Social explanation, the statement with the form of "A and B also like the item", is widely used in almost all the major recommender systems in the web and effectively improves the persuasiveness of the recommendation results by convincing more users to try. This paper presents the first algorithm to generate the most persuasive social explanation by recommending the optimal set of users to be put in the explanation. New challenges like modeling persuasiveness of multiple users, different types of users in social network, sparsity of likes, are discussed in depth and solved in our algorithm. The extensive evaluation demonstrates the advantage of our proposed algorithm compared with traditional methods.

Valued decision diagrams (VDDs) are data structures that represent functions mapping variable-value assignments to non-negative real numbers. They prove useful to compile cost functions, utility functions, or probability distributions. While the complexity of some queries (notably optimization) and transformations (notably conditioning) on VDD languages has been known for some time, there remain many significant queries and transformations, such as the various kinds of cuts, marginalizations, and combinations, the complexity of which has not been identified so far. This paper contributes to filling this gap and completing previous results about the time and space efficiency of VDD languages, thus leading to a knowledge compilation map for real-valued functions. Our results show that many tasks that are hard on valued CSPs are actually tractable on VDDs.

Balanced knockout tournaments are one of the most common formats for sports competitions, and are also used in elections and decision-making. We consider the computational problem of finding the optimal draw for a particular player in such a tournament. The problem has generated considerable research within AI in recent years. We prove that checking whether there exists a draw in which a player wins is NP-complete, thereby settling an outstanding open problem. Our main result has a number of interesting implications on related counting and approximation problems. We present a memoization-based algorithm for the problem that is faster than previous approaches. Moreover, we highlight two natural cases that can be solved in polynomial time. All of our results also hold for the more general problem of counting the number of draws in which a given player is the winner.

This paper presents an agent-based model that studies the emergence and evolution of a language system of logical constructions, i.e. a vocabulary and a set of grammatical constructions that allow the expression of logical combinations of categories. The model assumes the agents have a common vocabulary for basic categories, the ability to construct logical combinations of categories using Boolean functions, and some general purpose cognitive capacities for invention, adoption, induction and adaptation. But it does not assume the agents have a vocabulary for Boolean functions nor grammatical constructions for expressing such logical combinations of categories through language. The results of the experiments we have performed show that a language system of logical constructions emerges as a result of a process of self-organisation of the individual agents' interactions when these agents adapt their preferences for vocabulary and grammatical constructions to those they observe are used more often by the rest of the population, and that such a language system is transmitted from one generation to the next.

Newly-discovered materials have been central to recent technological advances. They have contributed significantly to breakthroughs in electronics, renewable energy and green buildings, and overall, have promoted the advancement of global human welfare. Yet, only a fraction of all possible materials have been explored. Accelerating the pace of discovery of materials would foster technological innovations, and would potentially address pressing issues in sustainability, such as energy production or consumption. The bottleneck of this discovery cycle lies, however, in the analysis of the materials data. As materials scientists have recently devised techniques to efficiently create thousands of materials and experimentalists have developed new methods and tools to characterize these materials, the limiting factor has become the data analysis itself. Hence, the goal of this paper is to stimulate the development of new computational techniques for the analysis of materials data, by bringing together the complimentary expertise of materials scientists and computer scientists. In collaboration with two major research laboratories in materials science, we provide the first publicly available dataset for the phase map identification problem. In addition, we provide a parameterized synthetic data generator to assess the quality of proposed approaches, as well as tools for data visualization and solution evaluation.

Online services such as web search and e-commerce applications typically rely on the collection of data about users, including details of their activities on the web. Such personal data is used to maximize revenues via targeting of advertisements and longer engagements of users, and to enhance the quality of service via personalization of content. To date, service providers have largely followed the approach of either requiring or requesting consent for collecting user data. Users may be willing to share private information in return for incentives, enhanced services, or assurances about the nature and extent of the logged data. We introduce stochastic privacy, an approach to privacy centering on the simple concept of providing people with a guarantee that the probability that their personal data will be shared does not exceed a given bound. Such a probability, which we refer to as the privacy risk, can be given by users as a preference or communicated as a policy by a service provider. Service providers can work to personalize and to optimize revenues in accordance with preferences about privacy risk. We present procedures, proofs, and an overall system for maximizing the quality of services, while respecting bounds on privacy risk. We demonstrate the methodology with a case study and evaluation of the procedures applied to web search personalization. We show how we can achieve near-optimal utility of accessing information with provable guarantees on the probability of sharing data.

This paper studies the problem of emotion classification in microblog texts. Given a microblog text which consists of several sentences, we classify its emotion as anger, disgust, fear, happiness, like, sadness or surprise if available. Existing methods can be categorized as lexicon based methods or machine learning based methods. However, due to some intrinsic characteristics of the microblog texts, previous studies using these methods always get unsatisfactory results. This paper introduces a novel approach based on class sequential rules for emotion classification of microblog texts. The approach first obtains two potential emotion labels for each sentence in a microblog text by using an emotion lexicon and a machine learning approach respectively, and regards each microblog text as a data sequence. It then mines class sequential rules from the dataset and finally derives new features from the mined rules for emotion classification of microblog texts. Experimental results on a Chinese benchmark dataset show the superior performance of the proposed approach.

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.

This paper presents a symbolic BDD-based model checking algorithm for an epistemic strategy logic with observational semantics. The logic has been shown to be more expressive than several variants of ATELand therefore the algorithm can also be used for ATEL model checking. We implement the algorithm in a model checker and apply it to an application on train control system. The performance of the algorithm is also reported, with a comparison showing improved results over a previous partially symbolic approach for ATEL model checking.

In this work, we propose a novel node splitting method for regression trees and incorporate it into the regression forest framework. Unlike traditional binary splitting, where the splitting rule is selected from a predefined set of binary splitting rules via trial-and-error, the proposed node splitting method first finds clusters of the training data which at least locally minimize the empirical loss without considering the input space. Then splitting rules which preserve the found clusters as much as possible are determined by casting the problem into a classification problem. Consequently, our new node splitting method enjoys more freedom in choosing the splitting rules, resulting in more efficient tree structures. In addition to the Euclidean target space, we present a variant which can naturally deal with a circular target space by the proper use of circular statistics. We apply the regression forest employing our node splitting to head pose estimation (Euclidean target space) and car direction estimation (circular target space) and demonstrate that the proposed method significantly outperforms state-of-the-art methods (38.5% and 22.5% error reduction respectively).