Matteo Brucato

Whanhee Cho, Kuangfei Long, Mahmood Jasim et al.

Selecting a bundle of items that collectively satisfies constraints is a fundamental task across databases, recommender systems, and text summarization. Unlike traditional retrieval that returns individual or top-k items, bundle retrieval is inherently combinatorial and, in general, NP-hard. Although package queries can efficiently retrieve bundles given a well-formed query, two key user-centric challenges remain: (1) expressing and tuning multi-dimensional bundle intent through a user-friendly interface, and (2) ensuring feasibility when the query yields empty results. We introduce Ex2Bundle, an Example-driven Bundle retrieval framework that enables users to specify their intent through example bundles and automatically synthesizes package queries that capture the intent implicit in those example bundles via aggregate constraints. Ex2Bundle also addresses a challenge unique to bundle retrieval: when inferred aggregate constraints are infeasible over the target data, our data-aware constraint relaxation minimally adjusts the constraint bounds while preserving alignment with user intent. We instantiate a specific application of focused text snippet extraction by example to demonstrate the efficacy of the Ex2Bundle framework. Extensive experiments over real-world datasets and a user study demonstrate that Ex2Bundle improves usability and consistently returns intent-aligned bundles even under distributional shifts of the target database.

Matteo Brucato, Fjodor Kholodkov, Soren Little et al.

DeQL (Decision Query Language) extends SQL to express decision queries: given options drawn from relational data, constraints from policy, and a measurable objective, a DeQL query computes the best course of action. Two constructs carry the extension: CREATE CANDIDATES, which defines the space of options from relational sources, and DECIDE, which declares decision variables, named constraints, and an objective over them. The design follows SQL's principles: the user states what to optimize while the engine chooses how to solve it, every query consumes and produces relations, and the structure of a problem stays visible to the engine. This document specifies the language (its design principles, syntax, formal grammar, and execution model) with examples spanning subset selection, allocation, assignment, scheduling, and decisions at multiple levels of aggregation, and extensions for optimization under uncertainty, inline model scoring, and time- and quality-bounded solving. It is the first version of the specification; the language is under active development, and this version fixes the core constructs on which later revisions will build.

Matteo Brucato, Azza Abouzied, Chris Blauvelt

On Kickstarter only 36% of crowdfunding campaigns successfully raise sufficient funds for their projects. In this paper, we explore the possibility of redistribution of crowdfunding donations to increase the chances of success. We define several intuitive redistribution policies and, using data from a real crowdfunding platform, LaunchGood, we assess the potential improvement in campaign fundraising success rates. We find that an aggressive redistribution scheme can boost campaign success rates from 37% to 79%, but such choice-agnostic redistribution schemes come at the cost of disregarding donor preferences. Taking inspiration from offline giving societies and donor clubs, we build a case for choice preserving redistribution schemes that strike a balance between increasing the number of successful campaigns and respecting giving preference. We find that choice-preserving redistribution can easily achieve campaign success rates of 48%. Finally, we discuss the implications of these different redistribution schemes for the various stakeholders in the crowdfunding ecosystem.