16,165 research outputs found
Quantum Recommendation Systems
A recommendation system uses the past purchases or ratings of products by
a group of users, in order to provide personalized recommendations to
individual users. The information is modeled as an preference
matrix which is assumed to have a good rank- approximation, for a small
constant .
In this work, we present a quantum algorithm for recommendation systems that
has running time . All known classical
algorithms for recommendation systems that work through reconstructing an
approximation of the preference matrix run in time polynomial in the matrix
dimension. Our algorithm provides good recommendations by sampling efficiently
from an approximation of the preference matrix, without reconstructing the
entire matrix. For this, we design an efficient quantum procedure to project a
given vector onto the row space of a given matrix. This is the first algorithm
for recommendation systems that runs in time polylogarithmic in the dimensions
of the matrix and provides an example of a quantum machine learning algorithm
for a real world application.Comment: 22 page
BoostFM: Boosted Factorization Machines for Top-N Feature-based Recommendation
Feature-based matrix factorization techniques such as Factorization Machines (FM) have been proven to achieve impressive accuracy for the rating prediction task. However, most common recommendation scenarios are formulated as a top-N item ranking problem with implicit feedback (e.g., clicks, purchases)rather than explicit ratings. To address this problem, with both implicit feedback and feature information, we propose a feature-based collaborative boosting recommender called BoostFM, which integrates boosting into factorization models during the process of item ranking. Specifically, BoostFM is an adaptive boosting framework that linearly combines multiple homogeneous component recommenders, which are repeatedly constructed on the basis of the individual FM model by a re-weighting scheme. Two ways are proposed to efficiently train the component recommenders from the perspectives of both pairwise and listwise Learning-to-Rank (L2R). The properties of our proposed method are empirically studied on three real-world datasets. The experimental results show that BoostFM outperforms a number of state-of-the-art approaches for top-N recommendation
Retrospective Higher-Order Markov Processes for User Trails
Users form information trails as they browse the web, checkin with a
geolocation, rate items, or consume media. A common problem is to predict what
a user might do next for the purposes of guidance, recommendation, or
prefetching. First-order and higher-order Markov chains have been widely used
methods to study such sequences of data. First-order Markov chains are easy to
estimate, but lack accuracy when history matters. Higher-order Markov chains,
in contrast, have too many parameters and suffer from overfitting the training
data. Fitting these parameters with regularization and smoothing only offers
mild improvements. In this paper we propose the retrospective higher-order
Markov process (RHOMP) as a low-parameter model for such sequences. This model
is a special case of a higher-order Markov chain where the transitions depend
retrospectively on a single history state instead of an arbitrary combination
of history states. There are two immediate computational advantages: the number
of parameters is linear in the order of the Markov chain and the model can be
fit to large state spaces. Furthermore, by providing a specific structure to
the higher-order chain, RHOMPs improve the model accuracy by efficiently
utilizing history states without risks of overfitting the data. We demonstrate
how to estimate a RHOMP from data and we demonstrate the effectiveness of our
method on various real application datasets spanning geolocation data, review
sequences, and business locations. The RHOMP model uniformly outperforms
higher-order Markov chains, Kneser-Ney regularization, and tensor
factorizations in terms of prediction accuracy
Learning to Customize Network Security Rules
Security is a major concern for organizations who wish to leverage cloud
computing. In order to reduce security vulnerabilities, public cloud providers
offer firewall functionalities. When properly configured, a firewall protects
cloud networks from cyber-attacks. However, proper firewall configuration
requires intimate knowledge of the protected system, high expertise and
on-going maintenance.
As a result, many organizations do not use firewalls effectively, leaving
their cloud resources vulnerable. In this paper, we present a novel supervised
learning method, and prototype, which compute recommendations for firewall
rules. Recommendations are based on sampled network traffic meta-data (NetFlow)
collected from a public cloud provider. Labels are extracted from firewall
configurations deemed to be authored by experts. NetFlow is collected from
network routers, avoiding expensive collection from cloud VMs, as well as
relieving privacy concerns.
The proposed method captures network routines and dependencies between
resources and firewall configuration. The method predicts IPs to be allowed by
the firewall. A grouping algorithm is subsequently used to generate a
manageable number of IP ranges. Each range is a parameter for a firewall rule.
We present results of experiments on real data, showing ROC AUC of 0.92,
compared to 0.58 for an unsupervised baseline. The results prove the hypothesis
that firewall rules can be automatically generated based on router data, and
that an automated method can be effective in blocking a high percentage of
malicious traffic.Comment: 5 pages, 5 figures, one tabl
Top-k Route Search through Submodularity Modeling of Recurrent POI Features
We consider a practical top-k route search problem: given a collection of
points of interest (POIs) with rated features and traveling costs between POIs,
a user wants to find k routes from a source to a destination and limited in a
cost budget, that maximally match her needs on feature preferences. One
challenge is dealing with the personalized diversity requirement where users
have various trade-off between quantity (the number of POIs with a specified
feature) and variety (the coverage of specified features). Another challenge is
the large scale of the POI map and the great many alternative routes to search.
We model the personalized diversity requirement by the whole class of
submodular functions, and present an optimal solution to the top-k route search
problem through indices for retrieving relevant POIs in both feature and route
spaces and various strategies for pruning the search space using user
preferences and constraints. We also present promising heuristic solutions and
evaluate all the solutions on real life data.Comment: 11 pages, 7 figures, 2 table
- …