to content from top sites. Our analysis reveals different bias levels across several widely used Web platforms. Search exposes users to a diverse set of sources, while social media traffic tends to exhibit high popularity and homogeneity bias. When we focus our analysis on traffic to news sites, we find higher levels of popularity bias, with smaller differences across applications. Overall, our results quantify the extent to which our choices of online systems confine us inside "social bubbles."Comment: 25 pages, 10 figures, to appear in the Journal of the Association for Information Science and Technology (JASIST

Which Factors Determine User’s First and Repeat Online Music Listening Respectively? Music Itself, User Itself, or Online Feedback

In the era of Web 2.0, does online feedback mainly dominant online users’ buying behavior, or are user’s own preference and product quality still important? Previous studies paid more attention to the influence of online feedback on users’ online buying behavior, however this paper focuses on how users’ own factors, product quality related factors and online feedback factors together influence a user’s buying behavior, and also how does this effect change as time goes by. Taking online music as our research industry and using the data from Last.fm website, this research shows that users’ preference and product quality are still the two most dominate factors influencing users’ online music listening, while online feedback plays an important role on users’ first listening. It is also found that the different influences of crowds and friends

Digital user's decision journey

The landscape of the Internet is continually evolving. This creates huge opportunities for different industries to optimize vital channels online, resulting in various-forms of new Internet services. As a result, digital users are interacting with many digital systems and they are exhibiting dynamic behaviors. Their shopping behaviors are drastically different today than it used to be, with offline and online shopping interacting with each other. They have many channels to access online media but their consumption patterns on different channels are quite different. They do philanthropy online to help others but their heterogeneous motivations and different fundraising campaigns leads to distinct path-to-contribution. Understanding the digital user’s decision making process behind their dynamic behaviors is critical as they interact with various digital systems for the firms to improve user experience and improve their bottom line. In this thesis, I study digital users’ decision journeys and the corresponding digital technology firms’ strategies using inter-disciplinary approaches that combine econometrics, economic structural modeling and machine learning. The uncovered decision journey not only offer empirical managerial insights but also provide guideline for introducing intervention to better serve digital users

O lado comportamental dos agentes de recomendação : uma revisão bibliométrica

Recommendation agents have been used to assist consumers in online purchase for almost 20 years. Their use has been studied in academic research with two different approaches. The first one addresses computational problems related to generating accurate recommendations. The other seeks to understand how user interaction with recommendation agents can alter behaviors in online shopping. Through bibliometric and scientometric methods, this study looked for the most influential papers, authors and journals in the field of behavioral recommendation research. In the present work, only articles investigating behavioral aspects of recommendation usage were considered. The identified articles were analyzed in terms of their methodology, variables and repercussion. At the end, a total of 175 articles published in journals from many different fields of academic research were found, attesting the multidisciplinary nature of this topic. Most of the studies were empirical investigations using experimental methodology, however theoretical papers showed to be more influential. It was possible to identify 29 different dependent variables used to measure the effects of recommendations in online assisted purchase. The 19 independent variables used in these studies were related to characteristics of the recommendation agent, user characteristics or vendor characteristics. Results also showed that the field still lacks confirmatory studies capable of creating a greater assurance for the knowledge already developed in the field.um período de cerca de 20 anos. Sua utilização atualmente é estudada na pesquisa acadêmica a partir de duas diferentes abordagens. A primeira se destina à resolução de problemas computacionais relacionados à geração de recomendações acuradas. A segunda tem como intuito entender como a interação do usuário com agentes de recomendação pode alterar seu comportamento de compra online. Usando um método bibliométrico e cientométrico, este estudo buscou os artigos, autores e publicações mais influentes no campo de pesquisa comportamental. Isto significa que apenas artigos que investigaram aspectos comportamentais do uso de recomendações foram considerados. Os artigos identificados foram também analisados em termos de sua metodologia, variáveis e repercussão. A maioria dos estudos se tratavam de investigações empíricas usando metodologia experimental, entretanto os artigos teóricos se demonstraram mais influentes. Também foi possível identificar 29 variáveis dependentes usadas para medir os efeitos das recomendações em compras online assistidas. As 19 variáveis independentes usadas nesses estudos estavam relacionadas com características do agente de recomendação, características do usuário ou características do vendedor. Os resultados também demonstraram que o campo ainda carece de estudos confirmatórios capazes de criar mais certeza para o conhecimento já desenvolvido na área

The Influence of Online Product Recommendations on Consumer Choice-Making Confidence, Effort, and Satisfaction

The number of products and services available online is growing at a tremendous pace. Consumers increasingly desire the ability to filter through the noise and quickly discover the products that are most relevant to their needs. Many businesses are implementing product recommender systems to provide this ability to consumers, and the result is often increased sales and more satisfied customers. However, recommender systems can also have negative consequences for consumers. For example, a recommender system can bias consumers to purchase more expensive products. Additionally, theories of consumer choice-making suggest that recommender systems can sometimes make purchase choices more difficult, resulting in outcomes that are contrary to the intended purposes of the system, such as customers expending greater shopping effort and feeling less satisfied as a result of receiving too many suggestions. The purpose of this dissertation is to further explore when recommender systems can negatively affect consumers’ online shopping experiences. I investigate three research questions: 1) When do product recommendations increase, rather than decrease, shopping effort? 2) When do product recommendations decrease, rather than increase, shopping satisfaction? And 3) When do recommender systems decrease, rather than increase, consumers’ choice-making confidence? I propose to study these questions by conducting an experiment using a fictitious retail website and online survey