103 research outputs found
Distributed Information Retrieval using Keyword Auctions
This report motivates the need for large-scale distributed approaches to information retrieval, and proposes solutions based on keyword auctions
A Match in Time Saves Nine: Deterministic Online Matching With Delays
We consider the problem of online Min-cost Perfect Matching with Delays
(MPMD) introduced by Emek et al. (STOC 2016). In this problem, an even number
of requests appear in a metric space at different times and the goal of an
online algorithm is to match them in pairs. In contrast to traditional online
matching problems, in MPMD all requests appear online and an algorithm can
match any pair of requests, but such decision may be delayed (e.g., to find a
better match). The cost is the sum of matching distances and the introduced
delays.
We present the first deterministic online algorithm for this problem. Its
competitive ratio is , where is the
number of requests. This is polynomial in the number of metric space points if
all requests are given at different points. In particular, the bound does not
depend on other parameters of the metric, such as its aspect ratio. Unlike
previous (randomized) solutions for the MPMD problem, our algorithm does not
need to know the metric space in advance
Allocation Problems in Ride-Sharing Platforms: Online Matching with Offline Reusable Resources
Bipartite matching markets pair agents on one side of a market with agents,
items, or contracts on the opposing side. Prior work addresses online bipartite
matching markets, where agents arrive over time and are dynamically matched to
a known set of disposable resources. In this paper, we propose a new model,
Online Matching with (offline) Reusable Resources under Known Adversarial
Distributions (OM-RR-KAD), in which resources on the offline side are reusable
instead of disposable; that is, once matched, resources become available again
at some point in the future. We show that our model is tractable by presenting
an LP-based adaptive algorithm that achieves an online competitive ratio of 1/2
- eps for any given eps greater than 0. We also show that no non-adaptive
algorithm can achieve a ratio of 1/2 + o(1) based on the same benchmark LP.
Through a data-driven analysis on a massive openly-available dataset, we show
our model is robust enough to capture the application of taxi dispatching
services and ride-sharing systems. We also present heuristics that perform well
in practice.Comment: To appear in AAAI 201
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