Social networks are commonly used for marketing purposes. For example, free samples of a product can be given to a few influential social network users (or "seed nodes"), with the hope that they will convince their friends to buy it. One way to formalize marketers' objective is through influence maximization (or IM), whose goal is to find the best seed nodes to activate under a fixed budget, so that the number of people who get influenced in the end is maximized. Recent solutions to IM rely on the influence probability that a user influences another one. However, this probability information may be unavailable or incomplete.In this paper, we study IM in the absence of complete information on influence probability. We call this problem Online Influence Maximization (OIM) since we learn influence probabilities at the same time we run influence campaigns. To solve OIM, we propose a multiple-trial approach, where (1) some seed nodes are selected based on existing influence information; (2) an influence campaign is started with these seed nodes; and (3) users' feedback is used to update influence information. We adopt the Explore-Exploit strategy, which can select seed nodes using either the current influence probability estimation (exploit), or the confidence bound on the estimation (explore). Any existing IM algorithm can be used in this framework. We also develop an incremental algorithm that can significantly reduce the overhead of handling users' feedback information. Our experiments show that our solution is more effective than traditional IM methods on the partial information.
Abstract-A social tagging system, such as del.icio.us and Flickr, allows users to annotate resources (e.g., web pages and photos) with text descriptions called tags. Tags have proven to be invaluable information for searching, mining, and recommending resources. In practice, however, not all resources receive the same attention from users. As a result, while some highlypopular resources are over-tagged, most of the resources are under-tagged. Incomplete tagging on resources severely affects the effectiveness of all tag-based techniques and applications. We address an interesting question: if users are paid to tag specific resources, how can we allocate incentives to resources in a crowd-sourcing environment so as to maximize the tagging quality of resources? We address this question by observing that the tagging quality of a resource becomes stable after it has been tagged a sufficient number of times. We formalize the concepts of tagging quality (TQ) and tagging stability (TS) in measuring the quality of a resource's tag description. We propose a theoretically optimal algorithm given a fixed "budget" (i.e., the amount of money paid for tagging resources). This solution decides the amount of rewards that should be invested on each resource in order to maximize tagging stability. We further propose a few simple, practical, and efficient incentive allocation strategies. On a dataset from del.icio.us, our best strategy provides resources with a close-to-optimal gain in tagging stability.
In a crowdsourcing system, Human Intelligence Tasks (HITs) (e.g., translating sentences, matching photos, tagging videos with keywords) can be conveniently specified. HITs are made available to a large pool of workers, who are paid upon completing the HITs they have selected. Since workers may have different capabilities, some difficult HITs may not be satisfactorily performed by a single worker. If more workers are employed to perform a HIT, the quality of the HIT's answer could be statistically improved. Given a set of HITs and a fixed "budget", we address the important problem of determining the number of workers (or plurality) of each HIT so that the overall answer quality is optimized. We propose a dynamic programming (DP) algorithm for solving the plurality assignment problem (PAP). We identify two interesting properties, namely, monotonicity and diminishing return, which are satisfied by a HIT if the quality of the HIT's answer increases monotonically at a decreasing rate with its plurality. We show for HITs that satisfy the two properties (e.g., multiple-choice-question HITs), the PAP is approximable. We propose an efficient greedy algorithm for such case. We conduct extensive experiments on synthetic and real datasets to evaluate our algorithms. Our experiments show that our greedy algorithm provides close-to-optimal solutions in practice.
Abstract-In social tagging systems, such as Delicious 1 and Flickr 2 , users are allowed to annotate resources (e.g., Web URLs and images) with textual descriptions called tags. Tags have proven to be invaluable building blocks in algorithms for searching, mining and recommending resources. In practice, however, not all resources receive the same attention from users, and as a result, most tags are added to the few highly-popular resources, while most of the resources receive few tags. Crucially, this incomplete tagging on resources can severely affect the effectiveness of all tagging applications.We present iTag, an incentive-based tagging system, which aims at improving tagging quality of resources, by incentivizing taggers under budget constraints. Our system is built upon traditional crowdsourcing systems such as Amazon Mechanical Turk (MTurk). In our demonstration, we will show how our system allows users to use simple but powerful strategies to significantly improve the tagging quality of resources.
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