Collaborative filtering suffers from the problems of data sparsity and cold start, which dramatically degrade recommendation performance. To help resolve these issues, we propose TrustSVD, a trust-based matrix factorization technique. By analyzing the social trust data from four real-world data sets, we conclude that not only the explicit but also the implicit influence of both ratings and trust should be taken into consideration in a recommendation model. Hence, we build on top of a state-of-the-art recommendation algorithm SVD++ which inherently involves the explicit and implicit influence of rated items, by further incorporating both the explicit and implicit influence of trusted users on the prediction of items for an active user. To our knowledge, the work reported is the first to extend SVD++ with social trust information. Experimental results on the four data sets demonstrate that our approach TrustSVD achieves better accuracy than other ten counterparts, and can better handle the concerned issues.

The task of predicting retweet behavior is an important and essential step for various social network applications, such as business intelligence, popular event prediction, and so on. Due to the increasing requirements, in recent years, the task has attracted extensive attentions. In this work, we propose a novel method using non-parametric statistical models to combine structural, textual, and temporal information together to predict retweet behavior. To evaluate the proposed method, we collect a large number of microblogs and their corresponding social networks from a real microblog service. Experimental results on the constructed dataset demonstrate that the proposed method can achieve better performance than state-of-the-art methods. The relative improvement of the the proposed over the method using only textual information is more than 38.5% in terms of F1-Score.

In a search task, a group of agents compete to be the first to find the solution. Each agent has different private information to incorporate into its search. This problem is inspired by settings such as scientific research, Bitcoin hash inversion, or hunting for some buried treasure. A social planner such as a funding agency, mining pool, or pirate captain might like to convince the agents to collaborate, share their information, and greatly reduce the cost of searching. However, this cooperation is in tension with the individuals' competitive desire to each be the first to win the search. The planner's proposal should incentivize truthful information sharing, reduce the total cost of searching, and satisfy fairness properties that preserve the spirit of the competition. We design contract-based mechanisms for information sharing without money. The planner solicits the agents' information and assigns search locations to the agents, who may then search only within their assignments. Truthful reporting of information to the mechanism maximizes an agent's chance to win the search. Epsilon-voluntary participation is satisfied for large search spaces. In order to formalize the planner's goals of fairness and reduced search cost, we propose a simplified, simulated game as a benchmark and quantify fairness and search cost relative to this benchmark scenario. The game is also used to implement our mechanisms. Finally, we extend to the case where coalitions of agents may participate in the mechanism, forming larger coalitions recursively.

We introduce a novel method for the continuous online prediction of particulate matter in the air (more specifically, PM10 and PM2.5) given sparse sensor information. A nonparametric model is developed using Gaussian Processes, which eschews the need for an explicit formulation of internal -- and usually very complex -- dependencies between meteorological variables. Instead, it uses historical data to extrapolate pollutant values both spatially (in areas with no sensor information) and temporally (the near future). Each prediction also contains a respective variance, indicating its uncertainty level and thus allowing a probabilistic treatment of results. A novel training methodology (Structural Cross-Validation) is presented, which preserves the spatio-temporal structure of available data during the hyperparameter optimization process. Tests were conducted using a real-time feed from a sensor network in an area of roughly 50x80 km, alongside comparisons with other techniques for air pollution prediction. The promising results motivated the development of a smartphone applicative and a website, currently in use to increase the efficiency of air quality monitoring and control in the area.

Peak demand for electricity continues to surge around the world. The supply-demand imbalance manifests itself in many forms, from rolling brownouts in California to power cuts in India. It is often suggested that exposing consumers to real-time pricing, will incentivize them to change their usage and mitigate the problem - akin to increasing tolls at peak commute times. We show that risk-averse consumers of electricity react to price fluctuations by scaling back on their total demand, not just their peak demand, leading to the unintended consequence of an overall decrease in production/consumption and reduced economic efficiency. We propose a new scheme that allows homes to move their demands from peak hours in exchange for greater electricity consumption in non-peak hours - akin to how airlines incentivize a passenger to move from an over-booked flight in exchange for, say, two tickets in the future. We present a formal framework for the incentive model that is applicable to different forms of the electricity market. We show that our scheme not only enables increased consumption and consumer social welfare but also allows the distribution company to increase profits. This is achieved by allowing load to be shifted while insulating consumers from real-time price fluctuations. This win-win is important if these methods are to be embraced in practice.

Advertising effectiveness measurement is a fundamental problem in online advertising. Various causal inference methods have been employed to measure the causal effects of ad treatments. However, existing methods mainly focus on linear logistic regression for univariate and binary treatments and are not well suited for complex ad treatments of multi-dimensions, where each dimension could be discrete or continuous. In this paper we propose a novel two-stage causal inference framework for assessing the impact of complex ad treatments. In the first stage, we estimate the propensity parameter via a sparse additive model; in the second stage, a propensity-adjusted regression model is applied for measuring the treatment effect. Our approach is shown to provide an unbiased estimation of the ad effectiveness under regularity conditions. To demonstrate the efficacy of our approach, we apply it to a real online advertising campaign to evaluate the impact of three ad treatments: ad frequency, ad channel, and ad size. We show that the ad frequency usually has a treatment effect cap when ads are showing on mobile device. In addition, the strategies for choosing best ad size are completely different for mobile ads and online ads.

Intelligent item recommendation is a key issue in AI research which enables recommender systems to be more “human-minded” when generating recommendations. However, one of the major features of human — forgetting, has barely been discussed as regards recommender systems. In this paper, we considered people’s forgetting of interest when performing personalized recommendations, and brought forward a personalized framework to integrate interest-forgetting property with Markov model. Multiple implementations of the framework were investigated and compared. The experimental evaluation showed that our methods could significantly improve the accuracy of item recommendation, which verified the importance of considering interest-forgetting in recommendations.

Correct evaluation of Machine Learning based sequencers require large data availability, large scale experiments and consideration of different evaluation measures. Such constraints make the construction of ad-hoc Intelligent Tutoring Systems (ITS) unfeasible and impose early integration in already existing ITS, which possesses a large amount of tasks to be sequenced. However, such systems were not designed to be combined with Machine Learning methods and require several adjustments. As a consequence more than a half of the components based on recommender technology are never evaluated with an online experiment. In this paper we show how we adapted a Matrix Factorization based performance predictor and a score based policy for task sequencing to be integrated in a commercial ITS with over 2000 tasks on 20 topics. We evaluated the experiment under different perspectives in comparison with the ITS sequencer designed by experts over the years. As a result we achieve same post-test results and outperform the current sequencer in the perceived experience questionnaire with almost no curriculum authoring effort. We also showed that the sequencer possess a better user modeling, better adapting to the knowledge acquisition rate of the students.

Emotion affects our understanding of the opinions and sentiments of others. Research has demonstrated that humans are able to recognize emotions in various domains, including speech and music, and that there are potential shared features that shape the emotion in both domains. In this paper, we investigate acoustic and visual features that are relevant to emotion perception in the domains of singing and speaking. We train regression models using two paradigms: (1) within-domain, in which models are trained and tested on the same domain and (2) cross-domain, in which models are trained on one domain and tested on the other domain. This strategy allows us to analyze the similarities and differences underlying the relationship between audio-visual feature expression and emotion perception and how this relationship is affected by domain of expression. We use kernel density estimation to model emotion as a probability distribution over the perception associated with multiple evaluators on the valence-activation space. This allows us to model the variation inherent in the reported perception. Results suggest that activation can be modeled more accurately across domains, compared to valence. Furthermore, visual features capture cross-domain emotion more accurately than acoustic features. The results provide additional evidence for a shared mechanism underlying spoken and sung emotion perception.

This paper proposes mining query subtopics from questions in community question answering (CQA). The subtopics are represented as a number of clusters of questions with keywords summarizing the clusters. The task is unique in that the subtopics from questions can not only facilitate user browsing in CQA search, but also describe aspects of queries from a question-answering perspective. The challenges of the task include how to group semantically similar questions and how to find keywords capable of summarizing the clusters. We formulate the subtopic mining task as a non-negative matrix factorization (NMF) problem and further extend the model of NMF to incorporate question similarity estimated from metadata of CQA into learning. Compared with existing methods, our method can jointly optimize question clustering and keyword extraction and encourage the former task to enhance the latter. Experimental results on large scale real world CQA datasets show that the proposed method significantly outperforms the existing methods in terms of keyword extraction, while achieving a comparable performance to the state-of-the-art methods for question clustering.