We present a supervised learning to rank algorithm that effectively orders images by exploiting the structure in image sequences. Most often in the supervised learning to rank literature, ranking is approached either by analyzing pairs of images or by optimizing a list-wise surrogate loss function on full sequences. In this work we propose MidRank, which learns from moderately sized sub-sequenc...

We consider the predictive problem of supervised ranking, where the task is to rank sets of candidate items returned in response to queries. Although there exist statistical procedures that come with guarantees of consistency in this setting, these procedures require that individuals provide a complete ranking of all items, which is rarely feasible in practice. Instead, individuals routinely pr...

Glossary Ranking: sort objects according to some order. Query-dependent ranking: objects are assigned with different ranks according to different queries. Proximity ranking: objects are ranked according to proximity or similarity to other objects. Homogeneous information network: networks that contain one type of objects and one type of relationships. Heterogeneous information network: networks...

We describe our participation in the Link-the-Wiki track at INEX 2009. We apply machine learning methods to the anchor-to-best-entry-point task and explore the impact of the following aspects of our approaches: features, learning methods as well as the collection used for training the models. We find that a learning to rank-based approach and a binary classification approach do not differ a lot...

This paper presents a representation theory for permutation-valued functions, which in their general form can also be called listwise ranking functions. Pointwise ranking functions assign a score to each object independently, without taking into account the other objects under consideration; whereas listwise loss functions evaluate the set of scores assigned to all objects as a whole. In many s...

In this paper, we introduce a web-scale general visual search system deployed in Microsoft Bing. The system accommodates tens of billions of images in the index, with thousands of features for each image, and can respond in less than 200 ms. In order to overcome the challenges in relevance, latency, and scalability in such large scale of data, we employ a cascaded learning-to-rank framework bas...

In this paper we describe our effort on TREC Contextual Suggestion Track. We present a recommendation system that built upon ElasticSearch along with a machine learning re-ranking model. We utilize real world users’ opinion as well as other information to build user profiles. With profile information, we then construct customized ElasticSearch queries to search on various fields. After that, a ...

As retrieval systems become more complex, learning to rank approaches are being developed to automatically tune their parameters. Using online learning to rank approaches, retrieval systems can learn directly from implicit feedback, while they are running. In such an online setting, algorithms need to both explore new solutions to obtain feedback for effective learning, and exploit what has alr...

A recently-proposed machine learning approach to reference resolution — the twin-candidate approach — has been shown to be more promising than the traditional single-candidate approach. This paper presents a pronoun interpretation system that extends the twin-candidate framework by (1) equipping it with the ability to identify non-referential pronouns, (2) training different models for handling...