Multi-Objective Recommender Systems (MORSs) emerged as a paradigm to
guarantee multiple (often conflicting) goals. Besides accuracy, a MORS can
operate at the global level, where additional beyond-accuracy goals are met for
the system as a whole, or at the individual level, meaning that the
recommendations are tailored to the needs of each user. The state-of-the-art
MORSs either operate at the global or individual level, without assuming the
co-existence of the two perspectives. In this study, we show that when global
and individual objectives co-exist, MORSs are not able to meet both types of
goals. To overcome this issue, we present an approach that regulates the
recommendation lists so as to guarantee both global and individual
perspectives, while preserving its effectiveness. Specifically, as individual
perspective, we tackle genre calibration and, as global perspective, provider
fairness. We validate our approach on two real-world datasets, publicly
released with this paper.

多目标推荐系统通过在全局或个体层面操作，旨在同时满足多个目标，然而当全局和个体目标共存时，现有的系统无法同时满足两类目标。本研究针对这一问题，提出了一种调节推荐列表的方法，以保证全局和个体视角的有效性，并对两个真实数据集进行了验证。

MOReGIn：全局与个体的多目标推荐

MOReGIn: Multi-Objective Recommendation at the Global and Individual  Levels

Recommender systems can be characterized as software solutions that provide
users 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 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) system level objectives.
In this paper we review these types of multi-objective recommendation settings
and outline open challenges in this area.

本文综述了多目标推荐系统的类型和竞争目标，并概述了在此领域的挑战。

多目标推荐系统：调查与挑战

Multi-Objective Recommender Systems: Survey and Challenges

Multi-objective gradient methods are becoming the standard for solving
multi-objective problems. Among others, they show promising results in
developing multi-objective recommender systems with both correlated and
conflicting objectives. Classic multi-gradient~descent usually relies on the
combination of the gradients, not including the computation of first and second
moments of the gradients. This leads to a brittle behavior and misses important
areas in the solution space. In this work, we create a multi-objective
model-agnostic Adamize method that leverages the benefits of the Adam optimizer
in single-objective problems. This corrects and stabilizes~the~gradients of
every objective before calculating a common gradient descent vector that
optimizes all the objectives simultaneously. We evaluate the benefits of
Multi-objective Adamize on two multi-objective recommender systems and for
three different objective combinations, both correlated or conflicting. We
report significant improvements, measured with three different Pareto front
metrics: hypervolume, coverage, and spacing. Finally, we show that the
\textit{Adamized} Pareto front strictly dominates the previous one on multiple
objective pairs.

本文介绍一种多目标梯度下降算法，利用 Adam optimizer 优化多目标推荐系统，通过多个 Pareto front metrics 得出该算法具有显著的改进，且优于之前的算法。

多目标推荐中基于动量的梯度方法

Momentum-based Gradient Methods in Multi-Objective Recommendation

Multi-objective recommender systems address the difficult task of
recommending items that are relevant to multiple, possibly conflicting,
criteria. However these systems are most often designed to address the
objective of one single stakeholder, typically, in online commerce, the
consumers whose input and purchasing decisions ultimately determine the success
of the recommendation systems. In this work, we address the multi-objective,
multi-stakeholder, recommendation problem involving one or more objective(s)
per stakeholder. In addition to the consumer stakeholder, we also consider two
other stakeholders; the suppliers who provide the goods and services for sale
and the intermediary who is responsible for helping connect consumers to
suppliers via its recommendation algorithms. We analyze the multi-objective,
multi-stakeholder, problem from the point of view of the online marketplace
intermediary whose objective is to maximize its commission through its
recommender system. We define a multi-objective problem relating all our three
stakeholders which we solve with a novel learning-to-re-rank approach that
makes use of a novel regularization function based on the Kendall tau
correlation metric and its kernel version; given an initial ranking of item
recommendations built for the consumer, we aim to re-rank it such that the new
ranking is also optimized for the secondary objectives while staying close to
the initial ranking. We evaluate our approach on a real-world dataset of hotel
recommendations provided by Expedia where we show the effectiveness of our
approach against a business-rules oriented baseline model.

本研究提出了一种基于多目标的重新排序方法，使用基于 Kendall tau 相关度量和其核版本的正则化函数来解决涉及多种利益相关方的推荐问题，对包括消费者、供应商和中介在内的三个利益相关方进行多目标优化，通过真实数据集验证了此方法的有效性。