Category-based and Popularity-guided Video Game Recommendation: A Balance-oriented Framework
Authors: 
Xiping Li, Jianghong Ma, Kangzhe Liu, Shanshan Feng, Haijun Zhang, Yutong WangAuthors Info & Claims
Xiping Li, Jianghong Ma, Kangzhe Liu, Shanshan Feng, Haijun Zhang, Yutong WangAuthors Info & ClaimsPages 3734 - 3744
Abstract
In recent years, the video game industry has experienced substantial growth, presenting players with a vast array of game choices. This surge in options has spurred the need for a specialized recommender system tailored for video games. However, current video game recommendation approaches tend to prioritize accuracy over diversity, potentially leading to unvaried game suggestions. In addition, the existing game recommendation methods commonly lack the ability to establish strict connections between games to enhance accuracy. Furthermore, many existing diversity-focused methods fail to leverage crucial item information, such as item category and popularity during neighbor modeling and message propagation. To address these challenges, we introduce a novel framework, called CPGRec, comprising three modules, namely accuracy-driven, diversity-driven, and comprehensive modules. The first module extends the state-of-the-art accuracy-focused game recommendation method by connecting games in a more stringent manner to enhance recommendation accuracy. The second module connects neighbors with diverse categories within the proposed game graph and harnesses the advantages of popular game nodes to amplify the influence of long-tail games within the player-game bipartite graph, thereby enriching recommendation diversity. The third module combines the above two modules and employs a new negative-sample rating score reweighting method to balance accuracy and diversity. Experimental results on the Steam dataset demonstrate the effectiveness of our proposed method in improving game recommendations. The dataset and source codes are anonymously released at: https://github.com/CPGRec2024/CPGRec.git.
Supplemental Material
MP4 File
video presentation
- Download
- 1066.45 MB
MP4 File
Supplemental video
- Download
- 10.51 MB
References
[1]
Chris Anderson, Christopher Nissley, and Chris Anderson. 2006. The long tail.
[2]
Syed Muhammad Anwar, Talha Shahzad, Zunaira Sattar, Rahma Khan, and Muhammad Majid. 2017. A game recommender system using collaborative filtering (GAMBIT). In 2017 14th International Bhurban Conference on Applied Sciences and Technology (IBCAST). 328--332.
[3]
C. BharathiPriya, Akash Sreenivasu, and Sampath Kumar. 2021. Online Video Game Recommendation System Using Content And Collaborative Filtering Techniques. In 2021 International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA). 1--7.
[4]
Jaime Carbonell and Jade Goldstein. 1998. The use of MMR, diversity-based reranking for reordering documents and producing summaries. In Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval. 335--336.
[5]
Lo"ic Caroux. 2023. Presence in video games: A systematic review and meta-analysis of the effects of game design choices. Applied Ergonomics, Vol. 107 (2023), 103936.
[6]
Peizhe Cheng, Shuaiqiang Wang, Jun Ma, Jiankai Sun, and Hui Xiong. 2017. Learning to recommend accurate and diverse items. In Proceedings of the World Wide Web. 183--192.
[7]
Germán Cheuque, Jose Antonio Guzman Gomez, and Denis Parra. 2019. Recommender Systems for Online Video Game Platforms: the Case of STEAM. 763--771.
[8]
Jiasheng Duan, Peng-Fei Zhang, Ruihong Qiu, and Zi Huang. 2023. Long short-term enhanced memory for sequential recommendation. World Wide Web, Vol. 26, 2 (2023), 561--583.
[9]
Chen Gao, Chao Huang, Donghan Yu, Haohao Fu, Tzh-Heng Lin, Depeng Jin, and Yong Li. 2022. Item Recommendation for Word-of-Mouth Scenario in Social E-Commerce. IEEE Transactions on Knowledge and Data Engineering, Vol. 34, 6 (2022), 2798--2809.
[10]
Chen Gao, Tzu-Heng Lin, Nian Li, Depeng Jin, and Yong Li. 2023. Cross-Platform Item Recommendation for Online Social E-Commerce. IEEE Transactions on Knowledge and Data Engineering, Vol. 35, 2 (2023), 1351--1364.
[11]
Valerio La Gatta, Vincenzo Moscato, Mirko Pennone, Marco Postiglione, and Giancarlo Sperlí. 2023. Music Recommendation via Hypergraph Embedding. IEEE Transactions on Neural Networks and Learning Systems, Vol. 34, 10 (2023), 7887--7899.
[12]
William L. Hamilton, Rex Ying, and Jure Leskovec. 2018. Inductive Representation Learning on Large Graphs. arxiv: 1706.02216 [cs.SI]
[13]
Han Han, Can Wang, Yunwei Zhao, Min Shu, Wenlei Wang, and Yong Min. 2022. SSLE: A framework for evaluating the ?Filter Bubble" effect on the news aggregator and recommenders. World Wide Web, Vol. 25, 3 (2022), 1169--1195.
[14]
Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, and Meng Wang. 2020. Lightgcn: Simplifying and powering graph convolution network for recommendation. In Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval. 639--648.
[15]
Fasiha Ikram, Humera Farooq, et al. 2022. Multimedia Recommendation System for Video Game Based on High-Level Visual Semantic Features. Scientific Programming, Vol. 2022 (2022).
[16]
Nan Jiang, Zihao Hu, Jie Wen, Jiahui Zhao, Weihao Gu, Ziang Tu, Ximeng Liu, Yuanyuan Li, Jianfei Gong, and Fengtao Lin. 2023. NAH: neighbor-aware attention-based heterogeneous relation network model in E-commerce recommendation. World Wide Web (2023), 1--22.
[17]
Sichen Jin, Yijia Zhang, Xingwang Li, and Mingyu Lu. 2023. Heterogeneous Graph Convolutional Network for E-commerce Product Recommendation with Adaptive Denoising Training. IEEE Transactions on Consumer Electronics (2023), 1--1.
[18]
Zafran Khan, Muhammad Ishfaq Hussain, Naima Iltaf, Joonmo Kim, and Moongu Jeon. 2021. Contextual recommender system for E-commerce applications. Applied Soft Computing, Vol. 109 (2021), 107552.
[19]
Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. arxiv: 1609.02907 [cs.LG]
[20]
Silvia Knobloch-Westerwick and Axel Westerwick. 2023. Algorithmic personalization of source cues in the filter bubble: Self-esteem and self-construal impact information exposure. New Media & Society, Vol. 25, 8 (2023), 2095--2117.
[21]
Valerio La Gatta, Vincenzo Moscato, Mirko Pennone, Marco Postiglione, and Giancarlo Sperl'i. 2022. Music recommendation via hypergraph embedding. IEEE Transactions on Neural Networks and Learning Systems (2022).
[22]
Yile Liang, Tieyun Qian, Qing Li, and Hongzhi Yin. 2021. Enhancing domain-level and user-level adaptivity in diversified recommendation. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 747--756.
[23]
Wenpeng Lu, Rongyao Wang, Shoujin Wang, Xueping Peng, Hao Wu, and Qian Zhang. 2022. Aspect-Driven User Preference and News Representation Learning for News Recommendation. IEEE Transactions on Intelligent Transportation Systems, Vol. 23, 12 (2022), 25297--25307.
[24]
Lien Michiels, Jens Leysen, Annelien Smets, and Bart Goethals. 2022. What are filter bubbles really? A review of the conceptual and empirical work. In Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization. 274--279.
[25]
Vincenzo Moscato, Antonio Picariello, and Giancarlo Sperlí. 2021. An Emotional Recommender System for Music. IEEE Intelligent Systems, Vol. 36, 5 (2021), 57--68.
[26]
Mark O'Neill, Elham Vaziripour, Justin Wu, and Daniel Zappala. 2016. Condensing steam: Distilling the diversity of gamer behavior. In Proceedings of the 2016 internet measurement conference. 81--95.
[27]
Javier Pérez-Marcos, Lucía Martín-Gómez, Diego M. Jiménez-Bravo, Vivian F. López, and María N. Moreno-García. 2020. Hybrid system for video game recommendation based on implicit ratings and social networks. Journal of Ambient Intelligence and Humanized Computing, Vol. 11, 11 (01 Nov 2020), 4525--4535.
[28]
Laure Prétet, Gaël Richard, Clément Souchier, and Geoffroy Peeters. 2023. Video-to-Music Recommendation Using Temporal Alignment of Segments. IEEE Transactions on Multimedia, Vol. 25 (2023), 2898--2911.
[29]
Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2012. BPR: Bayesian Personalized Ranking from Implicit Feedback. arxiv: 1205.2618 [cs.IR]
[30]
Michail Salampasis, Alkiviadis Katsalis, Theodosios Siomos, Marina Delianidi, Dimitrios Tektonidis, Konstantinos Christantonis, Pantelis Kaplanoglou, Ifigeneia Karaveli, Chrysostomos Bourlis, and Konstantinos Diamantaras. 2023. A Flexible Session-Based Recommender System for e-Commerce. Applied Sciences, Vol. 13, 5 (2023).
[31]
Alexander Semenov, Maciej Rysz, Gaurav Pandey, and Guanglin Xu. 2022. Diversity in news recommendations using contextual bandits. Expert Systems with Applications, Vol. 195 (2022), 116478.
[32]
Heng-Shiou Sheu, Zhixuan Chu, Daiqing Qi, and Sheng Li. 2022. Knowledge-Guided Article Embedding Refinement for Session-Based News Recommendation. IEEE Transactions on Neural Networks and Learning Systems, Vol. 33, 12 (2022), 7921--7927.
[33]
Lei Shi. 2013. Trading-off among accuracy, similarity, diversity, and long-tail: a graph-based recommendation approach. In Proceedings of the 7th ACM Conference on Recommender Systems. 57--64.
[34]
Hao Tang, Guoshuai Zhao, Yujiao He, Yuxia Wu, and Xueming Qian. 2023. Ranking-based contrastive loss for recommendation systems. Knowledge-Based Systems, Vol. 261 (2023), 110180.
[35]
Douglas Zanatta Ulian, João Luiz Becker, Carla Bonato Marcolin, and Eusebio Scornavacca. 2021. Exploring the effects of different Clustering Methods on a News Recommender System. Expert Systems with Applications, Vol. 183 (2021), 115341.
[36]
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems, Vol. 30 (2017).
[37]
Dongjing Wang, Xin Zhang, Yao Wan, Dongjin Yu, Guandong Xu, and Shuiguang Deng. 2022. Modeling Sequential Listening Behaviors With Attentive Temporal Point Process for Next and Next New Music Recommendation. IEEE Transactions on Multimedia, Vol. 24 (2022), 4170--4182.
[38]
Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, and Tat-Seng Chua. 2019. Neural graph collaborative filtering. In Proceedings of the ACM SIGIR. 165--174.
[39]
Youquan Wang, Zhiwen Dai, Jie Cao, Jia Wu, Haicheng Tao, and Guixiang Zhu. 2023. Intra-and inter-association attention network-enhanced policy learning for social group recommendation. World Wide Web, Vol. 26, 1 (2023), 71--94.
[40]
Michael Wolfowicz, David Weisburd, and Badi Hasisi. 2023. Examining the interactive effects of the filter bubble and the echo chamber on radicalization. Journal of Experimental Criminology, Vol. 19, 1 (2023), 119--141.
[41]
Chuhan Wu, Fangzhao Wu, Yongfeng Huang, and Xing Xie. 2023. Personalized news recommendation: Methods and challenges. ACM Transactions on Information Systems, Vol. 41, 1 (2023), 1--50.
[42]
Liangwei Yang, Zhiwei Liu, Yu Wang, Chen Wang, Ziwei Fan, and Philip S. Yu. 2022. Large-scale Personalized Video Game Recommendation via Social-aware Contextualized Graph Neural Network. In Proceedings of the Web Conference.
[43]
Liangwei Yang, Shengjie Wang, Yunzhe Tao, Jiankai Sun, Xiaolong Liu, Philip S. Yu, and Taiqing Wang. 2023. DGRec: Graph Neural Network for Recommendation with Diversified Embedding Generation. In Proceedings of the WSDM.
[44]
Rui Ye, Yuqing Hou, Te Lei, Yunxing Zhang, Qing Zhang, Jiale Guo, Huaiwen Wu, and Hengliang Luo. 2021. Dynamic Graph Construction for Improving Diversity of Recommendation. In Proceedings of the 15th ACM Conference on Recommender Systems. 651--655.
[45]
Jing Yi, Yaochen Zhu, Jiayi Xie, and Zhenzhong Chen. 2023. Cross-Modal Variational Auto-Encoder for Content-Based Micro-Video Background Music Recommendation. IEEE Transactions on Multimedia, Vol. 25 (2023), 515--528.
[46]
Qing Yin, Hui Fang, Zhu Sun, and Yew-Soon Ong. 2023. Understanding Diversity in Session-based Recommendation. ACM Transactions on Information Systems, Vol. 42, 1 (aug 2023), 1--34.
[47]
Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L Hamilton, and Jure Leskovec. 2018. Graph convolutional neural networks for web-scale recommender systems. In Proceedings of the 24th ACM SIGKDD. 974--983.
[48]
Mingwei Zhang, Guiping Wang, Lanlan Ren, Jianxin Li, Ke Deng, and Bin Zhang. 2022. METoNR: A meta explanation triplet oriented news recommendation model. Knowledge-Based Systems, Vol. 238 (2022), 107922.
[49]
Yu Zheng, Chen Gao, Liang Chen, Depeng Jin, and Yong Li. 2021. DGCN: Diversified Recommendation with Graph Convolutional Networks. In Proceedings of the Web Conference 2021. ACM.
[50]
Cai-Nicolas Ziegler, Sean M. McNee, Joseph A. Konstan, and Georg Lausen. 2005. Improving Recommendation Lists through Topic Diversification. In Proceddings of the World Wide Web 2005. 22--32.
Index Terms
- Category-based and Popularity-guided Video Game Recommendation: A Balance-oriented Framework
Recommendations
Collaborative Filtering Recommendation Algorithm Based on Category and Penalty
ICCDA '17: Proceedings of the International Conference on Compute and Data AnalysisThe recommended list generated by the recommender system can help users find recommended result that meet their interests and needs when they can not find keywords that accurately describe their requirements. In response to the long tail problem and ...
User Popularity Preference Aware Sequential Recommendation
Computational Science – ICCS 2023AbstractIn recommender systems, users’ preferences for item popularity are diverse and dynamic, which reveals the different items that users prefer. Therefore, identifying user popularity preferences are significant for personalized recommendations. ...
Novel Recommendation Based on Personal Popularity Tendency
ICDM '11: Proceedings of the 2011 IEEE 11th International Conference on Data MiningRecently, novel recommender systems have attracted considerable attention in the research community. Recommending popular items may not always satisfy users. For example, although most users likely prefer popular items, such items are often not very ...
Comments
Information & Contributors
Information
Published In
May 2024
4826 pages
ISBN:9798400701719
DOI:10.1145/3589334
- General Chairs:
- Tat-Seng Chua,
- Chong-Wah Ngo,
- Proceedings Chair:
- Roy Ka-Wei Lee,
- Program Chairs:
- Ravi Kumar,
- Hady W. Lauw
Copyright © 2024 ACM.
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 for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) 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 fee. Request permissions from [email protected].
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 13 May 2024
Check for updates
Author Tags
Qualifiers
- Research-article
Funding Sources
Conference
WWW '24
Sponsor:
Acceptance Rates
Overall Acceptance Rate 1,899 of 8,196 submissions, 23%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 236Total Downloads
- Downloads (Last 12 months)236
- Downloads (Last 6 weeks)10
Reflects downloads up to 05 Mar 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in