3,313 research outputs found
Show Me the Money: Dynamic Recommendations for Revenue Maximization
Recommender Systems (RS) play a vital role in applications such as e-commerce
and on-demand content streaming. Research on RS has mainly focused on the
customer perspective, i.e., accurate prediction of user preferences and
maximization of user utilities. As a result, most existing techniques are not
explicitly built for revenue maximization, the primary business goal of
enterprises. In this work, we explore and exploit a novel connection between RS
and the profitability of a business. As recommendations can be seen as an
information channel between a business and its customers, it is interesting and
important to investigate how to make strategic dynamic recommendations leading
to maximum possible revenue. To this end, we propose a novel \model that takes
into account a variety of factors including prices, valuations, saturation
effects, and competition amongst products. Under this model, we study the
problem of finding revenue-maximizing recommendation strategies over a finite
time horizon. We show that this problem is NP-hard, but approximation
guarantees can be obtained for a slightly relaxed version, by establishing an
elegant connection to matroid theory. Given the prohibitively high complexity
of the approximation algorithm, we also design intelligent heuristics for the
original problem. Finally, we conduct extensive experiments on two real and
synthetic datasets and demonstrate the efficiency, scalability, and
effectiveness our algorithms, and that they significantly outperform several
intuitive baselines.Comment: Conference version published in PVLDB 7(14). To be presented in the
VLDB Conference 2015, in Hawaii. This version gives a detailed submodularity
proo
Adaptive Threshold Sampling and Estimation
Sampling is a fundamental problem in both computer science and statistics. A
number of issues arise when designing a method based on sampling. These include
statistical considerations such as constructing a good sampling design and
ensuring there are good, tractable estimators for the quantities of interest as
well as computational considerations such as designing fast algorithms for
streaming data and ensuring the sample fits within memory constraints.
Unfortunately, existing sampling methods are only able to address all of these
issues in limited scenarios.
We develop a framework that can be used to address these issues in a broad
range of scenarios. In particular, it addresses the problem of drawing and
using samples under some memory budget constraint. This problem can be
challenging since the memory budget forces samples to be drawn
non-independently and consequently, makes computation of resulting estimators
difficult.
At the core of the framework is the notion of a data adaptive thresholding
scheme where the threshold effectively allows one to treat the non-independent
sample as if it were drawn independently. We provide sufficient conditions for
a thresholding scheme to allow this and provide ways to build and compose such
schemes.
Furthermore, we provide fast algorithms to efficiently sample under these
thresholding schemes
Managing Well Integrity using Reliability Based Models
Imperial Users onl
natural swine health
guide to keeping your pigs healthy with herbs and other natural product
Increasing the Reliability of Adaptive Quadrature Using Explicit Interpolants
We present two new adaptive quadrature routines. Both routines differ from
previously published algorithms in many aspects, most significantly in how they
represent the integrand, how they treat non-numerical values of the integrand,
how they deal with improper divergent integrals and how they estimate the
integration error. The main focus of these improvements is to increase the
reliability of the algorithms without significantly impacting their efficiency.
Both algorithms are implemented in Matlab and tested using both the "families"
suggested by Lyness and Kaganove and the battery test used by Gander and
Gautschi and Kahaner. They are shown to be more reliable, albeit in some cases
less efficient, than other commonly-used adaptive integrators.Comment: 32 pages, submitted to ACM Transactions on Mathematical Softwar
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Best-first heuristic search for multicore machines
To harness modern multicore processors, it is imperative to develop parallel versions of fundamental algorithms. In this paper, we compare different approaches to parallel best-first search in a shared-memory setting. We present a new method, PBNF, that uses abstraction to partition the state space and to detect duplicate states without requiring frequent locking. PBNF allows speculative expansions when necessary to keep threads busy. We identify and fix potential livelock conditions in our approach, proving its correctness using temporal logic. Our approach is general, allowing it to extend easily to suboptimal and anytime heuristic search. In an empirical comparison on STRIPS planning, grid pathfinding, and sliding tile puzzle problems using 8-core machines, we show that A*, weighted A* and Anytime weighted A* implemented using PBNF yield faster search than improved versions of previous parallel search proposals
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