Author name cluster

Darius Braziunas

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7 papers

2 author rows

AAAI Conference 2017 Conference Paper

Low-Rank Linear Cold-Start Recommendation from Social Data

Suvash Sedhain
Aditya Menon
Scott Sanner
Lexing Xie
Darius Braziunas

The cold-start problem involves recommendation of content to new users of a system, for whom there is no historical preference information available. This proves a challenge for collaborative ﬁltering algorithms that inherently rely on such information. Recent work has shown that social metadata, such as users’ friend groups and page likes, can strongly mitigate the problem. However, such approaches either lack an interpretation as optimising some principled objective, involve iterative non-convex optimisation with limited scalability, or require tuning several hyperparameters. In this paper, we ﬁrst show how three popular cold-start models are special cases of a linear content-based model, with implicit constraints on the weights. Leveraging this insight, we propose LoCo, a new model for cold-start recommendation based on three ingredients: (a) linear regression to learn an optimal weighting of social signals for preferences, (b) a low-rank parametrisation of the weights to overcome the high dimensionality common in social data, and (c) scalable learning of such low-rank weights using randomised SVD. Experiments on four realworld datasets show that LoCo yields signiﬁcant improvements over state-of-the-art cold-start recommenders that exploit high-dimensional social network metadata.

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AAAI Conference 2016 Conference Paper

On the Effectiveness of Linear Models for One-Class Collaborative Filtering

Suvash Sedhain
Aditya Menon
Scott Sanner
Darius Braziunas

In many personalised recommendation problems, there are examples of items users prefer or like, but no examples of items they dislike. A state-of-the-art method for such implicit feedback, or one-class collaborative ﬁltering (OC-CF), problems is SLIM, which makes recommendations based on a learned item-item similarity matrix. While SLIM has been shown to perform well on implicit feedback tasks, we argue that it is hindered by two limitations: ﬁrst, it does not produce user-personalised predictions, which hampers recommendation performance; second, it involves solving a constrained optimisation problem, which impedes fast training. In this paper, we propose LRec, a variant of SLIM that overcomes these limitations without sacriﬁcing any of SLIM’s strengths. At its core, LRec employs linear logistic regression; despite this simplicity, LRec consistently and signiﬁcantly outperforms all existing methods on a range of datasets. Our results thus illustrate that the OC-CF problem can be effectively tackled via linear classiﬁcation models.

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UAI Conference 2007 Conference Paper

Minimax regret based elicitation of generalized additive utilities

Darius Braziunas
Craig Boutilier

Abstract We describe the semantic foundations for elicitation of generalized additively independent (GAI) utilities using the minimax regret criterion, and propose several new query types and strategies for this purpose. Computational feasibility is obtained by exploiting the local GAI structure in the model. Our results provide a practical approach for implementing preference-based constrained configuration optimization as well as effective search in multiattribute product databases.

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AAAI Conference 2006 Conference Paper

Preference Elicitation and Generalized Additive Utility

Darius Braziunas

Any automated decision support software must tailor its actions or recommendations to the preferences of different users. Thus it requires some representation of user preferences as well as a means of eliciting or otherwise learning the preferences of the specific user on whose behalf it is acting. While additive preference models offer a compact representation of multiattribute utility functions, and ease of elicitation, they are often overly restrictive. The more flexible generalized additive independence (GAI) model maintains much of the intuitive nature of additive models, but comes at the cost of much more complex elicitation. In this article, we summarize the key contributions of our earlier paper (UAI 2005): (a) the first elaboration of the semantic foundations of GAI models that allows one to engage in preference elicitation using local queries over small subsets of attributes rather than global queries over full outcomes; and (b) specific procedures for Bayesian preference elicitation of the parameters of a GAI model using such local queries.

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UAI Conference 2005 Conference Paper

Local Utility Elicitation in GAI Models

Darius Braziunas
Craig Boutilier

Structured utility models are essential for the effective representation and elicitation of complex multiattribute utility functions. Generalized additive independence (GAI) models provide an attractive structural model of user preferences, offering a balanced tradeoff between simplicity and applicability. While representation and inference with such models is reasonably well understood, elicitation of the parameters of such models has been studied less from a practical perspective. We propose a procedure to elicit GAI model parameters using only "local" utility queries rather than "global" queries over full outcomes. Our local queries take full advantage of GAI structure and provide a sound framework for extending the elicitation procedure to settings where the uncertainty over utility parameters is represented probabilistically. We describe experiments using a myopic value-of-information approach to elicitation in a large GAI model.

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AAAI Conference 2004 Conference Paper

Stochastic Local Search for POMDP Controllers

Darius Braziunas
Craig Boutilier

The search for finite-state controllers for partially observable Markov decision processes (POMDPs) is often based on approaches like gradient ascent, attractive because of their relatively low computational cost. In this paper, we illustrate a basic problem with gradient-based methods applied to POMDPs, where the sequential nature of the decision problem is at issue, and propose a new stochastic local search method as an alternative. The heuristics used in our procedure mimic the sequential reasoning inherent in optimal dynamic programming (DP) approaches. We show that our algorithm consistently finds higher quality controllers than gradient ascent, and is competitive with (and, for some problems, superior to) other state-of-the-art controller and DP-based algorithms on large-scale POMDPs.

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ICRA Conference 2000 Conference Paper

Mobile Robot Navigation using Self-Similar Landmarks

Amy J. Briggs
Daniel Scharstein
Darius Braziunas
Cristian Dima
Peter Wall

We propose a new system for vision-based mobile robot navigation in an unmodeled environment. Simple, unobtrusive artificial landmarks are used as navigation and localization aids. The landmark patterns are designed so that they can be reliably detected in real-time in images taken with the robot's camera over a wide range of viewing configurations. The code for the recognition algorithm is available in the web site.

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