Point-of-interest (POI) recommendation has been well studied in recent years. However, most of the existing methods focus on the recommendation scenarios where users can provide explicit feedback. In most cases, however, the feedback is not explicit, but implicit. For example, we can only get a user’s check-in behaviors from the history of what POIs she/he has visited, but never know how much s...

Learning to rank has become an important research topic in machine learning. While most learning-to-rank methods learn the ranking functions by minimizing loss functions, it is the ranking measures (such as NDCG and MAP) that are used to evaluate the performance of the learned ranking functions. In this work, we reveal the relationship between ranking measures and loss functions in learningto-r...

Personalized search on web is becoming a reality due to popular mobile devices and widely available internet services. However, characteristics of the search on web, such as ranking, unpredictable user behaviors and issues related to automatic updating of user behavior profiles, present challenges in selecting optimal services for personalized search on web. Traditional personalization methods ...

Area Under the ROC Curve (AUC), often used for comparing classifiers, is a widely accepted performance measure for ranking instances. Many researches have studied optimization of AUC, usually via optimizing some approximation of a ranking function. Ranking SVMs are among the better performers but their usage in the literature is typically limited to learning a total ranking from partial ranking...

In information retrieval (IR), the objective of ranking problem is to construct and return a ranked list of relevant documents to the user. The document ranking list is demanded to satisfy user’s information need as much as possible with respect to a user’s query. To evaluate the goodness of the returned document ranking list, performance measures, such as Normalized Discounted Cumulative Gain ...

Preference learning is a challenging problem that involves the prediction of complex structures, such as weak or partial order relations. In the recent literature, the problem appears in many different guises, which we will first put into a coherent framework. This work then focuses on a particular learning scenario called label ranking, where the problem is to learn a mapping from instances to...

Traditionally the use of pseudo relevance feedback (PRF) techniques for query expansion has been demonstrated very effective. Particularly the use of Relevance Models (RM) in the context of the Language Modelling framework has been established as a high-performance approach to beat. In this paper we present an alternative estimation for the RM promoting terms that being present in the relevance...

Items from a database are often ranked based on a combination of multiple criteria. A user may have the flexibility to accept combinations that weigh these criteria differently, within limits. On the other hand, this choice of weights can greatly affect the fairness of the produced ranking. In this paper, we develop a system that helps users choose criterion weights that lead to greater fairnes...

Preference learning is a challenging problem that involves the prediction of complex structures, such as weak or partial order relations, rather than single values. In the recent literature, the problem appears in many different guises, which we will first put into a coherent framework. This work then focuses on a particular learning scenario called label ranking, where the problem is to learn ...

Intelligent well technology has provided facility for real time production control through use of subsurface instrumentation. Early detection of water production allows for a prompt remedial action. Effective water control requires the appropriate performance of individual devices in wells on maintaining the equilibrium between water and oil production over the entire field life. However, there...