Motivated by the difficulty of specifying complete ordinal preferences over a
large set of $m$ candidates, we study voting rules that are computable by
querying voters about $t < m$ candidates. Generalizing prior works that focused
on specific instances of this problem, our paper fully characterizes the set of
positional scoring rules that can be computed for any $1 \leq t < m$, which
notably does not include plurality. We then extend this to show a similar
impossibility result for single transferable vote (elimination voting). These
negative results are information-theoretic and agnostic to the number of
queries. Finally, for scoring rules that are computable with limited-sized
queries, we give parameterized upper and lower bounds on the number of such
queries a deterministic or randomized algorithm must make to determine the
score-maximizing candidate. While there is no gap between our bounds for
deterministic algorithms, identifying the exact query complexity for randomized
algorithms is a challenging open problem, of which we solve one special case.

通过对候选人的完整排序偏好的难以确定性我们研究了能够通过查询选民关于 t <m 个候选人的规则计算的投票规则。在先前研究的基础上，我们的研究全面描述了对于任何 1≤t < m 时可以计算的位置评分规则集合，特别地，这不包括多数派规则。然后，我们将这一结论推广到单可变投票 (淘汰投票) 中也出现了类似的不可能性结果。这些负面结果是信息理论的，并且与查询数量无关。最后，对于可以用有限大小的查询计算的评分规则，我们针对决定得分最大候选人的确定性或随机算法必须进行的查询数量提供了参数化的上界和下界。对于确定性算法而言，我们的边界是完全相同的，然而对于随机算法而言，确定其精确的查询复杂度是一个具有挑战性的开放问题，我们解决了其中一个特殊情况。

运用不完整调查数据计算选举规则

Computing Voting Rules with Elicited Incomplete Votes

To help their users to discover important items at a particular time, major
websites like Twitter, Yelp, TripAdvisor or NYTimes provide Top-K
recommendations (e.g., 10 Trending Topics, Top 5 Hotels in Paris or 10 Most
Viewed News Stories), which rely on crowdsourced popularity signals to select
the items. However, different sections of a crowd may have different
preferences, and there is a large silent majority who do not explicitly express
their opinion. Also, the crowd often consists of actors like bots, spammers, or
people running orchestrated campaigns. Recommendation algorithms today largely
do not consider such nuances, hence are vulnerable to strategic manipulation by
small but hyper-active user groups.
To fairly aggregate the preferences of all users while recommending top-K
items, we borrow ideas from prior research on social choice theory, and
identify a voting mechanism called Single Transferable Vote (STV) as having
many of the fairness properties we desire in top-K item (s)elections. We
develop an innovative mechanism to attribute preferences of silent majority
which also make STV completely operational. We show the generalizability of our
approach by implementing it on two different real-world datasets. Through
extensive experimentation and comparison with state-of-the-art techniques, we
show that our proposed approach provides maximum user satisfaction, and cuts
down drastically on items disliked by most but hyper-actively promoted by a few
users.

本文提出一种使用单可转移投票机制对用户进行 Top-K 推荐的方法，以获取更多用户的偏好并减少少数用户对超级活跃推广的物品的影响。通过两个真实世界数据集的实验和与最先进技术的比较，我们显示这种方法提供了最大的用户满意度。

平等的发言权：走向众包 Top-K 推荐中的公正代表

Equality of Voice: Towards Fair Representation in Crowdsourced Top-K  Recommendations

A voting bloc is defined to be a group of voters who have similar voting
preferences. The cleavage of the Irish electorate into voting blocs is of
interest. Irish elections employ a ``single transferable vote'' electoral
system; under this system voters rank some or all of the electoral candidates
in order of preference. These rank votes provide a rich source of preference
information from which inferences about the composition of the electorate may
be drawn. Additionally, the influence of social factors or covariates on the
electorate composition is of interest. A mixture of experts model is a mixture
model in which the model parameters are functions of covariates. A mixture of
experts model for rank data is developed to provide a model-based method to
cluster Irish voters into voting blocs, to examine the influence of social
factors on this clustering and to examine the characteristic preferences of the
voting blocs. The Benter model for rank data is employed as the family of
component densities within the mixture of experts model; generalized linear
model theory is employed to model the influence of covariates on the mixing
proportions. Model fitting is achieved via a hybrid of the EM and MM
algorithms. An example of the methodology is illustrated by examining an Irish
presidential election. The existence of voting blocs in the electorate is
established and it is determined that age and government satisfaction levels
are important factors in influencing voting in this election.

提出了一种基于混合专家模型的方法，将爱尔兰选民聚类为投票集团，并研究社会因素对其聚类的影响和投票集团的特性，以揭示爱尔兰选举的投票模式。