Artificial intelligence & Information retrieval

Month: February 2018

Now on arXiv: Optimizing interactive systems with data-driven objectives

Ziming Li, Artem Grotov, Julia Kiseleva, Harrie Oosterhuis and I have just released a new preprint on “optimizing interactive systems with data-driven objectives” on arXiv. Effective optimization is essential for interactive systems to provide a satisfactory user experience. However, it is often challenging to find an objective to optimize for. Generally, such objectives are manually crafted and rarely capture complex user needs accurately. Conversely, we propose an approach that infers the objective directly from observed user interactions. These inferences can be made regardless of prior knowledge and across different types of user behavior. Then we introduce: Interactive System Optimizer (ISO), a novel algorithm that uses these inferred objectives for optimization. Our main contribution is a new general principled approach to optimizing interactive systems using data-driven objectives. We demonstrate the high effectiveness of ISO over several GridWorld simulations. Rush over to arXiv to download the paper.

ICLR 2018 paper on Deep Learning with Logged Bandit Feedback online now

“Deep Learning with Logged Bandit Feedback” by Thorsten Joachims, Adith Swaminathan and Maarten de Rijke, to be published at ICLR 2018, is available online.

In the paper we propose a new output layer for deep neural networks that permits the use of logged contextual bandit feedback for training. Such contextual bandit feedback can be available in huge quantities (e.g., logs of search engines, recommender systems) at little cost, opening up a path for training deep networks on orders of magnitude more data. To this effect, we propose a counterfactual risk minimization approach for training deep networks using an equivariant empirical risk estimator with variance regularization, BanditNet, and show how the resulting objective can be decomposed in a way that allows stochastic gradient descent training. We empirically demonstrate the effectiveness of the method by showing how deep networks – ResNets in particular – can be trained for object recognition without conventionally labeled images.

WWW 2018 paper on Manifold Learning for Rank Aggregation online

“Manifold Learning for Rank Aggregation” by Shangsong Liang, Ilya Markov, Zhaochun Ren, and Maarten de Rijke, which will be published at WWW 2018, is available online now.

In the paper we address the task of fusing ranked lists of documents that are retrieved in response to a query. Past work on this task of rank aggregation often assumes that documents in the lists being fused are independent and that only the documents that are ranked high in many lists are likely to be relevant to a given topic. We propose manifold learning aggregation approaches, ManX and v-ManX, that build on the cluster hypothesis and exploit inter-document similarity information. ManX regularizes document fusion scores, so that documents that appear to be similar within a manifold, receive similar scores, whereas v-ManX first generates virtual adversarial documents and then regularizes the fusion scores of both original and virtual adversarial documents. Since aggregation methods built on the cluster hypothesis are computationally expensive, we adopt an optimization method that uses the top-k documents as anchors and considerably reduces the computational complexity of manifold-based methods, resulting in two efficient aggregation approaches, a-ManX and a-v-ManX. We assess the proposed approaches experimentally and show that they signi cantly outperform the state-of-the-art aggregation approaches, while a-ManX and a-v-ManX run faster than ManX, v-ManX, respectively.

JASIST paper “The birth of collective memories: Analyzing emerging entities in text streams” online

“The birth of collective memories: Analyzing emerging entities in text streams” by David Graus, Daan Odijk and Maarten de Rijke, to be published in the Journal of the Association for Information Science and Technology is online now at this location.

In the paper we study how collective memories are formed online. We do so by tracking entities that emerge in public discourse, that is, in online text streams such as social media and news streams, before they are incorporated into Wikipedia, which, we argue, can be viewed as an online place for collective memory. By tracking how entities emerge in public discourse, that is, the temporal patterns between their first mention in online text streams and subsequent incorporation into collective memory, we gain insights into how the collective remembrance process happens online. Specifically, we analyze nearly 80,000 entities as they emerge in online text streams before they are incorporated into Wikipedia. The online text streams we use for our analysis comprise of social media and news streams, and span over 579 million documents in a time span of 18 months. We discover two main emergence patterns: entities that emerge in a “bursty” fashion, that is, that appear in public discourse without a precedent, blast into activity and transition into collective memory. Other entities display a “delayed” pattern, where they appear in public discourse, experience a period of inactivity, and then resurface before transitioning into our cultural collective memory.

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