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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 feature-rich submodular utility models for diversified retrieval. We present an algorithm, called LSBGREEDY, and prove that it efficiently converges to a near-optimal 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

Current research on web search has focused on optimizing and evaluating single queries. However, a significant fraction of user queries are part of more complex tasks [20] which span multiple queries across one or more search sessions [26, 24]. An ideal search engine would not only retrieve relevant results for a user’s particular query but also be able to identify when the user is engaged in a more complex task and aid the user in completing that task [29, 1]. Toward optimizing whole-session or task relevance, we characterize and address the problem of intrinsic diversity (ID) in retrieval [30], a type of complex task that requires multiple interactions with current search engines. Unlike existing work on extrinsic diversity [30] that deals with ambiguity in intent across multiple users, ID queries often have little ambiguity in intent but seek content covering a variety of aspects on a shared theme. In such scenarios, the underlying needs are typically exploratory, comparative, or breadth-oriented in nature. We identify and address three key problems for ID retrieval: identifying authentic examples of ID tasks from post-hoc analysis of behavioral signals in search logs; learning to identify initiator queries that mark the start of an ID search task; and given an initiator query, predicting which content to prefetch and rank.

Abstract We propose an online prediction version of submodular set cover with connections to ranking and repeated active learning. In each round, the learning algorithm chooses a sequence of items. The algorithm then receives a monotone submodular function and suffers loss equal to the cover time of the function: the number of items needed, when items are selected in order of the chosen sequence, to achieve a coverage constraint. We develop an online learning algorithm whose loss converges to approximately that of the best sequence in hindsight. Our proposed algorithm is readily extended to a setting where multiple functions are revealed at each round and to bandit and contextual bandit settings. Problem In an online ranking problem, at each round we choose an ordered list of items and then incur some loss. Problems with this structure include search result ranking, ranking news articles, and ranking advertisements. In search result ranking, each round corresponds to a search query and the items correspond to search results. We consider online ranking problems in which the loss incurred at each round is the number of items in the list needed to achieve some goal. For example, in search result ranking a reasonable loss is the number of results the user needs to view before they find the complete information they need. We are specifically interested in problems where the list of items is a sequence of questions to ask or tests to perform in order to learn. In this case the ranking problem becomes a repeated active learning problem. For example, consider a medical diagnosis problem where at each round we choose a sequence of medical tests to perform on a patient with an unknown illness. The loss is the number of tests we need to perform in order to make a confident diagnosis. We propose an approach to these problems using a new online version of submodular set cover. A set function F (S) defined over a ground set V is called submodular if it satisfies the following diminishing returns property: for every Many natural objectives measuring information, influence, and coverage turn out to be submodular We propose the following online prediction version of submodular set cover, which we simply call online submodular set cover. At each time step t = 1 . . . T we choose a sequence of elements is chosen from a ground set V of size n (we use a superscript for rounds of the online problem and a subscript for other indices). After choosing S t , an adversary reveals a submodular, monotone, normalized function F t , and we suffer loss (F t , S t ) where (F t , S t ) min {n} ∪ {i : 1

Crowdsourcing markets have emerged as a popular platform for matching available workers with tasks to complete. The payment for a particular task is typically set by the task's requester, and may be adjusted based on the quality of the completed work, for example, through the use of "bonus" payments. In this paper, we study the requester's problem of dynamically adjusting quality-contingent payments for tasks. We consider a multi-round version of the well-known principal-agent model, whereby in each round a worker makes a strategic choice of the effort level which is not directly observable by the requester. In particular, our formulation significantly generalizes the budget-free online task pricing problems studied in prior work. We treat this problem as a multi-armed bandit problem, with each "arm" representing a potential contract. To cope with the large (and in fact, infinite) number of arms, we propose a new algorithm, AgnosticZooming, which discretizes the contract space into a finite number of regions, effectively treating each region as a single arm. This discretization is adaptively rened, so that more promising regions of the contract space are eventually discretized more finely. We analyze this algorithm, showing that it achieves regret sublinear in the time horizon and

We study recommendation in scenarios where there’s no prior information about the quality of content in the system. We present an online algorithm that continually optimizes recommendation relevance based on behavior of past users. Our method trades weaker theoretical guarantees in asymptotic performance than the state-ofthe-art for stronger theoretical guarantees in the online setting. We test our algorithm on real-world data collected from previous recommender systems and show that our algorithm learns faster than existing methods and performs equally well in the long-run. 1

Abstract. Queries that users pose to search engines are often ambigu-ous- either because different users express different query intents with the same query terms or because the query is underspecified and it is unclear which aspect of a particular query the user is interested in. In the Web search setting, search result diversification, whose goal is the creation of a search result ranking covering a range of query intents or aspects of a single topic respectively, has been shown in recent years to be an effective strategy to satisfy search engine users. We hypothesize that such a strategy will also be beneficial for search on microblogging platforms. Currently, progress in this direction is limited due to the lack of a microblog-based diversification corpus. In this paper we address this shortcoming and present our work on creating such a corpus. We are able to show that this corpus fulfils a number of diversification criteria as described in the literature. Initial search and retrieval experiments evaluating the benefits of de-duplication in the diversification setting are also reported. 1

Active learning is a machine learning setting where the learning algorithm decides what data is labeled. Submodular functions are a class of set functions for which many optimization problems have efficient exact or approximate algorithms. We examine their connections. • We propose a new class of interactive submodular optimization problems which connect and generalize submodular optimization and active learning over a finite query set. We derive greedy algorithms with approximately optimal worst-case cost. These analyses apply to exact learning, approximate learning, learning in the presence of adversarial noise, and applications that mix learning and covering. • We consider active learning in a batch, transductive setting where the learning algorithm selects a set of examples to be labeled at once. In this setting we derive new error bounds which use symmetric submodular functions for regularization, and we give algorithms which approximately minimize these bounds. • We consider a repeated active learning setting where the learning algorithm solves a sequence of related learning problems. We propose an approach to this problem based on a new online prediction version of submodular set cover. A common

We present a novel multilabel/ranking algorithm working in partial information settings. The algorithm is based on 2nd-order descent methods, and relies on upper-confidence bounds to trade-off exploration and exploitation. We analyze this algorithm in a partial adversarial setting, where covariates can be adversarial, but multilabel probabilities are ruled by (generalized) linear models. We show O(T 1/2 log T) regret bounds, which improve in several ways on the existing results. We test the effectiveness of our upper-confidence scheme by contrasting against full-information baselines on real-world multilabel datasets, often obtaining comparable performance. 1

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