Value and Impact of Recommender Systems

Communications in Computer and Information Science

et al. 2022

Recommender systems help people find relevant content in a personalized way. One main promise of such systems is that they are able to increase the visibility of items in the long tail, i.e., the lesserknown items in a catalogue. Existing research, however, suggests that in many situations today's recommendation algorithms instead exhibit a popularity bias, meaning that they often focus on rather popular items in their recommendations. Such a bias may not only lead to limited value of the recommendations for consumers and providers in the short run, but it may also cause undesired reinforcement effects over time. In this paper, we discuss the potential reasons for popularity bias and we review existing approaches to detect, quantify and mitigate popularity bias in recommender systems. Our survey therefore includes both an overview of the computational metrics used in the literature as well as a review of the main technical approaches to reduce the bias. We furthermore critically discuss today's literature, where we observe that the research is almost entirely based on computational experiments and on certain assumptions regarding the practical effects of including long-tail items in the recommendations.

Section: Methodological Issuesmentioning

confidence: 99%

Section: Potential Negative Effects Of Popularity Biasmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Mitigating Popularity Bias in Recommendation: Potential and Limits of Calibration Approaches

Klimashevskaia

Elahi

Communications in Computer and Information Science

et al. 2022

“…Thus, to truly advance the field in the future, a more holistic and interdisciplinary approach is required to obtain results that are more impactful in the real world (Jannach and Zanker 2022). Within computer science, research on human-computer interaction (HCI) aspects seem to be explored too little compared to algorithms (Konstan and Terveen 2021), even changes in the user interface of recommenders may have significant impact on the acceptance and effectiveness of a system (Garcin et al 2014;Steck, van Zwol, and Johnson 2015).…”

Section: Understanding Recommenders As Sociotechnical Systemsmentioning

confidence: 99%

Recommender systems: Trends and frontiers

Ricci

et al. 2022

AI Magazine

Self Cite

Recommender systems (RSs), as used by Netflix, YouTube, or Amazon, are one of the most compelling success stories of AI. Enduring research activity in this area has led to a continuous improvement of recommendation techniques over the years, and today's RSs are indeed often capable to make astonishingly good suggestions. With countless papers being published on the topic each year, one might think the recommendation problem is almost solved. In reality, however, the large majority of published works focuses on algorithmic improvements and relies on data-based evaluation procedures which may sometimes tell us little regarding the effects new algorithms will have in practice. This special issue contains a set of papers which address some of the open challenges and frontiers in RSs research: (i) building interactive and conversational solutions, (ii) understanding recommender systems as socio-technical systems with longitudinal dynamics, (iii) avoiding abstraction traps, and (iv) finding better ways of assessing the impact and value of recommender systems without field tests. RECOMMENDER SYSTEMS -A SUCCESS STORY, MOSTLYPersonalized suggestions for items to buy, news to read or movies to watch are nowadays ubiquitous on the web. Recommender systems are software solutions that generate these suggestions, commonly with the help of statistical models and machine learning techniques. Given their widespread use in practice, their often astonishingly good recommendations, and their proven value for consumers and providers, it is no surprise that research on recommender systems is flourishing. Today, we are witnessing a constantly growing interest in the topic both in academia and industry, with countless papers being published every year.Given this continued research interest, the use of latest deep learning technology also in industry, for example (Steck et al. 2021), and the high quality of many deployedThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

“…While recommender systems can serve various purposes and create value in different ways (Jannach and Zanker, 2021), the predominant (implicit) objective of recommender systems in literature today can be described as "guiding users to relevant items in situations where there is information overload, " or simply "finding good items" (Herlocker et al, 2000;Manouselis and Costopoulou, 2007;Cacheda et al, 2011;Kamishima et al, 2018). The most common way of operationalizing this information filtering problem is to frame the recommendation task as a supervised machine learning problem.…”

Section: Introductionmentioning

confidence: 99%

A survey on multi-objective recommender systems

Abdollahpouri

2023

Front. Big Data

Recommender systems can be characterized as software solutions that provide users with convenient access to relevant content. Traditionally, recommender systems research predominantly focuses on developing machine learning algorithms that aim to predict which content is relevant for individual users. In real-world applications, however, optimizing the accuracy of such relevance predictions as a single objective in many cases is not sufficient. Instead, multiple and often competing objectives, e.g., long-term vs. short-term goals, have to be considered, leading to a need for more research in multi-objective recommender systems. We can differentiate between several types of such competing goals, including (i) competing recommendation quality objectives at the individual and aggregate level, (ii) competing objectives of different involved stakeholders, (iii) long-term vs. short-term objectives, (iv) objectives at the user interface level, and (v) engineering related objectives. In this paper, we review these types of multi-objective recommendation settings and outline open challenges in this area.1