We propose Coactive Learning as a model of interaction between a learning system and a human user, where both have the common goal of providing results of maximum utility to the user. Interactions in the Coactive Learning model take the following form: at each step, the system (e.g. search engine) receives a context (e.g. query) and predicts an object (e.g. ranking); the user responds by correcting the system if necessary, providing a slightly improved but not necessarily optimal object as feedback. We argue that such preference feedback can be inferred in large quantity from observable user behavior (e.g., clicks in web search), unlike the optimal feedback required in the expert model or the cardinal valuations required for bandit learning. Despite the relaxed requirements for the feedback, we show that it is possible to adapt many existing online learning algorithms to the coactive framework. In particular, we provide algorithms that achieve square root regret in terms of cardinal utility, even though the learning algorithm never observes cardinal utility values directly. We also provide an algorithm with logarithmic regret in the case of strongly convex loss functions. An extensive empirical study demonstrates the applicability of our model and algorithms on a movie recommendation task, as well as ranking for web search.

In order to minimize redundancy and optimize coverage of multiple user interests, search engines and recommender systems aim to diversify their set of results. To date, these diversification mechanisms are largely hand-coded or relied on expensive training data provided by experts. To overcome this problem, we propose an online learning model and algorithms for learning diversified recommendations and retrieval functions from implicit feedback. In our model, the learning algorithm presents a ranking to the user at each step, and uses the set of documents from the presented ranking, which the user reads, as feedback. Even for imperfect and noisy feedback, we show that the algorithms admit theoretical guarantees for maximizing any submodular utility measure under approximately rational user behavior. In addition to the theoretical results, we find that the algorithm learns quickly, accurately, and robustly in empirical evaluations on two datasets.

In many areas of life, we now have almost complete electronic archives reaching back for well over two decades. This includes, for example, the body of research papers in computer science, all news articles written in the US, and most people's personal email. However, we have only rather limited methods for analyzing and understanding these collections. While keyword-based retrieval systems allow efficient access to individual documents in archives, we still lack methods for understanding a corpus as a whole. In this paper, we explore methods that provide a temporal summary of such corpora in terms of landmark documents, authors, and topics. In particular, we explicitly model the temporal nature of influence between documents and re-interpret summarization as a coverage problem over words anchored in time. The resulting models provide monotone sub-modular objectives for computing informative and non-redundant summaries over time, which can be efficiently optimized with greedy algorithms. Our empirical study shows the effectiveness of our approach over several baselines.

For ambiguous queries, conventional retrieval systems are bound by two conflicting goals. On the one hand, they should diversify and strive to present results for as many query intents as possible. On the other hand, they should provide depth for each intent by displaying more than a single result. Since both diversity and depth cannot be achieved simultaneously in the conventional static retrieval model, we propose a new dynamic ranking approach. In particular, our proposed two-level dynamic ranking model allows users to adapt the ranking through interaction, thus overcoming the constraints of presenting a one-size-fits-all static ranking. In this model, a user's interactions with the first-level ranking are used to infer this user's intent, so that second-level rankings can be inserted to provide more results relevant to this intent. Unlike previous dynamic ranking models, we provide an algorithm to efficiently compute dynamic rankings with provable approximation guarantees. We also propose the first principled algorithm for learning dynamic ranking functions from training data. In addition to the theoretical results, we provide empirical evidence demonstrating the gains in retrieval quality over conventional approaches.

We propose Coactive Learning as a model of interaction between a learning system and a human user, where both have the common goal of providing results of maximum utility to the user. At each step, the system (e.g. search engine) receives a context (e.g. query) and predicts an object (e.g. ranking). The user responds by correcting the system if necessary, providing a slightly improvedbut not necessarily optimal -object as feedback. We argue that such feedback can often be inferred from observable user behavior, for example, from clicks in web-search. Evaluating predictions by their cardinal utility to the user, we propose efficient learning algorithms that have O( 1 √ T ) average regret, even though the learning algorithm never observes cardinal utility values as in conventional online learning. We demonstrate the applicability of our model and learning algorithms on a movie recommendation task, as well as ranking for web-search.