Apptrends support
The extracted features encapsulate complex relationships between users, items, and metadata. The approach relies on representing the data using graphs, and then systematically extracting graph-based features and using them to enrich the original user models. In this study, a novel generic approach for uncovering latent preference patterns from user data is proposed and evaluated. Modeling users for the purpose of identifying their preferences and then personalizing services on the basis of these models is a complex task, primarily due to the need to take into consideration various explicit and implicit signals, missing or uncertain information, contextual aspects, and more.
#Apptrends support download#
With the proposed usage-based measures (retention and trend), we are able to shift sovereignty in app recommendations back to where it really matters: actual usage statistics, in contrast to download count and user ratings which are prone to manipulation by people. We conclude by demonstrating that usage behavior trend information can be used to develop better mobile app recommendations.
Less than 0.4% of the remaining 60% are constantly popular, 1% flop after an initial steep rise, and 7% continuously rise in popularity. In our results, roughly 40% of all apps never gain more than a handful of users. From these, we can distinguish, for instance, trendsetters from copycat apps. We identify typical trends in app popularity and classify applications into archetypes. To capture such effects, we develop a novel app-usage behavior trend measure which provides instantaneous information about the "hotness" of an application. It also reveals, however, that many applications have more complex usage behavior patterns due to seasonality, marketing, or other factors. Our analysis shows that, on average, applications lose 70% of their users in the first week, while very popular applications (top 100) lose only 45%. We study their impact on a large-scale database of app-usage data from a community of 339,842 users and more than 213,667 apps. We conduct the first independent and large-scale study of retention rates and usage behavior trends in the wild. Indeed, analytic companies have suggested that retention rates, i.e., the number of days users continue to interact with an installed app are low. A problem with these measures is that they reflect actual usage at most indirectly. The value of mobile apps is traditionally measured by metrics such as the number of downloads, installations, or user ratings. We conclude by demonstrating that usage behaviour trend information can be used to develop better mobile app recommendations. Less than 0.1% of the remaining 60% are constantly popular (Dominant apps), 1% have a quick drain of usage after an initial steep rise (Expired apps), and 6% continuously rise in popularity (Hot apps). Analysis of our data using this trend filter shows that roughly 40% of all apps never gain more than a handful of users (Marginal apps).
To capture such effects, we develop a novel app-usage trend measure which provides instantaneous information about the popularity of an application. It also reveals, however, that many applications have more complex usage behaviour patterns due to seasonality, marketing, or other factors. Our analysis shows that, on average, applications lose 65% of their users in the first week, while very popular applications (top 100) lose only 35%. We conduct the first independent and large-scale study of retention rates and usage trends on a dataset of app-usage data from a community of 339,842 users and more than 213,667 apps. Indeed, retention rates, i.e., the number of days users continue to interact with an installed app, have been suggested to predict successful app lifecycles. A problem with these measures is that they reflect usage only indirectly.
Popularity of mobile apps is traditionally measured by metrics such as the number of downloads, installations, or user ratings.