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- 2018
基于个性化的多样性优化推荐算法
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Abstract:
针对不同人对多样性的偏好不同, 提出一种能够在寻优精度和多样性之间权衡的个性化的多样性优化方法．该方法采用一种依据用户历史偏好和项目类别专家评分的推荐技术, 生成包含新颖项目和关联项目的多样化的候选推荐列表, 然后依据用户多样化偏好程度进行后过滤技术, 筛选出最终的多样化推荐列表．最后, 本文通过实验结果对比发现, 所提出的方法能够有效地提高推荐列表的多样性, 而且能够实现个性化的多样化过程．
A recommendation algorithm for optimizing diversity based on personalization，which is capable of controlling the trade-off between accuracy and diversity，is proposed for different people’s preference for diversity. The method，utilizing the recommendation technique which relies on historical user preferences and expert evaluation of project categories，generates a diversified candidate recommendation list including new projects and related projects. Then according to user’s diversity preference，the post-filtering approaches are employed to generate the final diversified recommendation list. Finally，the experiments and evaluation show that the proposed method can effectively improve the diversity of recommender lists，and achieve personalized diversification process
| [1] | Castells P, Wang J, Lara R, et al. Workshop on novelty and diversity in recommender systems:Diversity [C]//<i>Acm Conference on Recommender Systems</i>. Chicago, USA, 2011:393-394. |
| [2] | Castagnos S, Brun A, Boyer A. When diversity is needed…but not expected![C]//<i>Proc</i> 3<i>rd Int Conf Advances Inf Mining Manage</i>. Lisbon, Portugal, 2013:44-50. |
| [3] | Sandoval S V. Novelty and diversity evaluation and enhancement in recommender systems[D]. Madrid:Departamento de Ingenier?-a Inform?atica, Escuela Polit?lcnica Superior, Universidad Aut?snoma, 2015. |
| [4] | Pathak A, Patra B K. A knowledge reuse framework for improving novelty and diversity in recommendations [C]//<i>Proceedings of the</i> 2<i>nd ACM IKDD Conference on Data Sciences</i>. Bangalore, India, 2015:11-19. |
| [5] | Jiang H, Qi X, Sun H. Choice-based recommender systems:A unified approach to achieving relevancy and diversity[J]. <i>Oper Res</i>, 2014, 62(5):973-993. |
| [6] | Chen L, Wu W, He L. How personality influences users’ needs for recommendation diversity?[C]//<i>CHI</i>’13 <i>Extended Abstracts on Human Factors in Computing Systems</i>. Paris, France, 2013:829-834. |
| [7] | Tintarev N, Dennis M, Masthoff J. Adapting recommendation diversity to openness to experience:A study of human behavior[C]//<i>User Modeling</i>, <i>Adaptation</i>, <i>and Personalization</i>. Rome, Italy, 2013:190-202. |
| [8] | Yu F, Zeng A, Gillard S, et al. Network-based recommendation algorithms:A review[J]. <i>Physica A</i>:<i>Statistical Mechanics and Its Applications</i>, 2016(452):192-208. |
| [9] | Zhou T, Jiang L L, Su R Q. Effect of initial configuration on network based recommendation[J]. <i>Europhys Lett</i>, 2008, 81(5):58004. |
| [10] | Candillier L, Chevalier M, Dudognon D, et al. Diversity in recommender systems:Bridging the gap between users and systems[C]//<i>Conference on Advances in Human-oriented & Personalized Mechanisms</i>. Venice, Italy, 2013:48-58. |
| [11] | 刘赫, 张相洪. 一种基于最大边缘相关的特征选择方法[J]. 计算机研究与发展, 2012, 49(2):354-360. Liu He, Zhang Xianghong. A feature selection method based on maximal marginal revelance[J]. <i>Journal of Computer Research and Development</i>, 2012, 49(2):354-360(in Chinese). |
| [12] | Boim R, Milo T, Novgorodov S. Diversification and refinement in collaborative filtering recommender[C]// <i>ACM International Conference on Information Knowledge Management</i>. Glasgow, UK, 2011:739-744. |
| [13] | Smyth B, McClave P. Similarity vs. diversity[C]//<i>In Based Reasoning Research and Development</i>. Vancouver, Canada, 2001:347-361. |
| [14] | Adomavicius G, Kwon Y. Overcoming accuracy-diversity tradeoff in recommender systems:A variance-based approach[C]//<i>Proceedings of Wits</i>’. Paris, France, 2008. |
| [15] | Vargas S. Novelty and diversity enhancement and evaluation in recommender systems[C]//<i>International Acm Sigir Conference on Research</i>. Golden Coast, Astralia, 2014:1281. |
| [16] | Noia T D, Ostuni V C, Rosati J, et al. An analysis of users’ propensity toward diversity in recommendations [C]//<i>Proceedings of the</i> 8<i>th ACM Conference on Recommender Systems</i>. SiliconValley, USA, 2014:285-288. |
| [17] | Zhang F G, Liu Y H, Xiong Q Q. A novel mass diffusion recommendation algorithm based on user’s nearest neighbors[C]//<i>Proceedings of the International Symposium on Information Technology Convergence</i>. Jeju Island, Korea, 2016:254-261. |
| [18] | Vargas S, Castells P. Exploiting the diversity of user preferences for recommendation[C]//<i>Proceedings of the<i> 10<i>th Conference on Open Research Areas in Information Retrieval</i>. Paris, France, 2013:129-136.</i></i></i></i> |
| [19] | Adomavicius G, Kwon Y. Optimization-based approaches for maximizing aggregate recommendation diversity[J]. <i>INFORMS Journal on Computing</i>, 2014(24):896-911. |
| [20] | Castells P, Vargas S, Wang J. Novelty and diversity metrics for recommender systems:Choice, discovery and relevance[C]//<i>Proceedings of International Workshop on Diverty</i>. Chicago, USA, 2013:29-37. |
| [21] | Barraza-Urbina A, Heitmann B, Hayes C, et al. Xplodiv:An exploitation-exploration aware diversification approach for recommender systems[C]//<i>Proceed-ings of the<i> 28<i>th International Flairs Conference</i>. Hollywod, USA, 2015:1-6. |
| [22] | Cho Y H, Kim J K. Application of Web usage mining and product taxonomy to collaborative recommendations in e-commerce[J]. <i>Expert Systems with Applications</i>, 2004(26):233-246. |
| [23] | Bobadilla T, Ortega F, Hernando A, et al. Recommender systems survey[J]. <i>Knowledge-Based Systems</i>, 2013(46):109-132. |
