Personalized Recommendation Model Based on Dynamic Time
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摘要: 在推荐系统中，往往会存在数据的非实时性、稀疏性和冷启动性等问题，文中通过引入遗忘曲线来跟踪用户对资源偏好程度随时间变化情况，利用提出一种改进的K-Means聚类算法对用户集进行聚类，根据改进的个性化推荐算法对用户进行推荐，建立了一种基于动态时间的个性化推荐模型. 通过实验验证，文中提出的个性化推荐模型能够获取准确的用户偏好信息，并缓解冷启动问题，降低算法计算的时间空间复杂度，提高个性化推荐算法的推荐质量.
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关键词:
- 热传导
Abstract: In the recommendation systems, it often exists some problems of non-real-time data, sparse data and cold start. First, the paper uses forgetting curve to track the changed of user’s interests with time, and then puts forward an improved K-Means clustering algorithm to cluster users, and finally uses the improved personalized recommendation algorithm. A model of personalized recommendation based on dynamic time is built. The experimental results show that the proposed personalized recommendation model can get more accurate recommendation performance and alleviate the cold start problem. It also can reduce the computing time and space complexity, and improve the quality of the recommendation of personalized recommendation algorithm.-
Keywords:
- heat conduction
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[1]Li Chen, Personality in Recommender Systems[C]//Proceedings of the 3rd Workshop on Emotions and Personality in Personalized Systems 2015, p.2-2, September 16-20, 2015, Vienna, Austria
[2]Maria Augusta S.N. Nunes, Rong Hu, Personality-based recommender systems: an overview[C]//Proceedings of the sixth ACM conference on Recommender systems, September 09-13, 2012, Dublin, Ireland.
[3]Nirmal Jonnalagedda, Susan Gauch, Personalized News Recommendation Using Twitter[C]//Proceedings of the 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), p.21-25, November 17-20, 2013.
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[5]Yuan Guan.Preference of online users and personalized recommendations[J].Physica A: Statistical Mechanics and its Applications, 2013, 392(16):3417-3423
[6]Mojtaba Salehi.Hybrid recommendation approach for learning material based on sequential pattern of the accessed material and the learner’s preference tree[J].Knowledge-Based Systems, 2013, 48(1):57-69
[7]Yongfeng Zhang.Daily-Aware Personalized Recommendation based on Feature-Level Time Series Analysis[C]//Proceedings of the 24th International Conference on World Wide Web, 2015, 1373-1383.
[8] NAYAK R.A social matching system for an online dating network: a preliminary study[C]//Proceedings 2010 10th IEEE International Conference on Data Mining Workshops (ICDMW 2010). 2010.352-357.
[9] WANG T T.Predicting New User’s Behavior in Online Dating Systems[M]. Advanced Data Mining and Applications.Springer Berlin Heidelberg, 2011.266-277.
[10]于洪, 李俊华.一种解决新项目冷启动问题的推荐算法[J].软件学报, 2015, 26(6):1395-1408
[11]Koren Y.Collaborative filtering with temporal dynamics[J].Communications of the ACM, 2010, 53(4):89-97
[12]Florent Garcin.Personalized News Recommendation Based on Collaborative Filtering[C]//Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology, p.437-441, December 04-07, 2012.
[13] 丁浩, 戴牡红.基于用户评分和遗传算法的协同过滤推荐算法[J].计算机工程与应用, 2015,51 (17): 0-1
[14]Fidel Cacheda.Comparison of collaborative filtering algorithms: Limitations of current techniques and proposals for scalable, high-performance recommender systems[C]// ACM Transactions on the Web (TWEB), 2011, 1-33
[15]Cataldo Musto.Enhanced vector space models for content-based recommender systems[C]//In Proceedings of the fourth ACM conference on Recommender systems, Rec Sys ‘10, pages 361-364, New York, NY, USA, 2010.ACM.
[16]L Averell and A Heathcote.The form of the forgetting curve and the fate of memories[J].Journal of Mathematical Psychology, 2011, 55(1):25-35
[1]Li Chen, Personality in Recommender Systems[C]//Proceedings of the 3rd Workshop on Emotions and Personality in Personalized Systems 2015, p.2-2, September 16-20, 2015, Vienna, Austria
[2]Maria Augusta S.N. Nunes, Rong Hu, Personality-based recommender systems: an overview[C]//Proceedings of the sixth ACM conference on Recommender systems, September 09-13, 2012, Dublin, Ireland.
[3]Nirmal Jonnalagedda, Susan Gauch, Personalized News Recommendation Using Twitter[C]//Proceedings of the 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), p.21-25, November 17-20, 2013.
[4]Ralf Krestel.Recommendation on the social web: diversification and personalization[J].Proceedings of the 2011 international workshop on DETecting and Exploiting Cultural diversiTy on the social web, 2011, 10(1):24-24
[5]Yuan Guan.Preference of online users and personalized recommendations[J].Physica A: Statistical Mechanics and its Applications, 2013, 392(16):3417-3423
[6]Mojtaba Salehi.Hybrid recommendation approach for learning material based on sequential pattern of the accessed material and the learner’s preference tree[J].Knowledge-Based Systems, 2013, 48(1):57-69
[7]Yongfeng Zhang.Daily-Aware Personalized Recommendation based on Feature-Level Time Series Analysis[C]//Proceedings of the 24th International Conference on World Wide Web, 2015, 1373-1383.
[8] NAYAK R.A social matching system for an online dating network: a preliminary study[C]//Proceedings 2010 10th IEEE International Conference on Data Mining Workshops (ICDMW 2010). 2010.352-357.
[9] WANG T T.Predicting New User’s Behavior in Online Dating Systems[M]. Advanced Data Mining and Applications.Springer Berlin Heidelberg, 2011.266-277.
[10]于洪, 李俊华.一种解决新项目冷启动问题的推荐算法[J].软件学报, 2015, 26(6):1395-1408
[11]Koren Y.Collaborative filtering with temporal dynamics[J].Communications of the ACM, 2010, 53(4):89-97
[12]Florent Garcin.Personalized News Recommendation Based on Collaborative Filtering[C]//Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology, p.437-441, December 04-07, 2012.
[13] 丁浩, 戴牡红.基于用户评分和遗传算法的协同过滤推荐算法[J].计算机工程与应用, 2015,51 (17): 0-1
[14]Fidel Cacheda.Comparison of collaborative filtering algorithms: Limitations of current techniques and proposals for scalable, high-performance recommender systems[C]// ACM Transactions on the Web (TWEB), 2011, 1-33
[15]Cataldo Musto.Enhanced vector space models for content-based recommender systems[C]//In Proceedings of the fourth ACM conference on Recommender systems, Rec Sys ‘10, pages 361-364, New York, NY, USA, 2010.ACM.
[16]L Averell and A Heathcote.The form of the forgetting curve and the fate of memories[J].Journal of Mathematical Psychology, 2011, 55(1):25-35
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