Ranking over sets arise when users choose between groups of items. For example, a group may be of those movies deemed $5$ stars to them, or a customized tour package. It turns out, to model this data type properly, we need to investigate the general combinatorics problem of partitioning a set and ordering the subsets. Here we construct a probabilistic log-linear model over a set of ordered subsets. Inference in this combinatorial space is highly challenging: The space size approaches $(N!/2)6.93145^{N+1}$ as $N$ approaches infinity. We propose a \texttt{split-and-merge} Metropolis-Hastings procedure that can explore the state-space efficiently. For discovering hidden aspects in the data, we enrich the model with latent binary variables so that the posteriors can be efficiently evaluated. Finally, we evaluate the proposed model on large-scale collaborative filtering tasks and demonstrate that it is competitive against state-of-the-art methods.

We introduce kLog, a novel approach to statistical relational learning. Unlike standard approaches, kLog does not represent a probability distribution directly. It is rather a language to perform kernel-based learning on expressive logical and relational representations. kLog allows users to specify learning problems declaratively. It builds on simple but powerful concepts: learning from interpretations, entity/relationship data modeling, logic programming, and deductive databases. Access by the kernel to the rich representation is mediated by a technique we call graphicalization: the relational representation is first transformed into a graph --- in particular, a grounded entity/relationship diagram. Subsequently, a choice of graph kernel defines the feature space. kLog supports mixed numerical and symbolic data, as well as background knowledge in the form of Prolog or Datalog programs as in inductive logic programming systems. The kLog framework can be applied to tackle the same range of tasks that has made statistical relational learning so popular, including classification, regression, multitask learning, and collective classification. We also report about empirical comparisons, showing that kLog can be either more accurate, or much faster at the same level of accuracy, than Tilde and Alchemy. kLog is GPLv3 licensed and is available at http://klog.dinfo.unifi.it along with tutorials.

This paper examines classification of shots in video streams for indexing and semantic analysis. We describe an approach to obtain shot motion by making use of motion estimation algorithms to estimate camera movement. We improve prior work by using the four edge regions of a frame to classify No Motion shots. We analyze a neighborhood of shots and provide a new concept, middle-scale classification. This approach relies on automated labeling of frame transitions in terms of motion across adjacent frames. These annotations form a sequential scene-group that correlates with narrative events in the videos. We introduce six middle-scale classes and the corresponding likely sequence content from three clips of the movie The Lord of the Rings : The Return of the King , demonstrate that the middle-scale classification approach successfully extracts a summary of the salient aspects of the movie. We also show direct comparison with prior work on the full movie Matrix .

Although users' preference is semantically reflected in the free-form review texts, this wealth of information was not fully exploited for learning recommender models. Specifically, almost all existing recommendation algorithms only exploit rating scores in order to find users' preference, but ignore the review texts accompanied with rating information. In this paper, we propose a novel matrix factorization model (called TopicMF) which simultaneously considers the ratings and accompanied review texts. Experimental results on 22 real-world datasets show the superiority of our model over the state-of-the-art models, demonstrating its effectiveness for recommendation tasks.

Maximization of submodular functions has wide applications in artificial intelligence and machine learning. In this paper, we propose a scalable learning algorithm for maximizing an adaptive submodular function. The key structural assumption in our solution is that the state of each item is distributed according to a generalized linear model, which is conditioned on the feature vector of the item. Our objective is to learn the parameters of this model. We analyze the performance of our algorithm, and show that its regret is polylogarithmic in time and linear in the number of features. Finally, we evaluate our solution on two problems, preference elicitation and adaptive face detection, and demonstrate that high-quality policies can be learned sample efficiently.

We introduce Dramatis, a computational model of suspense based on a reformulation of a psychological definition of the suspense phenomenon. In this reformulation, suspense is correlated with the audience’s ability to generate a plan for the protagonist to avoid an impending negative outcome. Dramatis measures the suspense level by generating such a plan and determining its perceived likelihood of success. We report on three evaluations of Dramatis, including a comparison of Dramatis output to the suspense reported by human readers, as well as ablative tests of Dramatis components. In these studies, we found that Dramatis output corresponded to the suspense ratings given by human readers for stories in three separate domains.

Recent literature has demonstrated the difficulty of classifying between composers who write in extremely similar styles (homogeneous style). Additionally, machine learning studies in this field have been exclusively of technical import with little musicological interpretability or significance. We present a supervised machine learning system which addresses the difficulty of differentiating between stylistically homogeneous composers using foundational elements of music, their complexity and interaction. Our work expands on previous style classification studies by developing more complex features as well as introducing a new class of musical features which focus on local irregularities within musical scores. We demonstrate the discriminative power of the system as applied to Haydn and Mozart's string quartets. Our results yield interpretable musicological conclusions about Haydn's and Mozart's stylistic differences while distinguishing between the composers with higher accuracy than previous studies in this domain.

Temporal online content becomes the zeitgeist to reflect our interests and changes. Active users are essential participants and promoters behind it. Temporal dynamics becomes a viable way to investigate users. However, most current work only use global temporal trend and fail to distinguish such fine-grained patterns across groups. Different users have diverse interest and exhibit distinct behaviors, and temporal dynamics tend to be different. This paper proposes GrosToT (Group Specific Topics-over-Time), a unified probabilistic model to infer latent user groups and temporal topics at the same time. It models group-specific temporal topic variation from social content. By leveraging the comprehensive group-specific temporal patterns, GrosToT significantly outperforms state-of-the-art dynamics modeling methods. Our proposed approach shows advantage not only in temporal dynamics but also group content modeling. The dynamics over different groups vary, reflecting the groups' intention. GrosToT uncovers the interplay between group interest and temporal dynamics. Specifically, groups' attention to their medium-interested topics are event-driven, showing rich bursts; while its engagement in group's dominating topics are interest-driven, remaining stable over time.

We address the cold start problem in recommendation systems assuming no contextual information is available neither about users, nor items. We consider the case in which we only have access to a set of ratings of items by users. Most of the existing works consider a batch setting, and use cross-validation to tune parameters. The classical method consists in minimizing the root mean square error over a training subset of the ratings which provides a factorization of the matrix of ratings, interpreted as a latent representation of items and users. Our contribution in this paper is 5-fold. First, we explicit the issues raised by this kind of batch setting for users or items with very few ratings. Then, we propose an online setting closer to the actual use of recommender systems; this setting is inspired by the bandit framework. The proposed methodology can be used to turn any recommender system dataset (such as Netflix, MovieLens,...) into a sequential dataset. Then, we explicit a strong and insightful link between contextual bandit algorithms and matrix factorization; this leads us to a new algorithm that tackles the exploration/exploitation dilemma associated to the cold start problem in a strikingly new perspective. Finally, experimental evidence confirm that our algorithm is effective in dealing with the cold start problem on publicly available datasets. Overall, the goal of this paper is to bridge the gap between recommender systems based on matrix factorizations and those based on contextual bandits.

The workshop was accompanied by an evening Games are unique in that their components event, DAGGER, which drew together local game developers (from the rules and goals of the game to the appearance and academic research projects. Acting both of avatars and their dialogue) must encompass as an exhibition and as an informal gathering, the both functional and aesthetic prerequisites. Artificial DAGGER event allowed attendees to interact directly intelligence usually focuses on the functional quality with a wide variety of game types and technologies, of such game components, for example, ensuring as well as with their developers. As events such that an avatar can traverse a level in minimal time or as DAGGER help bridge the gap between theoretical that AI can win over any human in a strategy game. The papers avatar, or level would appeal to a particular player. of the workshop were published as AAAI Technical The Workshop on AI and Game Aesthetics provided Report WS-13-19.