15,969 research outputs found
Matching Users' Preference Under Target Revenue Constraints in Optimal Data Recommendation Systems
This paper focuses on the problem of finding a particular data recommendation
strategy based on the user preferences and a system expected revenue. To this
end, we formulate this problem as an optimization by designing the
recommendation mechanism as close to the user behavior as possible with a
certain revenue constraint. In fact, the optimal recommendation distribution is
the one that is the closest to the utility distribution in the sense of
relative entropy and satisfies expected revenue. We show that the optimal
recommendation distribution follows the same form as the message importance
measure (MIM) if the target revenue is reasonable, i.e., neither too small nor
too large. Therefore, the optimal recommendation distribution can be regarded
as the normalized MIM, where the parameter, called importance coefficient,
presents the concern of the system and switches the attention of the system
over data sets with different occurring probability. By adjusting the
importance coefficient, our MIM based framework of data recommendation can then
be applied to system with various system requirements and data
distributions.Therefore,the obtained results illustrate the physical meaning of
MIM from the data recommendation perspective and validate the rationality of
MIM in one aspect.Comment: 36 pages, 6 figure
Beyond Personalization: Research Directions in Multistakeholder Recommendation
Recommender systems are personalized information access applications; they
are ubiquitous in today's online environment, and effective at finding items
that meet user needs and tastes. As the reach of recommender systems has
extended, it has become apparent that the single-minded focus on the user
common to academic research has obscured other important aspects of
recommendation outcomes. Properties such as fairness, balance, profitability,
and reciprocity are not captured by typical metrics for recommender system
evaluation. The concept of multistakeholder recommendation has emerged as a
unifying framework for describing and understanding recommendation settings
where the end user is not the sole focus. This article describes the origins of
multistakeholder recommendation, and the landscape of system designs. It
provides illustrative examples of current research, as well as outlining open
questions and research directions for the field.Comment: 64 page
The Role of the Mangement Sciences in Research on Personalization
We present a review of research studies that deal with personalization. We synthesize current knowledge about these areas, and identify issues that we envision will be of interest to researchers working in the management sciences. We take an interdisciplinary approach that spans the areas of economics, marketing, information technology, and operations. We present an overarching framework for personalization that allows us to identify key players in the personalization process, as well as, the key stages of personalization. The framework enables us to examine the strategic role of personalization in the interactions between a firm and other key players in the firm's value system. We review extant literature in the strategic behavior of firms, and discuss opportunities for analytical and empirical research in this regard. Next, we examine how a firm can learn a customer's preferences, which is one of the key components of the personalization process. We use a utility-based approach to formalize such preference functions, and to understand how these preference functions could be learnt based on a customer's interactions with a firm. We identify well-established techniques in management sciences that can be gainfully employed in future research on personalization.CRM, Persoanlization, Marketing, e-commerce,
FATREC Workshop on Responsible Recommendation Proceedings
We sought with this workshop, to foster a discussion of various topics that fall under the general umbrella of responsible recommendation: ethical considerations in recommendation, bias and discrimination in recommender systems, transparency and accountability, social impact of recommenders, user privacy, and other related concerns. Our goal was to encourage the community to think about how we build and study recommender systems in a socially-responsible manner.
Recommendation systems are increasingly impacting people\u27s decisions in different walks of life including commerce, employment, dating, health, education and governance. As the impact and scope of recommendations increase, developing systems that tackle issues of fairness, transparency and accountability becomes important. This workshop was held in the spirit of FATML (Fairness, Accountability, and Transparency in Machine Learning), DAT (Data and Algorithmic Transparency), and similar workshops in related communities. With Responsible Recommendation , we brought that conversation to RecSys
Modeling Recommender Ecosystems: Research Challenges at the Intersection of Mechanism Design, Reinforcement Learning and Generative Models
Modern recommender systems lie at the heart of complex ecosystems that couple
the behavior of users, content providers, advertisers, and other actors.
Despite this, the focus of the majority of recommender research -- and most
practical recommenders of any import -- is on the local, myopic optimization of
the recommendations made to individual users. This comes at a significant cost
to the long-term utility that recommenders could generate for its users. We
argue that explicitly modeling the incentives and behaviors of all actors in
the system -- and the interactions among them induced by the recommender's
policy -- is strictly necessary if one is to maximize the value the system
brings to these actors and improve overall ecosystem "health". Doing so
requires: optimization over long horizons using techniques such as
reinforcement learning; making inevitable tradeoffs in the utility that can be
generated for different actors using the methods of social choice; reducing
information asymmetry, while accounting for incentives and strategic behavior,
using the tools of mechanism design; better modeling of both user and
item-provider behaviors by incorporating notions from behavioral economics and
psychology; and exploiting recent advances in generative and foundation models
to make these mechanisms interpretable and actionable. We propose a conceptual
framework that encompasses these elements, and articulate a number of research
challenges that emerge at the intersection of these different disciplines
All that Glitters is not Gold: Understanding the Impacts of Platform Recommendation Algorithm Changes on Complementors in the Sharing Economy
Sharing platforms often leverage recommendation algorithms to reduce matching costs and improve buyer satisfaction. However, the economic impacts of different recommendation algorithms on the business operations of complementors remains unclear. This study uses natural quasi-experiments and proprietary data from a home-cooked food-sharing platform with two recommendation algorithms: word-of-mouth recommendation (WMR) and botler personalization recommendation (BPR). Results show the WMR negatively affects revenue while BPR has a positive effect. The contrast revenue effects have been attributed to capacity constraints for complementors and matching frictions for consumers. WMR encourages sellers to specialize in high-quality products but limits new product development. BPR promotes innovation to suit diverse customer tastes but may reduce quality. This reflects the exploration-exploitation trade-off: WMR exploits existing competences, while BPR explores new products to satisfy personal preferences. The authors discuss implications for how to utilize recommendation algorithms and artificial intelligence for the prosperity of sharing economy platforms
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