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SmartAds: Bringing Contextual Ads to Mobile Apps
"... A recent study showed that while US consumers spent 30% more time on mobile apps than on traditional web, advertisers spent 1600 % less money on mobile ads. One key reason is that unlike most web ad providers, today’s mobile ads are not contextual—they do not take into account the content of the pag ..."
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A recent study showed that while US consumers spent 30% more time on mobile apps than on traditional web, advertisers spent 1600 % less money on mobile ads. One key reason is that unlike most web ad providers, today’s mobile ads are not contextual—they do not take into account the content of the page they are displayed on. Thus, most mobile ads are irrelevant to what the user is interested in. For example, it is not uncommon to see gambling ads being displayed in a Bible app. This irrelevance results in low clickthrough rates, and hence advertisers shy away from the mobile platform. Using data from top 1200 apps in Windows Phone marketplace, and a one-week trace of ad keywords from Microsoft’s ad network, we show that content displayed by mobile apps is a potential goldmine of keywords that advertisers are interested in. However, unlike web pages, which can be crawled and indexed offline for contextual advertising, content shown on mobile apps is often either generated dynamically, or is embedded in the apps themselves; and hence cannot be crawled. The only solution is to scrape the content at runtime, extract keywords and fetch contextually relevant ads. The challenge is to do this without excessive overhead and without violating user privacy. In this paper, we describe a system called SmartAds to address this challenge. We have built a prototype of SmartAds for Windows Phone apps. In a large user study with over 5000 ad impressions, we found that SmartAds nearly doubles the relevance score, while consuming minimal additional resources and preserving user privacy.
Truth in Advertising: The Hidden Cost of Mobile Ads for Software Developers
"... Abstract—The “free app ” distribution model has been ex-tremely popular with end users and developers. Developers use mobile ads to generate revenue and cover the cost of developing these free apps. Although the apps are ostensibly free, they in fact do come with hidden costs. Our study of 21 real w ..."
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Abstract—The “free app ” distribution model has been ex-tremely popular with end users and developers. Developers use mobile ads to generate revenue and cover the cost of developing these free apps. Although the apps are ostensibly free, they in fact do come with hidden costs. Our study of 21 real world Android apps shows that the use of ads leads to mobile apps that consume significantly more network data, have increased energy consumption, and require repeated changes to ad related code. We also found that complaints about these hidden costs are significant and can impact the ratings given to an app. Our results provide actionable information and guidance to software developers in weighing the tradeoffs of incorporating ads into their mobile apps. Index Terms—Mobile advertisements, mobile devices I.
How Much Energy Can We Save From Prefetching Ads?: Energy Drain Analysis of Top 100 Apps
- In ACM HotPower
, 2013
"... Recently, there has been a surge of interests on devel-oping techniques and architectures for prefetching ads to potentially reduce the smartphone energy drain by 3G/4G radios from fetching ads. Despite the develop-ment of prefetching techniques, it remains unclear (1) how much smartphone energy do ..."
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Recently, there has been a surge of interests on devel-oping techniques and architectures for prefetching ads to potentially reduce the smartphone energy drain by 3G/4G radios from fetching ads. Despite the develop-ment of prefetching techniques, it remains unclear (1) how much smartphone energy do ads consume in popu-lar apps in dominant app markets, and (2) out of which, what portion can we realistically save from prefetching? We present a measurement study of the energy drain of top 100 free apps in Google Play, totaling more than 2.2 B downloads, to re-examine the above two motiva-tional questions for ads energy research. We found the upper bound energy savings from prefetching ads is low: out of the top 100 apps, only 57 apps display ads, which incur on average 3.2 % total energy on ads 3G tails. We further show the already-low upper bound ads energy saving is hard to achieve by ads prefetching as different apps exhibit very different ads behavior. 1.
MAdScope: Characterizing Mobile In-App Targeted Ads
- Proceedings of ACM Mobisys
, 2015
"... ABSTRACT Advertising is the primary source of revenue for many mobile apps. One important goal of the ad delivery process is targeting users, based on criteria like users' geolocation, context, demographics, long-term behavior, etc. In this paper we report an in-depth study that broadly charac ..."
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ABSTRACT Advertising is the primary source of revenue for many mobile apps. One important goal of the ad delivery process is targeting users, based on criteria like users' geolocation, context, demographics, long-term behavior, etc. In this paper we report an in-depth study that broadly characterizes what targeting information mobile apps send to ad networks and how effectively, if at all, ad networks utilize the information for targeting users. Our study is based on a novel tool, called MAdScope, that can (1) quickly harvest ads from a large collection of apps, (2) systematically probe an ad network to characterize its targeting mechanism, and (3) emulate user profiles of specific preferences and interests to study behavioral targeting. Our analysis of 500K ad requests from 150K Android apps and 101 ad networks indicates that apps do not yet exploit the full potential of targeting: even though ad controls provide APIs to send a lot of information to ad networks, much key targeting information is optional and is often not provided by app developers. We also use MAdScope to systematically probe top 10 in-app ad networks to harvest over 1 million ads and find that while targeting is used by many of the top networks, there remain many instances where targeting information or behavioral profile does not have a statistically significant impact on how ads are chosen. We also contrast our findings with a recent study of targeted in-browser ads.
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"... Advertisements are the de-facto currency of the Internet with many popular applications (e.g. Angry Birds) and online services (e.g., YouTube) relying on advertisement generated revenue. However, the current economic models and mechanisms for mobile advertising are fundamentally not sustainable and ..."
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Advertisements are the de-facto currency of the Internet with many popular applications (e.g. Angry Birds) and online services (e.g., YouTube) relying on advertisement generated revenue. However, the current economic models and mechanisms for mobile advertising are fundamentally not sustainable and far from ideal. In particular, as we show, applications which use mobile advertising are capable of using significant amounts of a mobile users ’ critical resources without being controlled or held accountable. This paper seeks to redress this situation by enabling advertisement supported applications to become significantly more “userfriendly”. To this end, we present the design and implementation of CAMEO, a new framework for mobile advertising that 1) employs intelligent and proactive retrieval of advertisements, using context prediction, to significantly reduce the bandwidth and energy overheads of advertising, and 2) provides a negotiation protocol and framework that empowers applications to subsidize their data traffic costs by “bartering” their advertisement rights for access bandwidth from mobile ISPs. Our evaluation, that uses real mobile advertising data collected from around the globe, demonstrates that CAMEO effectively reduces the resource consumption caused by mobile advertising.
What’s the Gist? Privacy-Preserving Aggregation of User Profiles
"... Abstract. Online service providers gather increasingly large amounts of personal data into user profiles and mon-etize them with advertisers and data brokers. Users have little control of what information is processed and face an all-or-nothing decision between receiving free services or refusing to ..."
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Abstract. Online service providers gather increasingly large amounts of personal data into user profiles and mon-etize them with advertisers and data brokers. Users have little control of what information is processed and face an all-or-nothing decision between receiving free services or refusing to be profiled. This paper explores an alternative approach where users only disclose an aggregate model – the “gist ” – of their data. The goal is to preserve data utility and simultaneously provide user privacy. We show that this approach is practical and can be realized by let-ting users contribute encrypted and differentially-private data to an aggregator. The aggregator combines encrypted contributions and can only extract an aggregate model of the underlying data. In order to dynamically assess the value of data aggregates, we use an information-theoretic measure to compute the amount of “valuable ” information provided to advertisers and data brokers. We evaluate our framework on an anonymous dataset of 100,000 U.S. users obtained from the U.S. Census Bureau and show that (i) it provides accurate aggregates with as little as 100 users, (ii) it generates revenue for both users and data brokers, and (iii) its overhead is appreciably low. 1
Mobile AD(D) Estimating Mobile App Session Times for Better Ads
"... While mobile advertisement is the dominant source of revenue for mobile apps, the usage patterns of mobile users, and thus their engagement and exposure times, may be in conflict with the effectiveness of current ads. User engagement with apps can range from a few seconds to several minutes, dependi ..."
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While mobile advertisement is the dominant source of revenue for mobile apps, the usage patterns of mobile users, and thus their engagement and exposure times, may be in conflict with the effectiveness of current ads. User engagement with apps can range from a few seconds to several minutes, depending on a number of factors such as users ’ locations, concurrent activities and goals. Despite the wide-range of engagement times, the current format of ad auctions dictates that ads are priced, sold and configured prior to actual viewing, regardless of the actual ad exposure time. We argue that the wealth of easy-to-gather contextual informa-tion on mobile devices is sufficient to allow advertisers to make better choices by effectively predicting exposure time. We analyze mobile device usage patterns with a detailed two-week long user study of 37 users in the US and South Korea. After characterizing application session times, we use factor analysis to derive a simple predictive model and show it is able to offer improved accuracy compared to mean session time over 90 % of the time. We make the case for including predicted ad exposure duration in the price of mobile advertisements and posit that such information could significantly impact the effectiveness of mobile ads by giving publishers the ability to tune campaigns for engagement length, and enable a more efficient market for ad impressions while lowering network utilization and device power consumption.
PocketTrend: Timely Identification and Delivery of Trending Search Content to Mobile Users
"... Trending search topics cause unpredictable query load spikes that hurt the end-user search experience, particularly the mobile one, by introducing longer delays. To understand how trending search top-ics are formed and evolve over time, we analyze 21 million queries submitted during periods where po ..."
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Trending search topics cause unpredictable query load spikes that hurt the end-user search experience, particularly the mobile one, by introducing longer delays. To understand how trending search top-ics are formed and evolve over time, we analyze 21 million queries submitted during periods where popular events caused search query volume spikes. Based on our findings, we design and evaluate PocketTrend, a system that automatically detects trending topics in real time, identifies the search content associated to the topics, and then intelligently pushes this content to users in a timely manner. In that way, PocketTrend enables a client-side search engine that can instantly answer user queries related to trending events, while at the same time reducing the impact of these trends on the data-center workload. Our results, using real mobile search logs, show that in the presence of a trending event, up to 13–17 % of the overall search traffic can be eliminated from the datacenter, with as many as 19 % of all users benefiting from PocketTrend.
CAMEO: A Middleware for Mobile Advertisement Delivery
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