Personalized Bundle Recommendation in Online Games
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Personalized Bundle Recommendation in Online Games Qilin Deng Kai Wang Minghao Zhao Fuxi AI Lab, NetEase Games Fuxi AI Lab, NetEase Games Fuxi AI Lab, NetEase Games Hangzhou, Zhejiang, China Hangzhou, Zhejiang, China Hangzhou, Zhejiang, China [email protected] [email protected] [email protected] Zhene Zou Runze Wu Jianrong Tao Fuxi AI Lab, NetEase Games Fuxi AI Lab, NetEase Games Fuxi AI Lab, NetEase Games Hangzhou, Zhejiang, China Hangzhou, Zhejiang, China Hangzhou, Zhejiang, China [email protected] [email protected] [email protected] Changjie Fan Liang Chen Fuxi AI Lab, NetEase Games Sun Yat-Sen University Hangzhou, Zhejiang, China Guangzhou, Guangdong, China [email protected] [email protected] ABSTRACT ACM Reference Format: In business domains, bundling is one of the most important market- Qilin Deng, Kai Wang, Minghao Zhao, Zhene Zou, Runze Wu, Jianrong Tao, ing strategies to conduct product promotions, which is commonly Changjie Fan, and Liang Chen. 2020. Personalized Bundle Recommendation in Online Games. In Proceedings of the 29th ACM International Conference used in online e-commerce and offline retailers. Existing recom- on Information and Knowledge Management (CIKM ’20), October 19–23, 2020, mender systems mostly focus on recommending individual items Virtual Event, Ireland. ACM, New York, NY, USA, 8 pages. https://doi.org/ that users may be interested in. In this paper, we target at a practical 10.1145/3340531.3412734 but less explored recommendation problem named bundle recom- mendation, which aims to offer a combination of items to users. To 1 INTRODUCTION tackle this specific recommendation problem in the context of the Recommender system, which is an effective tool to alleviate the virtual mall in online games, we formalize it as a link prediction information overload, is widely used in modern e-commerce web- problem on a user-item-bundle tripartite graph constructed from sites and online service business, e.g., Amazon, Taobao, Netflix. The the historical interactions, and solve it with a neural network model basic goal of a recommender system is to find potentially interest- that can learn directly on the graph-structure data. Extensive ex- ing items for a user. Existing recommender systems mostly focus periments on three public datasets and one industrial game dataset on recommending individual items to users, such as the extensive demonstrate the effectiveness of the proposed method. Further, the efforts on collaborative filtering that directly models the interaction bundle recommendation model has been deployed in production for between users and items. more than one year in a popular online game developed by Netease Games, and the launch of the model yields more than 60% improve- ment on conversion rate of bundles, and a relative improvement of more than 15% on gross merchandise volume (GMV). Included three items CCS CONCEPTS • Information systems ! Learning to rank; Recommender systems. Discounted price arXiv:2104.05307v1 [cs.IR] 12 Apr 2021 KEYWORDS recommender system; bundle recommendation; neural networks; deep learning; graph neural networks; link prediction Figure 1: The Bundle GUI in the Game Love is Justice. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed In addition to consuming items individually, bundles are also for profit or commercial advantage and that copies bear this notice and the full citation ubiquitous in real-world scenarios. A bundle is a collection of items on the first page. Copyrights for components of this work owned by others than ACM (products or services) consumed as a whole, and it usually reflects must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a the frequent items which are appealing to most customers. In tra- fee. Request permissions from [email protected]. ditional business domains, e.g., supermarkets and offline retailers, CIKM ’20, October 19–23, 2020, Virtual Event, Ireland it often takes bundling as a critical marketing strategy to attract © 2020 Association for Computing Machinery. ACM ISBN 978-1-4503-6859-9/20/10...$15.00 customers and increase sales revenue. Moreover, the combination of https://doi.org/10.1145/3340531.3412734 items are especially ubiquitous on online service platforms, e.g., the music playlist on Spotify, the book lists on GoodReads, the boards 풖ퟏ 풖ퟐ 풖ퟑ of pins on Pinterest, and the game bundles on Steam. In Figure 1, we show the bundle recommendation scenario in the online game Love is Justice1, a popular mobile game developed by Netease Games2, where users could impersonate specific roles and experience beautiful antique scenes and romantic plots, and their purchase and behavior data are extensively tracked by the game server. Here, we give some brief introductions to the Graphical User Interface (GUI) involved in this study. Once the user enters 풃ퟏ 풃ퟐ 풃ퟑ 풃ퟒ the homepage of the game interface (shown in the figure), the system will occasionally pop up a personalized discount bundle to Figure 2: A toy example of a user-bundle bipartite graph attract the user, or the user himself can check the discount bundle with edges representing observed user-bundle interactions. when he wants. In addition, the items included in the bundle can The red arrow lines denote message passing paths. also be purchased separately, although they are not discounted. According to our analysis of purchase statistics, more than 65% of game revenue comes from these discounted bundles, which also graph constructed from historical data, and formalize the bundle shows that it is profitable to increase the conversion rate of these recommendation problem as the link prediction problem in the tri- personalized bundles. partite graph. To alleviate the sparsity of user interactions on bun- In this paper, we address the problem of bundle recommendation dles, we integrate user-items interactions that provide additional in the context of online games, which aims to provide game players information on user interests. To account for the compositional with the pre-defined bundles (combination of items) they are most similarity between bundles, we derive the bundle representation likely to be interested in. Intuitively, this particular recommendation by aggregating the item representations, which provides a natural problem can be solved by treating bundles as "items" and then good generalization ability over different bundles. We also model using traditional recommendation algorithms such as collaborative the correlation between bundle items in the form of learnable trans- filtering. However, such straightforward solutions do not work well formation parameters. Finally, we unify these improvements in to capture user preference over bundles due to the following three our proposed framework named BundleNet, and the multi-layer difficulties: message passing structure can capture the high-order and multi- • Data sparsity and cold-start. Compared with user-item in- path interactions over the user-item-bundle tripartite graph. As teractions, user-bundle interactions are usually more sparse shown in Figure 2, bundle 13 can reach user D1 through the path due to the exponential combination characteristics of bun- 13 ! D2 ! 11 ! D1, and similar for bundle 14. Moreover, com- dles and limited exposure resources. And only if the user is pared with 13, 14 is a more reliable recommendation for D1, since satisfied with the item combination or the discounted price intuitively there is only one path existing between D1 and 13, while is attractive, the user will have a strong willingness to buy two paths connecting D1 to 14. Overall, the main contributions of the bundles rather than individual items, which makes the this paper are summarized as follows: user-bundle interaction data appear more sparse. • We explore the promising yet challenging problem of bundle • Generalization over bundles. Previous item recommenda- recommendation in the context of online games, and provide tion algorithms may rely on item-level content features (e.g., a practical case for the application of deep learning methods category and brand in e-commerce), and user-item collabora- in the industry. tive relationships. However, there is usually no informative • We employ a differentiable message passing framework to bundle-level content features in the bundle recommendation effectively capture the user preferences for bundles, which scenario. This makes it difficult to provide the model’s gen- can incorporate the intermediate role of items between users eralization ability in bundle preference prediction through a and bundles on the user-item-bundle tripartite graph. content-based model. • Extensive offline experiments on both in-game and other real- • Correlation within the bundles. The items within the world datasets are conducted to verify the effectiveness of the bundle are usually highly correlated and compatible. Com- proposed model. Further, we deploy the whole framework pared to typical item recommendation, the bundle recom- online and demonstrate its effective performance through mendation problem is more complex considering that the online A/B Testing. user-bundle preference is a nontrivial combination of user- item preference. And directly modeling the interaction effect 2 PROBLEM DEFINITION