1,010 research outputs found
Learning Personalized Risk Preferences for Recommendation
The rapid growth of e-commerce has made people accustomed to shopping online.
Before making purchases on e-commerce websites, most consumers tend to rely on
rating scores and review information to make purchase decisions. With this
information, they can infer the quality of products to reduce the risk of
purchase. Specifically, items with high rating scores and good reviews tend to
be less risky, while items with low rating scores and bad reviews might be
risky to purchase. On the other hand, the purchase behaviors will also be
influenced by consumers' tolerance of risks, known as the risk attitudes.
Economists have studied risk attitudes for decades. These studies reveal that
people are not always rational enough when making decisions, and their risk
attitudes may vary in different circumstances.
Most existing works over recommendation systems do not consider users' risk
attitudes in modeling, which may lead to inappropriate recommendations to
users. For example, suggesting a risky item to a risk-averse person or a
conservative item to a risk-seeking person may result in the reduction of user
experience. In this paper, we propose a novel risk-aware recommendation
framework that integrates machine learning and behavioral economics to uncover
the risk mechanism behind users' purchasing behaviors. Concretely, we first
develop statistical methods to estimate the risk distribution of each item and
then draw the Nobel-award winning Prospect Theory into our model to learn how
users choose from probabilistic alternatives that involve risks, where the
probabilities of the outcomes are uncertain. Experiments on several e-commerce
datasets demonstrate that our approach can achieve better performance than many
classical recommendation approaches, and further analyses also verify the
advantages of risk-aware recommendation beyond accuracy
Two-Sided Value-Based Music Artist Recommendation in Streaming Music Services
Most work on music recommendations has focused on the consumer side not the provider side. We develop a two-sided value-based approach to music artist recommendation for a streaming music scenario. It combines the value yielded for the music industry and consumers in an integrated model. For the industry, the approach aims to increase the conversion rate of potential listeners to adopters, which produces new revenue. For consumers, it aims to improve their utility related to recommendations they receive. We use one year of listening records for 15,000+ Last.fm users to train and test the proposed recommendation model on 143 artists. Compared to collaborative filtering, the results show some improvement in recommendation performance by considering both sides’ value in con-junction with other factors, including time, location, external information and listening behavior
The effects of variety and bundling on choice and satisfaction: Applications to new telecommunication and media services
The purpose of this working paper is twofold; 1)to review consumer behavior literature on how assortment variety and bundling influence choice related variables, and 2)to apply this review on an analysis of telecommunication and new services. Literature related to the characteristics of assortment/bundle, perception of the assortment/bundle, perception of the choice situation, choice, perception of the choice, and experience with the chosen option is reviewed with focus on assortment and bundling. The review is based on an open literature search using keywords as “assortment size”, “assortment variety”, “bundling” and “unbundling” in databases as ISI and Ebsco. In addition, manual reviews of references used in the articles revealed from the databases have also been used to make sure we cover as many relevant articles as possible. The articles reviewed are briefly summarized in table 1 (assortment studies) and table 2 (bundling studies).
Based on the literature reviewed, the results revealed are applied in a theoretical analysis of the effects of variety and bundling on choice- and post-choice related variables in new telecommunication and media services. Six services are discussed; traditional telephony and broadband services, mobile internet services and applications, services in heterogeneous access networks, multiplay services, TV-channel network services, and online video services. The analyses focus on potential effects of assortment variety and bundling on choice and post choice related variables for each of the six services. Because regulatory authorities typically use variety to stimulate efficient competition, some regulatory issues of relevance for each of the six services are also briefly discussed.
The main results from the general consumer literature review on variety and bundling is summarized. A brief summary of what seems to be the most relevant issues related to variety, bundling, and regulatory actions for the six telecommunication and media services analyzed is also presented. The review of the literature and the analyses of the six services show a significant need for research on how variety and bundling influence choice and choice related variables. A discussion of potential routes for future research together with a preliminary draft of a research model closes the discussion of this working paper
Not as good as it used to be: Do streaming platforms penalize quality?
We study the incentives of a streaming platform to bias consumption when products are vertically differentiated. The platform offers mixed bundles of content to monetize consumers' interest in variety and pays royalties to sellers based on the effective consumption of the content they produce. When products are not vertically differentiated, the platform has no incentive to bias consumption in equilibrium: the platform being active represents a Pareto-improvement compared to the case in which she is not. With vertical differentiation, royalties can differ; the platform always biases recommendations in favor of the cheapest content, which hurts consumers and the high-quality seller. Biased recommendation always diminishes the incentives of a seller to increase the quality of her content for a given demand. If a significant share of the users is ex-ante unaware of the existence of the sellers the platform can bias recommendations more freely, but joining the platform encourages investment in quality. The bias, however, can lead to inefficient allocation of R&D efforts. From a policy perspective, we propose this as a novel rationale for regulating algorithmic recommendations in streaming platform
On the Disclosure of Promotion Value in Platforms with Learning Sellers
We consider a platform facilitating trade between sellers and buyers with the
objective of maximizing consumer surplus. Even though in many such marketplaces
prices are set by revenue-maximizing sellers, platforms can influence prices
through (i) price-dependent promotion policies that can increase demand for a
product by featuring it in a prominent position on the webpage and (ii) the
information revealed to sellers about the value of being promoted. Identifying
effective joint information design and promotion policies is a challenging
dynamic problem as sellers can sequentially learn the promotion value from
sales observations and update prices accordingly. We introduce the notion of
confounding promotion policies, which are designed to prevent a Bayesian seller
from learning the promotion value (at the expense of the short-run loss of
diverting consumers from the best product offering). Leveraging these policies,
we characterize the maximum long-run average consumer surplus that is
achievable through joint information design and promotion policies when the
seller sets prices myopically. We then establish a Bayesian Nash equilibrium by
showing that the seller's best response to the platform's optimal policy is to
price myopically at every history. Moreover, the equilibrium we identify is
platform-optimal within the class of horizon-maximin equilibria, in which
strategies are not predicated on precise knowledge of the horizon length, and
are designed to maximize payoff over the worst-case horizon. Our analysis
allows one to identify practical long-run average optimal platform policies in
a broad range of demand models
Fairness in Recommendation: Foundations, Methods and Applications
As one of the most pervasive applications of machine learning, recommender
systems are playing an important role on assisting human decision making. The
satisfaction of users and the interests of platforms are closely related to the
quality of the generated recommendation results. However, as a highly
data-driven system, recommender system could be affected by data or algorithmic
bias and thus generate unfair results, which could weaken the reliance of the
systems. As a result, it is crucial to address the potential unfairness
problems in recommendation settings. Recently, there has been growing attention
on fairness considerations in recommender systems with more and more literature
on approaches to promote fairness in recommendation. However, the studies are
rather fragmented and lack a systematic organization, thus making it difficult
to penetrate for new researchers to the domain. This motivates us to provide a
systematic survey of existing works on fairness in recommendation. This survey
focuses on the foundations for fairness in recommendation literature. It first
presents a brief introduction about fairness in basic machine learning tasks
such as classification and ranking in order to provide a general overview of
fairness research, as well as introduce the more complex situations and
challenges that need to be considered when studying fairness in recommender
systems. After that, the survey will introduce fairness in recommendation with
a focus on the taxonomies of current fairness definitions, the typical
techniques for improving fairness, as well as the datasets for fairness studies
in recommendation. The survey also talks about the challenges and opportunities
in fairness research with the hope of promoting the fair recommendation
research area and beyond.Comment: Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST
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