584 research outputs found
Does Exposure to Shared Solutions Lead to Better Outcomes? An Empirical Investigation in Online Crowdsourcing Contests
Crowdsourcing contests provide an effective way to elicit novel ideas and creative solutions from collective intelligence. A key design feature of crowdsourcing contests is the competition between contest participants to complete a specific task with financial awards to the winner(s). In recent years, some crowdsourcing contest platforms provide options to contest participants for solution sharing during the competition. This study intends to evaluate the influence of exposure to shared solutions on different stakeholders, including the team, and the requester. Our study employs a multiple-level panel data from a large online crowdsourcing platform, Kaggle.com, to examine these effects. For teams, exposure to shared solutions helps new entrant teams to jump-start and help teams to achieve better performance in the subsequent submissions, and the teams’ skill level negatively moderates these positive effects. For requesters, allowing solution sharing has both benefits and costs in terms of improving the best performance of the crowd. We highlight the theoretical implications of the study and provide practical suggestions for crowdsourcing contest platforms to help them decide whether to allow solution sharing during the competition
A Parallel and Efficient Algorithm for Learning to Match
Many tasks in data mining and related fields can be formalized as matching
between objects in two heterogeneous domains, including collaborative
filtering, link prediction, image tagging, and web search. Machine learning
techniques, referred to as learning-to-match in this paper, have been
successfully applied to the problems. Among them, a class of state-of-the-art
methods, named feature-based matrix factorization, formalize the task as an
extension to matrix factorization by incorporating auxiliary features into the
model. Unfortunately, making those algorithms scale to real world problems is
challenging, and simple parallelization strategies fail due to the complex
cross talking patterns between sub-tasks. In this paper, we tackle this
challenge with a novel parallel and efficient algorithm for feature-based
matrix factorization. Our algorithm, based on coordinate descent, can easily
handle hundreds of millions of instances and features on a single machine. The
key recipe of this algorithm is an iterative relaxation of the objective to
facilitate parallel updates of parameters, with guaranteed convergence on
minimizing the original objective function. Experimental results demonstrate
that the proposed method is effective on a wide range of matching problems,
with efficiency significantly improved upon the baselines while accuracy
retained unchanged.Comment: 10 pages, short version was published in ICDM 201
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