The most critical challenge for the recommendation system is to achieve the high prediction quality on the large scale sparse data contributed by the users. In this paper, we present a novel approach to the social recommendation problem, which takes the advantage of the graph Laplacian regularization to capture the underlying social relationship among the users. Differently from the previous approaches, that are based on the conventional gradient descent optimization, we formulate the presented graph Laplacian regularized social recommendation problem into a low-rank semidefinite program, which is able to be efficiently solved by the quasi-Newton algorithm. We have conducted the empirical evaluation on a large scale dataset of high sparsity, the promising experimental results show that our method is very effective and efficient for the social recommendation task.

We study iterative randomized greedy algorithms for generating (elimination) orderings with small induced width and state space size — two parameters known to bound the complexity of inference in graphical models. We propose and implement the Iterative Greedy Variable Ordering (IGVO) algorithm, a new variant within this algorithm class. An empirical evaluation using different ranking functions and conditions of randomness, demonstrates that IGVO finds significantly better orderings than standard greedy ordering implementations when evaluated within an anytime framework. Additional order of magnitude improvements are demonstrated on a multi-core system, thus further expanding the set of solvable graphical models. The experiments also confirm the superiority of the MinFill heuristic within the iterative scheme.

We prove that it is NP-hard for a coalition of two manipulators to compute how to manipulate the Borda voting rule. This resolves one of the last open problems in the computational complexity of manipulating common voting rules. Because of this NP-hardness, we treat computing a manipulation as an approximation problem where we try to minimize the number of manipulators. Based on ideas from bin packing and multiprocessor scheduling, we propose two new approximation methods to compute manipulations of the Borda rule. Experiments show that these methods significantly outperform the previous best known approximation method. We are able to find optimal manipulations in almost all the randomly generated elections tested. Our results suggest that, whilst computing a manipulation of the Borda rule by a coalition is NP-hard, computational complexity may provide only a weak barrier against manipulation in practice.

Bagging is a simple yet effective design which combines multiple single learners to form an ensemble for prediction. Despite its popular usage in many real-world applications, existing research is mainly concerned with studying unstable learners as the key to ensure the performance gain of a bagging predictor, with many key factors remaining unclear. For example, it is not clear when a bagging predictor can outperform a single learner and what is the expected performance gain when different learning algorithms were used to form a bagging predictor. In this paper, we carry out comprehensive empirical studies to evaluate bagging predictors by using 12 different learning algorithms and 48 benchmark data-sets. Our analysis uses robustness and stability decompositions to characterize different learning algorithms, through which we rank all learning algorithms and comparatively study their bagging predictors to draw conclusions. Our studies assert that both stability and robustness are key requirements to ensure the high performance for building a bagging predictor. In addition, our studies demonstrated that bagging is statistically superior to most single base learners, except for KNN and Naïve Bayes (NB). Multi-layer perception (MLP), Naïve Bayes Trees (NBTree), and PART are the learning algorithms with the best bagging performance.

This work focuses on developing domain-independent heuristics for probabilistic planning problems characterized by full observability and non-deterministic effects of actions that are expressed by probability distributions. The approach is to first search for a high probability deterministic plan using a classical planner. A novel probabilistic plan graph heuristic is used to guide the search towards high probability plans. The resulting plans can be used in a system that handles unexpected outcomes by runtime replanning. The plans can also be incrementally augmented with contingency branches for the most critical action outcomes. This abstract will describe the steps that we have taken in completing the above work and the obtained results.

In this paper, we present the exact online updating formulae for the generalized inverse of centered matrices. The computational cost is O ( mn ) for matrices of size m × n . Experimental results validate the proposed method’s accuracy and efficiency.  

Creating accurate computational models of human decision making is a vital step towards the realization of socially intelligent systems capable of both predicting and simulating human behavior. In modeling human decision making, a key factor is the psychological phenomenon known as "framing", in which the preferences of a decision maker change in response to contextual changes in decision problems. Existing approaches treat framing as a one-dimensional contextual influence based on the perception of outcomes as either gains or losses. However, empirical studies have shown that framing effects are much more multifaceted than one-dimensional views of framing suggest. To address this limitation, we propose an integrative approach to modeling framing which combines the psychological principles of cognitive appraisal theories and decision-theoretic notions of utility and probability. We show that this approach allows for both the identification and computation of the salient contextual factors in a decision as well as modeling how they ultimately affect the decision process. Furthermore, we show that our multi-dimensional, appraisal-based approach can account for framing effects identified in the empirical literature which cannot be addressed by one-dimensional theories, thereby promising more accurate models of human behavior.

Much of opinion mining research focuses on product reviews because reviews are opinion-rich and contain little irrelevant information. However, this cannot be said about online discussions and comments. In such postings, the discussions can get highly emotional and heated with many emotional statements, and even personal attacks. As a result, many of the postings and sentences do not express positive or negative opinions about the topic being discussed. To find people’s opinions on a topic and its different aspects, which we call evaluative opinions, those irrelevant sentences should be removed. The goal of this research is thus to identify evaluative opinion sentences. A novel unsupervised approach is proposed to solve the problem, and our experimental results show that it performs well.

Collaborative tagging services provided by various social web sites become popular means to mark web resources for different purposes such as categorization, expression of a preference and so on. However, the tags are of syntactic nature, in a free style and do not reflect semantics, resulting in the problems of redundancy, ambiguity and less semantics. Current tag-based recommender systems mainly take the explicit structural information among users, resources and tags into consideration, while neglecting the important implicit semantic relationships hidden in tagging data. In this study, we propose a Semantic Enhancement Recommendation strategy (SemRec), based on both structural information and semantic information through a unified fusion model. Extensive experiments conducted on two real datasets demonstarte the effectiveness of our approaches.

The personal stories that people write in their Internet weblogs include a substantial amount of information about the causal relationships between everyday events. In this paper we describe our efforts to use millions of these stories for automated commonsense causal reasoning. Casting the commonsense causal reasoning problem as a Choice of Plausible Alternatives, we describe four experiments that compare various statistical and information retrieval approaches to exploit causal information in story corpora. The top performing system in these experiments uses a simple co-occurrence statistic between words in the causal antecedent and consequent, calculated as the Pointwise Mutual Information between words in a corpus of millions of personal stories.