基于隐式反馈的个性化游戏推荐方法

doi:10.16182/j.issn1004731x.joss.19-0636

系统仿真学报 ›› 2021, Vol. 33 ›› Issue (4): 809-817.doi: 10.16182/j.issn1004731x.joss.19-0636

基于隐式反馈的个性化游戏推荐方法

沙静, 曾巩俐, 杨扬, 卫遥

山东科技大学计算机科学与工程学院,山东青岛 266590

收稿日期:2019-12-06 修回日期:2020-04-13 出版日期:2021-04-18 发布日期:2021-04-14
作者简介:沙静(1976-),女,博士,副教授,CCF会员,研究方向为Petri网建模与验证,系统性能分析,大数据,机器学习。E-mail：xo5547@163.com
基金资助:
国家自然科学基金(61170078,61472228); 山东省科技发展计划(2012G0020120)

Personalized Game Recommendation Method Based on Implicit Feedback

Sha Jing, Zeng Gongli, Yang Yang, Wei Yao

College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China

Received:2019-12-06 Revised:2020-04-13 Online:2021-04-18 Published:2021-04-14

摘要/Abstract

摘要： 传统的推荐系统常用显式反馈进行个性化推荐,但显式反馈数据不易获取,质量不好且易引起用户反感,使推荐结果不能满足用户需求。隐式反馈数据更容易获取,可更好地为用户提供其感兴趣的内容。提出一种基于隐式反馈数据的个性化游戏推荐方法。该方法基于游戏时长、游戏次数等隐式反馈数据,构建针对游戏用户数据的隐式反馈推荐模型,通过隐语义推荐算法实现了游戏的个性化推荐。通过大量真实数据集的对比实验,表明该方法的精确率和召回率均优于其他方法。

关键词: 推荐系统, 隐式反馈, 隐语义模型, 个性化推荐

Abstract: Traditional recommendation systems often use explicit feedback for personalized recommendations. But the explicit feedback data is not easy to obtain, and the quality is poor, and the recommendation results unable to meet the requitrment of the user. Implicit feedback data is easier to obtain and can provide users with the better content. A personalized game recommendation method based on implicit feedback data is proposed. The method builds an implicit feedback recommendation model for game user data based on implicit feedback data such as the game duration and game numbers. A personalized recommendation of the game is implemented through an implicit semantic recommendation algorithm. Through comparative experiments on a large number of real data sets, it is shown that the accuracy and recall of the proposed method are better than other methods.

Key words: recommended system, implicit feedback, LFM(Latent Factor Model), personalized recommendations

中图分类号:

沙静, 曾巩俐, 杨扬, 卫遥. 基于隐式反馈的个性化游戏推荐方法[J]. 系统仿真学报, 2021, 33(4): 809-817.

Sha Jing, Zeng Gongli, Yang Yang, Wei Yao. Personalized Game Recommendation Method Based on Implicit Feedback[J]. Journal of System Simulation, 2021, 33(4): 809-817.

参考文献

[1] Pazzani M J, Billsus D.Content-based Recommendation Systems[M]. The Adaptive Web. Berlin: Springer, 2007: 325-341.
[2] 李伟霖, 王成良, 文俊浩. 基于评论与评分的协同过滤算法[J]. 计算机应用研究, 2017, 34(2): 361-364.
Li Weilin, Wang Chengliang, Wen Junhao.Reliability Analysis of Manipulator based on Fourth- Momentestimation[J]. Application Research of Computers, 2017, 34(2): 361-364.
[3] Wang C D, Deng Z H, Lai J H, et al.Serendipitous Recommendation in E-commerce Using Innovator-based Collaborative Filtering[J]. IEEE transactions on cybernetics (S2168-2267), 2019, 49(7): 2678-2692.
[4] 李博, 陈志刚, 黄瑞, 等. 基于LDA模型的音乐推荐算法[J]. 计算机工程, 2016, 42(6): 175-179.
Li Bo, Chen Zhigang, Huang Rui, et al.Music Recommendation Algorithm based on LDA Model[J]. Computer Engineering, 2016, 42(6): 175-179.
[5] Chen C, Zheng X, Wang Y, et al.Context-aware Collaborative Topic Regression with Social Matrix Factorization for Recommender Systems[C]// 28th AAAI Conference on Artificial Intelligence. Québec: AAAI Press, 2014: 9-15.
[6] Deng Z H, Huang L, Wang C D, et al.DeepCF: a unified Framework of Representation Learning and Matching Function Learning in Recommender System[C]// 33rd AAAI Conf. on Artificial Intelligence. Palo Alto: AAAI Press, 2019: 61-68.
[7] Pan R, Zhou Y, Cao B, et al.One-class Collaborative Filtering[C]// 8^th IEEE International Conference on Data Minin. Pisa: IEEE, 2008: 502-511.
[8] Jiang M, Cui P, Liu R, et al.Social Contextual Recommendation[C]// 21st ACM Int'l Conf. on Information and Knowledge Management. Maui, Hawaii, USA: ACM Press, 2012: 45-54. [doi: 10.1145/2396761.2396771]
[9] Chen K, Chen T, Zheng G, et al.Collaborative Personalized Tweet Recommendation[C]//35th Int'l ACM SIGIR Conf. on Research and Development in Information Retrieval. Portland Oregon USA: ACM Press, 2012: 661-670. [doi: 10.1145/2348283. 2348372]
[10] Shi Y, Karatzoglou A, Baltrunas L, et al.TFMAP: Optimizing MAP for Top-n Context-aware Recommendation[C]//35th Int'l ACM SIGIR Conf. on Research and Development in Information Retrieval. Portland Oregon USA: ACM Press, 2012: 155-164. [doi: 10.1145/2348283. 2348308]
[11] Hu Y, Koren Y, Volinsky C.Collaborative Filtering for Implicit Feedback Datasets[C]//8^th IEEE Int'l Conf. on Data Mining (ICDM 2008). Pisa, Italy: IEEE, 2008: 263-272. [doi: 10.1109/ICDM.2008.22]
[12] Steck H.Training and Testing of Recommender Systems on Data Missing not at Random[C]//16th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining. Washington DC USA: ACM Press, 2010: 713-722. [doi: 10.1145/1835804. 1835895]
[13] Herlocker J L, Konstan J A, Borchers A, et al.An Algorithmic Framework for Performing Collaborative Filtering[C]//22nd Annual Int'l ACM SIGIR Conf. on Research and Development in Information Retrieval. Berkeley California USA: ACM Press, 1999: 230-237. [doi:10.1145/312624. 312682]
[14] Sarwar B, Karypis G, Konstan J, et al.Item-Based Collaborative Filtering Recommendation Algorithms[C]// 10th Int'l Conf. on World Wide Web. Hong Kong: ACM Press, 2001: 285-295. [doi: 10.1145/371920. 372071]
[15] Paterek A.Improving Regularized Singular Value Decomposition for Collaborative Filtering[C]//KDD Cup and Workshop. San Jose: ACM Press, 2007: 5-8.
[16] Koren Y.Collaborative Filtering with Temporal Dynamics[J]. Communications of the ACM (S0001-0782), 2010, 53(4): 89-97.
[17] 印鉴, 王智圣, 李琪, 等. 基于大规模隐式反馈的个性化推荐[J]. 软件学报, 2014, 25(9): 1953-1966.
Yin Jian, Wang Zhisheng, Li Qi, et al.Personalized Recommendation based on Large-scale Implicit Feedback[J]. Journal of Software, 2014, 25(9): 1953-1966.
[18] Rendle S, Freudenthaler C, Gantner Z, et al.BPR: Bayesian Personalized Ranking from Implicit Feedback[C]//25th Conf. on Uncertainty in Artificial Intelligence. Montreal, Canada: AUAI Press, 2009: 452-461.
[19] 吴宾, 娄铮铮, 叶阳东. 联合正则化的矩阵分解推荐算法[J]. 软件学报, 2018, 29(9): 2681-2696.
Wu Bin, Lou Zhengzheng, Ye Yangdong.Co-regularized Matrix Factorization Recommendation Algorithm[J]. Journal of Software, 2018, 29(9): 2681-2696.
[20] 刘华锋, 景丽萍, 于剑. 融合社交信息的矩阵分解推荐方法研究综述[J]. 软件学报, 2018, 29(2): 340-362.
Liu Huafeng, Jing Liping, Yu Jian.Survey of Matrix Factorization based Recommendation Methods by Integrating Social Information[J]. Journal of Software, 2018, 29(2): 340-362.
[21] Lin C Y, Wang L C, Tsai K H.Hybrid Real-time Matrix Factorization for Implicit Feedback Recommendation Systems[J]. IEEE Access (S2169-3536), 2018, 6: 21369-21380.
[22] 俞东进, 陈聪, 吴建华, 等. 基于隐式反馈数据的个性化游戏推荐[J]. 电子学报, 2018, 46(11): 2626-2632.
Yu Dongjin, Chen Cong, Wu Jianhua, et al.Personalized Game Recommendation Based on Implicit Feedback[J]. Electronic Journal, 2018, 46(11): 2626-2632.
[23] 李琳娜, 江雪琴. 推荐系统中的隐语义模型研究[J]. 情报工程, 2016, 2(4): 30-39.
Li Linna, Jiang Xueqin.Research on the Latent Factor Model in Recommender System[J]. Intelligence engineering, 2016, 2(4): 30-39.
[24] 邵波. 基于信息匹配的推荐算法研究[D]. 沈阳: 沈阳建筑大学, 2015.
Shao Bo.Research on Recommendation Algorithm based on Information Matching [D]. Shenyang: Shenyang Jianzhu University, 2015.
[25] 王俊淑, 张国明, 胡斌. 基于深度学习的推荐算法研究综述[J]. 南京师范大学学报(工程技术版), 2018, 18(4): 33-43.
Wang Junshu, Zhang Guoming, Hu Bin.A Review of Research on Recommendation Algorithms Based on Deep Learning[J]. Journal of Nanjing Normal University (Engineering and Technology Edition), 2018, 18(4): 33-43.
[26] 程明月, 刘淇, 李徵, 等. 多重对级贝叶斯个性化排序算法[J]. 南京信息工程大学学报(自然科学版), 2019, 11(3): 302-308.
Cheng Mingyue, Liu Qi, Li Hui, et al.Multi-pair Bayesian Personalized Ranking[J]. Journal of Nanjing University of Information Science and Technology (Natural Science Edition), 2019, 11(3): 302-308.
[27] Li G, Chen Q.Exploiting Explicit and Implicit Feedback for Personalized Ranking[J]. Mathematical Problems in Engineering (S1024-123X), 2016(1): 1-11.

基于隐式反馈的个性化游戏推荐方法

Personalized Game Recommendation Method Based on Implicit Feedback

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