In mobile social games, reducing user attrition, i.e. churn, is decisive to increase player retention and rise revenues. Survival analysis focuses on predicting the time of occurrence of a certain event and efficiently deals with the censored data problem, which is in the nature of churn. However, due to the inflexibility of the traditional survival statistical algorithms, the accuracy achieved is often poor. In contrast, novel ensemble learning techniques, increasingly popular in a variety of scientific fields, provide high-class prediction results.
In this talk, we present a survival ensemble model which provides a comprehensive analysis together with an accurate prediction of churn for each player, as a function of time and game level. Firstly, we describe that churn prediction by survival ensembles significantly improves the accuracy and robustness of traditional analyses. Secondly, we discuss these results in the framework of Game Data Science as a Service. The goal of Silicon Studio is to Democratize Game Data Science. Hence, the proposed method is able to make predictions in an operational business environment and easily adapts to different kinds of games, players, and therefore distributions of the data. We focus on a flexible technique that does not need a previous manipulation of the data and that is able to deal efficiently with the temporal dimension of the churn prediction problem.
Churn Prediction in Mobile Social Games: Towards a Complete Assessment Using ...Silicon Studio Corporation
Reducing user attrition, i.e. churn, is a broad challenge faced by several industries. In mobile social games, decreasing churn is decisive to increase player retention and rise revenues. Churn prediction models allow to understand player loyalty and to anticipate when they will stop playing a game. Thanks to these predictions, several initiatives can be taken to retain those players who are more likely to churn. Survival analysis focuses on predicting the time of occurrence of a certain event, churn in our case. Classical methods, like regressions, could be applied only when all players have left the game. The challenge arises for datasets with incomplete churning information for all players, as most of them still connect to the game. This is called a censored data problem and is in the nature of churn. Censoring is commonly dealt with survival analysis techniques, but due to the inflexibility of the survival statistical algorithms, the accuracy achieved is often poor. In contrast, novel ensemble learning techniques, increasingly popular in a variety of scientific fields, provide high-class prediction results. In this work, we develop, for the first time in the social games domain, a survival ensemble model which provides a comprehensive analysis together with an accurate prediction of churn. For each player, we predict the probability of churning as function of time, which permits to distinguish various levels of loyalty profiles. Additionally, we assess the risk factors that explain the predicted player survival times. Our results show that churn prediction by survival ensembles significantly improves the accuracy and robustness of traditional analyses, like Cox regression.
Recipes for Optical Effect System Design - Real-time Rendering of Physically...Silicon Studio Corporation
These slides are a portion of the lecture on "Real-Time Rendering of Physically Based Optical Effects in Theory and Practice" at Siggraph 2015. The whole course is available on the tri-Ace web site. All of the Silicon Studio slides are available from our web site.
Silicon Studio: http://www.siliconstudio.co.jp/rd/presentations/
tri-Ace: http://research.tri-ace.com/s2015.html
このスライドはSIGGRAPH2015のCourse「Real-Time Rendering of Physically Based Optical Effects in Theory and Practice」の講演資料の一部です。Course全体のスライドはトライエースのWebサイトに掲載されています。元のスライドデータは、シリコンスタジオまたはトライエースのWebサイトからダウンロードできます。
シリコンスタジオ:http://www.siliconstudio.co.jp/rd/presentations/
トライエース:http://research.tri-ace.com/s2015.html
Subtle Anamorphic Lens Effects - Real-time Rendering of Physically Based Opt...Silicon Studio Corporation
These slides are a portion of the lecture on "Real-Time Rendering of Physically Based Optical Effects in Theory and Practice" at Siggraph 2015. The whole course is available on the tri-Ace web site. All of the Silicon Studio slides are available from our web site.
Silicon Studio: http://www.siliconstudio.co.jp/rd/presentations/
tri-Ace: http://research.tri-ace.com/s2015.html
このスライドはSIGGRAPH2015のCourse「Real-Time Rendering of Physically Based Optical Effects in Theory and Practice」の講演資料の一部です。Course全体のスライドはトライエースのWebサイトに掲載されています。元のスライドデータは、シリコンスタジオまたはトライエースのWebサイトからダウンロードできます。
シリコンスタジオ:http://www.siliconstudio.co.jp/rd/presentations/
トライエース:http://research.tri-ace.com/s2015.html
Making Your Bokeh Fascinating - Real-time Rendering of Physically Based Opti...Silicon Studio Corporation
These slides are a portion of the lecture on "Real-Time Rendering of Physically Based Optical Effects in Theory and Practice" at Siggraph 2015. The whole course is available on the tri-Ace web site. All of the Silicon Studio slides are available from our web site.
Silicon Studio: http://www.siliconstudio.co.jp/rd/presentations/
tri-Ace: http://research.tri-ace.com/s2015.html
このスライドはSIGGRAPH2015のCourse「Real-Time Rendering of Physically Based Optical Effects in Theory and Practice」の講演資料の一部です。Course全体のスライドはトライエースのWebサイトに掲載されています。元のスライドデータは、シリコンスタジオまたはトライエースのWebサイトからダウンロードできます。
シリコンスタジオ:http://www.siliconstudio.co.jp/rd/presentations/
トライエース:http://research.tri-ace.com/s2015.html
Lenses - Real-time Rendering of Physically Based Optical Effect in Theory an...Silicon Studio Corporation
These slides are a portion of the lecture on "Real-Time Rendering of Physically Based Optical Effects in Theory and Practice" at Siggraph 2015. The whole course is available on the tri-Ace web site. All of the Silicon Studio slides are available from our web site.
Silicon Studio: http://www.siliconstudio.co.jp/rd/presentations/
tri-Ace: http://research.tri-ace.com/s2015.html
このスライドはSIGGRAPH2015のCourse「Real-Time Rendering of Physically Based Optical Effects in Theory and Practice」の講演資料の一部です。Course全体のスライドはトライエースのWebサイトに掲載されています。元のスライドデータは、シリコンスタジオまたはトライエースのWebサイトからダウンロードできます。
シリコンスタジオ:http://www.siliconstudio.co.jp/rd/presentations/
トライエース:http://research.tri-ace.com/s2015.html
GPUと神経回路網の構成を分析することによって、これら3つの趣旨を主張したいです。
現状の神経回路網の構成のままでは、GPU使用率に非効率があります。
ii. リアルタイム描画に応用する場合、そのような非効率は必要ないはずです。
iii. 「ハッシュ分業」アーキテクチャを提案します。このアプローチによって非効率をなくせます。
「ハッシュ分業」とは、タイルごとにその内容をハッシュ化し、神経回路網の重みを選択して読み込むアプローチです。