Given information about which options a decision-maker definitely rejects
from given finite sets of options, we study the implications for
decision-making with E-admissibility. This means that from any finite set of
options, we reject those options that no probability mass function compatible
with the given information gives the highest expected utility. We use the
mathematical framework of choice functions to specify choices and rejections,
and specify the available information in the form of conditions on such
functions. We characterise the most conservative extension of the given
information to a choice function that makes choices based on E-admissibility,
and provide an algorithm that computes this extension by solving linear
feasibility problems.

该研究利用选择函数的数学框架和条件来解决决策中的 E - 可接受性问题，并提供一种算法来计算给定信息的最保守扩展。

给定有限选择评估的电子合格性决策

Decision-making with E-admissibility given a finite assessment of choices

We present a conversational recommendation system based on a Bayesian
approach. A probability mass function over the items is updated after any
interaction with the user, with information-theoretic criteria optimally
shaping the interaction and deciding when the conversation should be terminated
and the most probable item consequently recommended. Dedicated elicitation
techniques for the prior probabilities of the parameters modeling the
interactions are derived from basic structural judgements. Such prior
information can be combined with historical data to discriminate items with
different recommendation histories. A case study based on the application of
this approach to \emph{stagend.com}, an online platform for booking
entertainers, is finally discussed together with an empirical analysis showing
the advantages in terms of recommendation quality and efficiency.

基于贝叶斯方法的对话式推荐系统，通过概率质量函数更新物品，信息理论标准最优地塑造互动并决定何时结束对话，并相应地推荐最可能的物品，结合历史数据进行先验信息推断，应用于 stagend.com 平台，并通过实证分析显示出推荐质量和效率方面的优势。

基于贝叶斯的对话式推荐系统

A Bayesian Approach to Conversational Recommendation Systems

We extend the Blahut-Arimoto algorithm for maximizing Massey's directed
information. The algorithm can be used for estimating the capacity of channels
with delayed feedback, where the feedback is a deterministic function of the
output. In order to do so, we apply the ideas from the regular Blahut-Arimoto
algorithm, i.e., the alternating maximization procedure, onto our new problem.
We provide both upper and lower bound sequences that converge to the optimum
value. Our main insight in this paper is that in order to find the maximum of
the directed information over causal conditioning probability mass function
(PMF), one can use a backward index time maximization combined with the
alternating maximization procedure. We give a detailed description of the
algorithm, its complexity, the memory needed, and several numerical examples.

本文扩展了 Blahut-Arimoto 算法，用于最大化 Massey 的有向信息，提出了一种既适用于分析具有延迟反馈信道的能力的算法，同时也提供了上下边界序列，为了找到有向信息在因果条件概率质量函数下的最大值，我们使用了反向指数时间最大化和替代最大化过程。