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51
Learning diverse rankings with multiarmed bandits
 In Proceedings of the 25 th ICML
, 2008
"... Algorithms for learning to rank Web documents usually assume a document’s relevance is independent of other documents. This leads to learned ranking functions that produce rankings with redundant results. In contrast, user studies have shown that diversity at high ranks is often preferred. We presen ..."
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Cited by 102 (7 self)
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Algorithms for learning to rank Web documents usually assume a document’s relevance is independent of other documents. This leads to learned ranking functions that produce rankings with redundant results. In contrast, user studies have shown that diversity at high ranks is often preferred. We present two online learning algorithms that directly learn a diverse ranking of documents based on users ’ clicking behavior. We show that these algorithms minimize abandonment, or alternatively, maximize the probability that a relevant document is found in the top k positions of a ranking. Moreover, one of our algorithms asymptotically achieves optimal worstcase performance even if users’ interests change. 1.
Efficient diversityaware search
 In Proc. SIGMOD ’11
, 2011
"... Typical approaches of ranking information in response to a user’s query that return the most relevant results ignore important factors contributing to user satisfaction; for instance, the contents of a result document may be redundant given the results already examined. Motivated by emerging applica ..."
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Cited by 27 (1 self)
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Typical approaches of ranking information in response to a user’s query that return the most relevant results ignore important factors contributing to user satisfaction; for instance, the contents of a result document may be redundant given the results already examined. Motivated by emerging applications, in this work we study the problem of DiversityAware Search, the essence of which is ranking search results based on both their relevance, as well as their dissimilarity to other results reported. DiversityAware Search is generally a hard problem, and even tractable instances thereof cannot be efficiently solved by adapting existing approaches. We propose DIVGEN, an efficient algorithm for diversityaware search, which achieves significant performance improvements via novel data access primitives. Although selecting the optimal schedule of data accesses is a hard problem, we devise the first lowoverhead data access prioritization scheme with theoretical quality guarantees, and good performance in practice. A comprehensive evaluation on real and synthetic largescale corpora demonstrates the efficiency and effectiveness of our approach.
DivRank: the Interplay of Prestige and Diversity in Information Networks
, 2010
"... Information networks are widely used to characterize the relationships between data items such as text documents. Many important retrieval and mining tasks rely on ranking the data items based on their centrality or prestige in the network. Beyond prestige, diversity has been recognized as a crucial ..."
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Cited by 27 (3 self)
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Information networks are widely used to characterize the relationships between data items such as text documents. Many important retrieval and mining tasks rely on ranking the data items based on their centrality or prestige in the network. Beyond prestige, diversity has been recognized as a crucial objective in ranking, aiming at providing a nonredundant and high coverage piece of information in the top ranked results. Nevertheless, existing networkbased ranking approaches either disregard the concern of diversity, or handle it with nonoptimized heuristics, usually based on greedy vertex selection. We propose a novel ranking algorithm, DivRank,based on a reinforced random walk in an information network. This model automatically balances the prestige and the diversity of the top ranked vertices in a principled way. DivRank not only has a clear optimization explanation, but also well connects to classical models in mathematics and network science. We evaluate DivRank using empirical experiments on three different networks as well as a text summarization task. DivRank outperforms existing networkbased ranking methods in terms of enhancing diversity in prestige.
Externalities in online advertising
 In International World Wide Web Conference (WWW
, 2008
"... Most models for online advertising assume that an advertiser’s value from winning an ad auction, which depends on the clickthrough rate or conversion rate of the advertisement, is independent of other advertisements served alongside it in the same session. This ignores an important externality effec ..."
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Cited by 27 (2 self)
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Most models for online advertising assume that an advertiser’s value from winning an ad auction, which depends on the clickthrough rate or conversion rate of the advertisement, is independent of other advertisements served alongside it in the same session. This ignores an important externality effect: as the advertising audience has a limited attention span, a highquality ad on a page can detract attention from other ads on the same page. That is, the utility to a winner in such an auction also depends on the set of other winners. In this paper, we introduce the problem of modeling externalities in online advertising, and study the winner determination problem in these models. Our models are based on choice models on the audience side. We show that in the most general case, the winner determination problem is hard even to approximate. However, we give an approximation algorithm for this problem with an approximation factor that is logarithmic in the ratio of the maximum to the minimum bid. Furthermore, we show that there are some interesting special cases, such as the case where the audience preferences are single peaked, where the problem can be solved exactly in polynomial time. For all these algorithms, we prove that the winner determination algorithm can be combined with VCGstyle payments to yield truthful mechanisms.
MaxSum Diversification, Monotone Submodular Functions and Dynamic Updates (Extended Abstract)
, 2012
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Linear Submodular Bandits and their Application to Diversified Retrieval
"... Diversified retrieval and online learning are two core research areas in the design of modern information retrieval systems. In this paper, we propose the linear submodular bandits problem, which is an online learning setting for optimizing a general class of featurerich submodular utility models f ..."
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Cited by 14 (2 self)
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Diversified retrieval and online learning are two core research areas in the design of modern information retrieval systems. In this paper, we propose the linear submodular bandits problem, which is an online learning setting for optimizing a general class of featurerich submodular utility models for diversified retrieval. We present an algorithm, called LSBGREEDY, and prove that it efficiently converges to a nearoptimal model. As a case study, we applied our approach to the setting of personalized news recommendation, where the system must recommend small sets of news articles selected from tens of thousands of available articles each day. In a live user study, we found that LSBGREEDY significantly outperforms existing online learning approaches. 1
Diversified Ranking on Large Graphs: An Optimization Viewpoint
"... Diversified ranking on graphs is a fundamental mining task and has a variety of highimpact applications. There are two important open questions here. The first challenge is the measure how to quantify the goodness of a given topk ranking list that captures both the relevance and the diversity? Th ..."
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Diversified ranking on graphs is a fundamental mining task and has a variety of highimpact applications. There are two important open questions here. The first challenge is the measure how to quantify the goodness of a given topk ranking list that captures both the relevance and the diversity? The second challenge lies in the algorithmic aspect how to find an optimal, or nearoptimal, topk ranking list that maximizes the measure we defined in a scalable way? In this paper, we address these challenges from an optimization point of view. Firstly, we propose a goodness measure for a given topk ranking list. The proposed goodness measure intuitively captures both (a) the relevance between each individual node in the ranking list and the query; and (b) the diversity among different nodes in the ranking list. Moreover, we propose a scalable algorithm (linear wrt the size of the graph) that generates a provably nearoptimal solution. The experimental evaluations on real graphs demonstrate its effectiveness and efficiency.
Scalable diversified ranking on large graphs
 In ICDM
, 2011
"... Abstract—Enhancing diversity in ranking on graphs has been identified as an important retrieval and mining task. Nevertheless, many existing diversified ranking algorithms cannot be scalable to large graphs as they have high time or space complexity. In this paper, we propose a scalable algorithm to ..."
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Cited by 13 (3 self)
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Abstract—Enhancing diversity in ranking on graphs has been identified as an important retrieval and mining task. Nevertheless, many existing diversified ranking algorithms cannot be scalable to large graphs as they have high time or space complexity. In this paper, we propose a scalable algorithm to find the topK diversified ranking list on graphs. The key idea of our algorithm is that we first compute the Pagerank of the nodes of the graph, and then perform a carefully designed vertex selection algorithm to find the topK diversified ranking list. Specifically, we firstly present a new diversified ranking measure, which can capture both relevance and diversity. Secondly, we prove the submodularity of the proposed measure. And then we propose an efficient greedy algorithm with linear time and space complexity with respect to the size of the graph to achieve nearoptimal diversified ranking. Finally, we evaluate the proposed method through extensive experiments on four real networks. The experimental results indicate that the proposed method outperforms existing diversified ranking algorithms both on improving diversity in ranking and the efficiency of the algorithms. I.
GenDeR: A Generic Diversified Ranking Algorithm
"... Diversified ranking is a fundamental task in machine learning. It is broadly applicable in many real world problems, e.g., information retrieval, team assembling, product search, etc. In this paper, we consider a generic setting where we aim to diversify the topk ranking list based on an arbitrary ..."
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Cited by 8 (0 self)
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Diversified ranking is a fundamental task in machine learning. It is broadly applicable in many real world problems, e.g., information retrieval, team assembling, product search, etc. In this paper, we consider a generic setting where we aim to diversify the topk ranking list based on an arbitrary relevance function and an arbitrary similarity function among all the examples. We formulate it as an optimization problem and show that in general it is NPhard. Then, we show that for a large volume of the parameter space, the proposed objective function enjoys the diminishing returns property, which enables us to design a scalable, greedy algorithm to find the (1 − 1/e) nearoptimal solution. Experimental results on real data sets demonstrate the effectiveness of the proposed algorithm. 1