6,586 research outputs found

    Context-Aware Hierarchical Online Learning for Performance Maximization in Mobile Crowdsourcing

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    In mobile crowdsourcing (MCS), mobile users accomplish outsourced human intelligence tasks. MCS requires an appropriate task assignment strategy, since different workers may have different performance in terms of acceptance rate and quality. Task assignment is challenging, since a worker's performance (i) may fluctuate, depending on both the worker's current personal context and the task context, (ii) is not known a priori, but has to be learned over time. Moreover, learning context-specific worker performance requires access to context information, which may not be available at a central entity due to communication overhead or privacy concerns. Additionally, evaluating worker performance might require costly quality assessments. In this paper, we propose a context-aware hierarchical online learning algorithm addressing the problem of performance maximization in MCS. In our algorithm, a local controller (LC) in the mobile device of a worker regularly observes the worker's context, her/his decisions to accept or decline tasks and the quality in completing tasks. Based on these observations, the LC regularly estimates the worker's context-specific performance. The mobile crowdsourcing platform (MCSP) then selects workers based on performance estimates received from the LCs. This hierarchical approach enables the LCs to learn context-specific worker performance and it enables the MCSP to select suitable workers. In addition, our algorithm preserves worker context locally, and it keeps the number of required quality assessments low. We prove that our algorithm converges to the optimal task assignment strategy. Moreover, the algorithm outperforms simpler task assignment strategies in experiments based on synthetic and real data.Comment: 18 pages, 10 figure

    Peer Effects and Social Preferences in Voluntary Cooperation

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    Substantial evidence suggests the behavioral relevance of social preferences and also the importance of social influence effects ("peer effects"). Yet, little is known about how peer effects and social preferences are related. In a three-person gift-exchange experiment we find causal evidence for peer effects in voluntary cooperation: agents' efforts are positively related despite the absence of material payoff interdependencies. We confront this result with major theories of social preferences which predict that efforts are unrelated, or negatively related. Some theories allow for positively-related efforts but cannot explain most observations. Conformism, norm following and considerations of social esteem are candidate explanations.social preferences, voluntary cooperation, peer effects, reflection problem, gift exchange, conformism, social norms, social esteem

    Shareholder Wealth Maximization: A Schelling Point

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    (Excerpt) Imagine a reality television game show where two contestants begin the game in two different places in New York City. The object of the game is for the two contestants to find each other, but they do not know anything about each other and they have no way of communicating. If they succeed, both contestants win a prize. If they fail, they get nothing. With no ability to explicitly bargain over the meeting, the parties have to make an educated guess about what the other person is most likely to do. Most people, confronted with this sort of tacit coordination game, will attempt the meeting at a major New York City landmark such as the Empire State Building. Absent any other clues as to the optimal equilibrium meeting point, both parties choose a place that is imaginatively unique and intuitive, expecting that the place will also be unique in the other’s imagination. The Empire State Building stands out not because it is a particularly optimal meeting place, but rather because it is iconic, nearly synonymous with New York City itself. This is called a “focal point,” or “Schelling point,” after Professor Thomas Schelling. There are two important observations that arise from the New York City game: first, that people can coordinate without communication and, second, that value-creating outcomes can be achieved despite multiple equilibria and high transaction costs. As to the former, the fact that many more people than would be expected by chance would likely collect the prize illustrates that coordination without communication is possible

    Estimating Position Bias without Intrusive Interventions

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    Presentation bias is one of the key challenges when learning from implicit feedback in search engines, as it confounds the relevance signal. While it was recently shown how counterfactual learning-to-rank (LTR) approaches \cite{Joachims/etal/17a} can provably overcome presentation bias when observation propensities are known, it remains to show how to effectively estimate these propensities. In this paper, we propose the first method for producing consistent propensity estimates without manual relevance judgments, disruptive interventions, or restrictive relevance modeling assumptions. First, we show how to harvest a specific type of intervention data from historic feedback logs of multiple different ranking functions, and show that this data is sufficient for consistent propensity estimation in the position-based model. Second, we propose a new extremum estimator that makes effective use of this data. In an empirical evaluation, we find that the new estimator provides superior propensity estimates in two real-world systems -- Arxiv Full-text Search and Google Drive Search. Beyond these two points, we find that the method is robust to a wide range of settings in simulation studies

    Contextual Centrality: Going Beyond Network Structures

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    Centrality is a fundamental network property which ranks nodes by their structural importance. However, structural importance may not suffice to predict successful diffusions in a wide range of applications, such as word-of-mouth marketing and political campaigns. In particular, nodes with high structural importance may contribute negatively to the objective of the diffusion. To address this problem, we propose contextual centrality, which integrates structural positions, the diffusion process, and, most importantly, nodal contributions to the objective of the diffusion. We perform an empirical analysis of the adoption of microfinance in Indian villages and weather insurance in Chinese villages. Results show that contextual centrality of the first-informed individuals has higher predictive power towards the eventual adoption outcomes than other standard centrality measures. Interestingly, when the product of diffusion rate pp and the largest eigenvalue λ1\lambda_1 is larger than one and diffusion period is long, contextual centrality linearly scales with eigenvector centrality. This approximation reveals that contextual centrality identifies scenarios where a higher diffusion rate of individuals may negatively influence the cascade payoff. Further simulations on the synthetic and real-world networks show that contextual centrality has the advantage of selecting an individual whose local neighborhood generates a high cascade payoff when pλ1<1p \lambda_1 < 1. Under this condition, stronger homophily leads to higher cascade payoff. Our results suggest that contextual centrality captures more complicated dynamics on networks and has significant implications for applications, such as information diffusion, viral marketing, and political campaigns

    Human Motion Trajectory Prediction: A Survey

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    With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.Comment: Submitted to the International Journal of Robotics Research (IJRR), 37 page

    Evaluation and Optimal Calibration of Purchase Time Recommendation Services

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    Price Comparison Sites enable customers to make better – more informed, less costly – buying decisions through providing price information and offering buying advice in the form of prediction services. While these services differ to some extent, they are comparable regarding their prediction target and usually monitor every arbitrarily small price decrease. We use a large data set of daily minimum prices for 272 smartphones consisting of 198,560 daily price movements from a Price Comparison Site to show that the standard prediction setting is not optimal. A custom evaluation framework allows the maximization of the achievable savings by altering the calibration of the forecasting service to monitor changes that exceed a certain threshold. Additionally, we show that time series features calculated in a calibration period can be used to obtain precise out of sample estimates of the saving optimal forecasting setting
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