Recency ranking refers to the ranking of web results by accounting for both relevance and freshness. This is particularly important for "recency sensitive" queries such as breaking news queries. In this study, we propose a set of novel click features to improve machine learned recency ranking. Rather than computing simple aggregate click through rates, we derive these features using the temporal click through data and query reformulation chains. One of the features that we use is click buzz that captures the spiking interest of a url for a query. We also propose time weighted click through rates which treat recent observations as being exponentially more important. The promotion of fresh content is typically determined by the query intent which can change dynamically over time. Quite often users query reformulations convey clues about the query's intent. Hence we enrich our click features by following query reformulations which typically benefit the first query in the chain of reformulations. Our experiments show these novel features can improve the NDCG5 of a major online search engine's ranking for "recency sensitive" queries by up to 1.57%. This is one of the very few studies that exploits temporal click through data and query reformulations for recency ranking.

We describe a generic framework for representing and reasoning with annotated Semantic Web data, formalise the annotated language, the corresponding deductive system, and address the query answering problem. We extend previous contributions on RDF annotations by providing a unified reasoning formalism and allowing the seamless combination of different annotation domains. We demonstrate the feasibility of our method by instantiating it on (i) temporal RDF; (ii) fuzzy RDF; (iii) and their combination. A prototype shows that implementing and combining new domains is easy and that RDF stores can easily be extended to our framework.

The AI community has achieved great success in designing high-performance algorithms for hard combinatorial problems, given both considerable domain knowledge and considerable effort by human experts. Two influential methods aim to automate this process: automated algorithm configuration and portfolio-based algorithm selection. The former has the advantage of requiring virtually no domain knowledge, but produces only a single solver; the latter exploits per-instance variation, but requires a set of relatively uncorrelated candidate solvers. Here, we introduce Hydra, a novel technique for combining these two methods, thereby realizing the benefits of both. Hydra automatically builds a set of solvers with complementary strengths by iteratively configuring new algorithms. It is primarily intended for use in problem domains for which an adequate set of candidate solvers does not already exist. Nevertheless, we tested Hydra on a widely studied domain, stochastic local search algorithms for SAT, in order to characterize its performance against a well-established and highly competitive baseline. We found that Hydra consistently achieved major improvements over the best existing individual algorithms, and always at least roughly matched — and indeed often exceeded — the performance of the best portfolios of these algorithms.

The discovery of non-linear causal relationship under additive non-Gaussian noise models has attracted considerable attention recently because of their high flexibility. In this paper, we propose a novel causal inference algorithm called least-squares independence regression (LSIR). LSIR learns the additive noise model through minimization of an estimator of the squared-loss mutual information between inputs and residuals. A notable advantage of LSIR over existing approaches is that tuning parameters such as the kernel width and the regularization parameter can be naturally optimized by cross-validation, allowing us to avoid overfitting in a data-dependent fashion. Through experiments with real-world datasets, we show that LSIR compares favorably with the state-of-the-art causal inference method.

The use of simple auction mechanisms like the GSP in online advertising can lead to significant loss of efficiency and revenue when advertisers have rich preferences — even simple forms of expressiveness like budget constraints can lead to suboptimal outcomes. While the optimal allocation of inventory can provide greater efficiency and revenue, natural formulations of the underlying optimization problems grow exponentially in the number of features of interest, presenting a key practical challenge. To address this problem, we propose a means for automatically partitioning inventory into abstract channels so that the least relevant features are ignored. Our approach, based on LP/MIP column and constraint generation, dramatically reduces the size of the problem, thus rendering optimization computationally feasible at practical scales. Our algorithms allow for principled tradeoffs between tractability and solution quality. Numerical experiments demonstrate the computational practicality of our approach as well as the quality of the resulting abstractions.

Forming teams using heterogeneous agents that perform well together to accomplish a task in a game can be a challenging problem. There can often be an enormous amount of combinations to look through, and having an agent that is really good at a particular task is no guarantee that agent will perform well on a team with members with different abilities. Picking a good team is important, as changing teams is often not allowed midway through a task.

Machine learning and data mining have become aware that using constraints when learning patterns and rules can be very useful. To this end, a large number of special purpose systems and techniques have been developed for solving such constraint-based mining and learning problems. These techniques have, so far, been developed independently of the general purpose tools and principles of constraint programming known within the field of artificial intelligence. This paper shows that off-the-shelf constraint programming techniques can be applied to various pattern mining and rule learning problems (cf. also (De Raedt, Guns, and Nijssen 2008; Nijssen, Guns, and De Raedt 2009)). This does not only lead to methodologies that are more general and flexible, but also provides new insights into the underlying mining problems that allow us to improve the state-of-the-art in data mining. Such a combination of constraint programming and data mining raises a number of interesting new questions and challenges.

Constraint programming can be divided very crudely into modeling and solving. Modeling defines the problem, in terms of variables that can take on different values, subject to restrictions (constraints) on which combinations of variables are allowed. Solving finds values for all the variables that simultaneously satisfy all the constraints. However, the impact of constraint programming has been constrained by a lack of "user-friendliness''. Constraint programming has a major "declarative" aspect, in that a problem model can be handed off for solution to a variety of standard solving methods. These methods are embedded in algorithms, libraries, or specialized constraint programming languages. To fully exploit this declarative opportunity however, we must provide more assistance and automation in the modeling process, as well as in the design of application-specific problem solvers. Automated modelling and solving in constraint programming presents a major challenge for the artificial intelligence community. Artificial intelligence, and in particular machine learning, is a natural field in which to explore opportunities for moving more of the burden of constraint programming from the user to the machine. This paper presents technical challenges in the areas of constraint model acquisition, formulation and reformulation, synthesis of filtering algorithms for global constraints, and automated solving. We also present the metrics by which success and progress can be measured.

This paper presents the Topic-Aspect Model (TAM), a Bayesian mixture model which jointly discovers topics and aspects. We broadly define an aspect of a document as a characteristic that spans the document, such as an underlying theme or perspective. Unlike previous models which cluster words by topic or aspect, our model can generate token assignments in both of these dimensions, rather than assuming words come from only one of two orthogonal models. We present two applications of the model. First, we model a corpus of computational linguistics abstracts, and find that the scientific topics identified in the data tend to include both a computational aspect and a linguistic aspect. For example, the computational aspect of GRAMMAR emphasizes parsing, whereas the linguistic aspect focuses on formal languages. Secondly, we show that the model can capture different viewpoints on a variety of topics in a corpus of editorials about the Israeli-Palestinian conflict. We show both qualitative and quantitative improvements in TAM over two other state-of-the-art topic models.

In distributed work systems, individual users perform work for other users. A significant challenge in these systems is to provide proper incentives for users to contribute as much work as they consume, even when monitoring is not possible. We formalize the problem of designing "incentive-compatible accounting mechanisms" that measure the net contributions of users, despite relying on voluntary reports. We introduce the Drop-Edge Mechanism that removes any incentive for a user to manipulate via misreports about work contributed or consumed. We prove that Drop-Edge provides a good approximation to a user's net contribution, and is accurate in the limit as the number of users grows. We demonstrate very good welfare properties in simulation compared to an existing, manipulable mechanism. In closing, we show the power of sybil attacks in accounting mechanisms and discuss our ongoing work, including a real-world implementation and evaluation of the Drop-Edge Mechanism in a BitTorrent client.