Recommender systems are an important part of the modern human experience
whose influence ranges from the food we eat to the news we read. Yet, there is
still debate as to what extent recommendation platforms are aligned with the
user goals. A core issue fueling this debate is the challenge of inferring a
user utility based on engagement signals such as likes, shares, watch time
etc., which are the primary metric used by platforms to optimize content. This
is because users utility-driven decision-processes (which we refer to as
System-2), e.g., reading news that are relevant for them, are often confounded
by their impulsive decision-processes (which we refer to as System-1), e.g.,
spend time on click-bait news. As a result, it is difficult to infer whether an
observed engagement is utility-driven or impulse-driven. In this paper we
explore a new approach to recommender systems where we infer user utility based
on their return probability to the platform rather than engagement signals. Our
intuition is that users tend to return to a platform in the long run if it
creates utility for them, while pure engagement-driven interactions that do not
add utility, may affect user return in the short term but will not have a
lasting effect. We propose a generative model in which past content
interactions impact the arrival rates of users based on a self-exciting Hawkes
process. These arrival rates to the platform are a combination of both System-1
and System-2 decision processes. The System-2 arrival intensity depends on the
utility and has a long lasting effect, while the System-1 intensity depends on
the instantaneous gratification and tends to vanish rapidly. We show
analytically that given samples it is possible to disentangle System-1 and
System-2 and allow content optimization based on user utility. We conduct
experiments on synthetic data to demonstrate the effectiveness of our approach.

本文探讨了一种新的推荐系统方法，基于用户返回概率而非用户参与度来推测用户效用，以实现基于用户效用的内容优化。

System-2 推荐系统：通过时间点过程将效用与参与度解耦

System-2 Recommenders: Disentangling Utility and Engagement in  Recommendation Systems via Temporal Point-Processes

In this paper, we tackle the problem of computing a sequence of rankings with
the guarantee of the Pareto-optimal balance between (1) maximizing the utility
of the consumers and (2) minimizing unfairness between producers of the items.
Such a multi-objective optimization problem is typically solved using a
combination of a scalarization method and linear programming on bi-stochastic
matrices, representing the distribution of possible rankings of items. However,
the above-mentioned approach relies on Birkhoff-von Neumann (BvN)
decomposition, of which the computational complexity is $\mathcal{O}(n^5)$ with
$n$ being the number of items, making it impractical for large-scale systems.
To address this drawback, we introduce a novel approach to the above problem by
using the Expohedron - a permutahedron whose points represent all achievable
exposures of items. On the Expohedron, we profile the Pareto curve which
captures the trade-off between group fairness and user utility by identifying a
finite number of Pareto optimal solutions. We further propose an efficient
method by relaxing our optimization problem on the Expohedron's circumscribed
$n$-sphere, which significantly improve the running time. Moreover, the
approximate Pareto curve is asymptotically close to the real Pareto optimal
curve as the number of substantial solutions increases. Our methods are
applicable with different ranking merits that are non-decreasing functions of
item relevance. The effectiveness of our methods are validated through
experiments on both synthetic and real-world datasets.

我们介绍了一种使用 Expohedron 解决多目标优化问题的方法，该方法能够在用户效用的最大化和生产者之间的不公平性的最小化之间提供 Pareto 最优平衡，并通过识别有限数量的 Pareto 最优解来捕获组公平性和用户效用之间的权衡。我们还提出了在 Expohedron 的外接 $n$-sphere 上放松优化问题的高效方法，从而显著提高了运行时间。

追求效率的重复排名中组群之间的帕累托最优公平性

Towards Efficient Pareto-optimal Utility-Fairness between Groups in  Repeated Rankings

Competition between traditional platforms is known to improve user utility by
aligning the platform's actions with user preferences. But to what extent is
alignment exhibited in data-driven marketplaces? To study this question from a
theoretical perspective, we introduce a duopoly market where platform actions
are bandit algorithms and the two platforms compete for user participation. A
salient feature of this market is that the quality of recommendations depends
on both the bandit algorithm and the amount of data provided by interactions
from users. This interdependency between the algorithm performance and the
actions of users complicates the structure of market equilibria and their
quality in terms of user utility. Our main finding is that competition in this
market does not perfectly align market outcomes with user utility.
Interestingly, market outcomes exhibit misalignment not only when the platforms
have separate data repositories, but also when the platforms have a shared data
repository. Nonetheless, the data sharing assumptions impact what mechanism
drives misalignment and also affect the specific form of misalignment (e.g. the
quality of the best-case and worst-case market outcomes). More broadly, our
work illustrates that competition in digital marketplaces has subtle
consequences for user utility that merit further investigation.

该论文研究了基于数据驱动市场中较少被研究的领域，介绍了一种市场模型，发现市场竞争不能完美地使市场结果与用户效用相一致，具有一定的偏差。

数字市场中的竞争、对齐和平衡

Competition, Alignment, and Equilibria in Digital Marketplaces

Preference elicitation is the task of suggesting a highly preferred
configuration to a decision maker. The preferences are typically learned by
querying the user for choice feedback over pairs or sets of objects. In its
constructive variant, new objects are synthesized "from scratch" by maximizing
an estimate of the user utility over a combinatorial (possibly infinite) space
of candidates. In the constructive setting, most existing elicitation
techniques fail because they rely on exhaustive enumeration of the candidates.
A previous solution explicitly designed for constructive tasks comes with no
formal performance guarantees, and can be very expensive in (or unapplicable
to) problems with non-Boolean attributes. We propose the Choice Perceptron, a
Perceptron-like algorithm for learning user preferences from set-wise choice
feedback over constructive domains and hybrid Boolean-numeric feature spaces.
We provide a theoretical analysis on the attained regret that holds for a large
class of query selection strategies, and devise a heuristic strategy that aims
at optimizing the regret in practice. Finally, we demonstrate its effectiveness
by empirical evaluation against existing competitors on constructive scenarios
of increasing complexity.

本论文介绍了一种基于 Choice Perceptron 的算法，该算法可用于从 set-wise choice feedback 中学习用户喜好，并在理论上证明了其良好性能。该算法可解决搜索空间较大、具有 Boolean-numeric 特征空间的构造性任务。