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Sentiment Diversification with Different Biases
"... Prior search result diversification work focuses on achieving topical variety in a ranked list, typically equally across all aspects. In this paper, we diversify with sentiments according to an explicit bias. We want to allow users to switch the result perspective to better grasp the polarity of opi ..."
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Prior search result diversification work focuses on achieving topical variety in a ranked list, typically equally across all aspects. In this paper, we diversify with sentiments according to an explicit bias. We want to allow users to switch the result perspective to better grasp the polarity of opinionated content, such as during a literature review. For this, we first infer the prior sentiment bias inherent in a controversial topic – the ‘Topic Sentiment’. Then, we utilize this information in 3 different ways to diversify results according to various sentiment biases: (1) Equal diversification to achieve a balanced and unbiased representation of all sentiments on the topic; (2) Diversification towards the Topic Sentiment, in which the actual sentiment bias in the topic is mirrored to emphasize the general perception of the topic; (3) Diversification against the Topic Sentiment, in which documents about the ‘minority ’ or outlying sentiment(s) are boosted and those with the popular sentiment are demoted. Since sentiment classification is an essential tool for this task, we experiment by gradually degrading the accuracy of a perfect classifier down to 40%, and show which diversification approaches prove most stable in this setting. The results reveal that the proportionality-based methods and our SCSF model, considering sentiment strength and frequency in the diversified list, yield the highest gains. Further, in case the Topic Sentiment cannot be reliably estimated, we show how performance is affected by equal diversification when actually an emphasis either towards or against the Topic Sentiment is desired: in the former case, an average of 6.48 % is lost across all evaluation measures, whereas in the latter case this is 16.23%, confirming that bias-specific sentiment diversification is crucial.
Copulas for information retrieval.
- In Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval.
, 2013
"... ABSTRACT In many domains of information retrieval, system estimates of document relevance are based on multidimensional quality criteria that have to be accommodated in a unidimensional result ranking. Current solutions to this challenge are often inconsistent with the formal probabilistic framewor ..."
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ABSTRACT In many domains of information retrieval, system estimates of document relevance are based on multidimensional quality criteria that have to be accommodated in a unidimensional result ranking. Current solutions to this challenge are often inconsistent with the formal probabilistic framework in which constituent scores were estimated, or use sophisticated learning methods that make it difficult for humans to understand the origin of the final ranking. To address these issues, we introduce the use of copulas, a powerful statistical framework for modeling complex multi-dimensional dependencies, to information retrieval tasks. We provide a formal background to copulas and demonstrate their effectiveness on standard IR tasks such as combining multidimensional relevance estimates and fusion of results from multiple search engines. We introduce copula-based versions of standard relevance estimators and fusion methods and show that these lead to significant performance improvements on several tasks, as evaluated on large-scale standard corpora, compared to their non-copula counterparts. We also investigate criteria for understanding the likely effect of using copula models in a given retrieval scenario.
DUM: Diversity-weighted utility maximization for recommendations
- CoRR
, 2014
"... The need for diversification of recommendation lists manifests in a number of recommender systems use cases. However, an increase in diversity may undermine the utility of the recommendations, as relevant items in the list may be replaced by more diverse ones. In this work we propose a novel method ..."
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The need for diversification of recommendation lists manifests in a number of recommender systems use cases. However, an increase in diversity may undermine the utility of the recommendations, as relevant items in the list may be replaced by more diverse ones. In this work we propose a novel method for maximizing the utility of the recommended items subject to the diversity of user’s tastes, and show that an optimal solution to this problem can be found greed-ily. We evaluate the proposed method in two online user studies as well as in an offline analysis incorporating a number of evalua-tion metrics. The results of evaluations show the superiority of our method over a number of baselines.
Diversely Enumerating System‐Level Architectures
- Proceedings of EMSOFT 2013, Embedded Systems Week, September 29-October 4, 2013
"... ABSTRACT Embedded systems are highly constrained. System-level constraints, such as task partitioning problems and communication scheduling problems, are common, combinatorial, and fundamentally intractable. Though modern constraint solvers can help to synthesize constrained architectures, the arch ..."
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ABSTRACT Embedded systems are highly constrained. System-level constraints, such as task partitioning problems and communication scheduling problems, are common, combinatorial, and fundamentally intractable. Though modern constraint solvers can help to synthesize constrained architectures, the architect's troubles do not end here: There may be (infinitely) many architectures satisfying system-level constraints. Multiple candidates must be examined and this is often infeasible for large solution spaces. In this paper we describe an improved enumeration scheme, which still reaps the benefits of modern constraint solvers. The idea is to build a diverse enumerator around an unmodified constraint solver. A diverse enumerator uniformly draws equivalence classes of solutions. Such an enumerator is powerful because it allows unbiased enumeration of the space and can be used to make inferences about the space as a whole. This paper presents the theory, practice, and algorithms for diverse enumeration of architectures with system-level constraints.
Optimal Greedy Diversity for Recommendation
"... The need for diversification manifests in various recommendation use cases. In this work, we pro-pose a novel approach to diversifying a list of rec-ommended items, which maximizes the utility of the items subject to the increase in their diversity. From a technical perspective, the problem can be v ..."
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The need for diversification manifests in various recommendation use cases. In this work, we pro-pose a novel approach to diversifying a list of rec-ommended items, which maximizes the utility of the items subject to the increase in their diversity. From a technical perspective, the problem can be viewed as maximization of a modular function on the polytope of a submodular function, which can be solved optimally by a greedy method. We eval-uate our approach in an offline analysis, which in-corporates a number of baselines and metrics, and in two online user studies. In all the experiments, our method outperforms the baseline methods. 1
INTEGRATING NON-TOPICAL ASPECTS INTO INFORMATION RETRIEVAL
, 2014
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