Online recommendation systems are a well-known and common part of Data Science but turning complex in-game interactions into personalised real-time recommendations presents a whole new set of challenges. In this presentation we will detail how we developed and deployed a bi-directional event stream recommendation system in RuneScape, the world’s largest free-to-play massively multiplayer online game. By capturing a feature rich bi-directional relationship between player and content we were able to train different ‘flavours’ of recommendation, each of which is tailored towards a different business target. Through the careful delivery of these different ‘flavours’ we balance engagement, monetisation and enjoyment according to shifting business requirements. With over 200 million registered players, our system scales to deliver personalised in-game content at the right moment. This approach drives engagement with our content and produces a significant uplift in both the financial and ‘enjoyment’ metrics of the game.
With millions of players the multi-terabyte daily event stream generated by RuneScape presents massive value when these ‘raw’ events can be transformed into personalised in-game content recommendations. While developing this system we overcame a number of unique challenges, specifically:
We will answer these questions by detailing two key components, firstly, a big data processing pipeline that produces personalised recommendations and in-game trigger conditions for real-time delivery and, secondly, one of the world’s first commercial deployments of a feature based bi-directional recommendation model 1. Current recommendation approaches are constrained by a User-Item Vs. Item-User design decision, by comparison our in-game content recommendation system is bi-directional and will only recommend content to a player if both content-player and player-content dependencies are satisfied. This ensures that recommended content is both desirable and suitable, avoiding player frustration through the recommendation of desirable but unobtainable items. Furthermore, by characterising each player and item by their interaction frequencies we can link content interaction to subsequent behavioural outcomes, e.g. linking a certain piece of content to subsequent purchase behaviour or increase in game time.
This presentation will be relevant to three groups of people; those who are facing inherently bi-directional recommendation problems (e.g. dating websites), those who wish to extract value from a user’s ‘raw’ event sequences and those who wish to have the flexibility to produce recommendations that optimise multiple (potentially completing) business metrics.
1 Gorla, J 2014, A Bi-directional Unified Model for Information Retrieval. Ph.D. thesis, University College London.
Simon Worgan is the Senior Data Scientist at Jagex Games Studio. In this role he has applied his machine learning expertise to a variety of challenges within the games industry, these include in-game sentiment analysis, behavioural player clustering and predictive modelling. With a Computer Science Ph.D. from the University of Southampton he has over 6 years’ experience in the real world application of Data Science.
Sam is a Data Engineer at Jagex, the largest independent games developer and publisher in the UK. Sam has a wealth of experience on leveraging the Hadoop stack to build business Intelligence and analytics data products.
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