计算机科学 ›› 2019, Vol. 46 ›› Issue (4): 228-234.doi: 10.11896/j.issn.1002-137X.2019.04.036
苏畅, 武鹏飞, 谢显中, 李宁
SU Chang, WU Peng-fei, XIE Xian-zhong, LI Ning
摘要： 在基于位置的社交网络中,协同过滤作为目前应用最广泛的推荐技术,存在数据稀疏性和冷启动等问题。针对协同过滤算法的不足,提出了一种结合用户兴趣和地理因素的兴趣点推荐算法。该方法首先通过自适应带宽的核密度分布、朴素贝叶斯算法以及兴趣点的流行度挖掘用户的地理偏好,并根据地理偏好模型筛选出一部分候选推荐兴趣点;然后,为了克服协同过滤算法的数据稀疏性问题和用户冷启动问题,结合用户签到相似性、类别信息和用户信任度构建用户偏好模型进行兴趣点推荐;最后,使用 Yelp数据集进行实验分析,结果表明所提出的基于用户兴趣和地理因素的兴趣点推荐模型取得了良好的推荐效果。
中图分类号:
| [1]LI H,GE Y,ZHU H,et al.Point-of-Interest Recommendations:Learning Potential Checkins from Friends[C]∥In Proceedings of the 22nd ACM SIGKDD International Conference on Know-ledge Discovery and Data Mining.San Francisco:ACM Press,2016:975-984. [2]YU Y,WANG H,SUN S,et al.Exploiting location significance and user authority for point-of-interest recommendation[C]∥Advances in Knowledge Discovery and Data Mining- 21st Paci-fic-Asia Conference.South Korea:PAKDD Press,2017:119-130. [3]YANG S,HUANG G,XIANG Y,et al.Modeling User Preferences on Spatiotemporal Topics for Point-of-Interest Recommendation[C]∥IEEE International Conference on Services Computing.Honolulu:IEEE Press,2017:204-211. [4]LIM K H,CHAN J,LECKIE C,et al.Personalized trip recommendation for tourists based on user interests,points of interest visit durations and visit recency[J].Knowledge & Information Systems,2017(2):1-32. [5]XIE M,WANG S,WANG H,et al.Learning Graph-based POI Embedding for Location based Recommendation[C]∥ACM International on Conference on Information and Knowledge Mana-gement.Indianapolis:ACM,2016:15-24. [6]FANG M Y,DAI B R.Power of Bosom Friends,POI Recom- mendation by Learning Preference of Close Friends and Similar Users[C]∥Big Data Analytics and Knowledge Discovery.Porto:Springer Press,2016:179-192. [7]ZHANG J D,CHOW C Y,ZHENG Y.ORec:An Opinion-Based Point-of-Interest Recommendation Framework[C]∥ACM International on Conference on Information and Knowledge Mana-gement.Melbourne:ACM,2015:1641-1650. [8]YE M,YIN P,LEE W C,et al.Exploiting geographical influence for collaborative point-of-interest recommendation[C]∥Proceeding of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval.Beijing:ACM Press,2011:325-334. [9]ZHANG J D,CHOW C Y,LI Y.iGeoRec:A Personalized and Efficient Geographical Location Recommendation Framework[J].IEEE Transactions on Services Computing,2015,8(5):701-714. [10]LI H,HONG R,ZHU S,et al.Point-of-Interest Recommender Systems:A Separate-Space Perspective[C]∥IEEE International Conference on Data Mining.Barcelona:IEEE Press,2016:231-240. [11]CHENG C,HUANG J,ZHONG N.Point-of-Interest Recommendations by Unifying Multiple Correlations[C]∥Web-Age Information Management.Nanchang:Springer Press,2016:178-190. [12]LIN K,WANG J,ZHANG Z,et al.Adaptive location recommendation algorithm based on location-based social networks[C]∥International Conference on Computer Science & Education.Cambridge:IEEE Press,2015:137-142. [13]WANG R Q,PAN J,LI Y X,Research on collaborative recommendation method based on multiple data sources of social network.Telecommunications Science[J].Telecommunications Science,2015,31(6):78-84.(in Chinese) 王瑞琴,潘俊,李一啸.基于多社交数据源的协同推荐方法研究[J].电信科学,2015,31(6):78-84. [14]HE M,XIAO R,LIU W S,et al.Collaborative Filtering Recommendation Algorithm Combing Category Information and User Interests [J].Telecommunications Science,2017,44(8):230-235.(in Chinese) 何明,肖润,刘伟世,等.融合类别信息和用户兴趣度的协同过滤推荐算法[J].计算机科学,2017,44(8):230-235. [15]Yelp.Challenge Data Set[OL].(2015-12-24).http://www.yelp.com/dataset_change. [16]ZHANG J D,CHOW C Y.GeoSoCa:Exploiting Geographical,Social and Categorical Correlations for Point-of-Interest Recommendations[C]∥Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval.Santiago:ACM Press,2015:443-452. |
| [1] | 程章桃, 钟婷, 张晟铭, 周帆. 基于图学习的推荐系统研究综述 Survey of Recommender Systems Based on Graph Learning 计算机科学, 2022, 49(9): 1-13. https://doi.org/10.11896/jsjkx.210900072 |
| [2] | 王冠宇, 钟婷, 冯宇, 周帆. 基于矢量量化编码的协同过滤推荐方法 Collaborative Filtering Recommendation Method Based on Vector Quantization Coding 计算机科学, 2022, 49(9): 48-54. https://doi.org/10.11896/jsjkx.210700109 |
| [3] | 孙晓寒, 张莉. 基于评分区域子空间的协同过滤推荐算法 Collaborative Filtering Recommendation Algorithm Based on Rating Region Subspace 计算机科学, 2022, 49(7): 50-56. https://doi.org/10.11896/jsjkx.210600062 |
| [4] | 蔡晓娟, 谭文安. 一种改进的融合相似度和信任度的协同过滤算法 Improved Collaborative Filtering Algorithm Combining Similarity and Trust 计算机科学, 2022, 49(6A): 238-241. https://doi.org/10.11896/jsjkx.210400088 |
| [5] | 何亦琛, 毛宜军, 谢贤芬, 古万荣. 基于点割集图分割的矩阵变换与分解的推荐算法 Matrix Transformation and Factorization Based on Graph Partitioning by Vertex Separator for Recommendation 计算机科学, 2022, 49(6A): 272-279. https://doi.org/10.11896/jsjkx.210600159 |
| [6] | 郭亮, 杨兴耀, 于炯, 韩晨, 黄仲浩. 基于注意力机制和门控网络相结合的混合推荐系统 Hybrid Recommender System Based on Attention Mechanisms and Gating Network 计算机科学, 2022, 49(6): 158-164. https://doi.org/10.11896/jsjkx.210500013 |
| [7] | 董晓梅, 王蕊, 邹欣开. 面向推荐应用的差分隐私方案综述 Survey on Privacy Protection Solutions for Recommended Applications 计算机科学, 2021, 48(9): 21-35. https://doi.org/10.11896/jsjkx.201100083 |
| [8] | 王营丽, 姜聪聪, 冯小年, 钱铁云. 时间感知的兴趣点推荐方法 Time Aware Point-of-interest Recommendation 计算机科学, 2021, 48(9): 43-49. https://doi.org/10.11896/jsjkx.210400130 |
| [9] | 詹皖江, 洪植林, 方路平, 吴哲夫, 吕跃华. 基于对抗性学习的协同过滤推荐算法 Collaborative Filtering Recommendation Algorithm Based on Adversarial Learning 计算机科学, 2021, 48(7): 172-177. https://doi.org/10.11896/jsjkx.200600077 |
| [10] | 邵超, 宋淑米. 基于信任关系下用户兴趣偏好的协同过滤推荐算法 Collaborative Filtering Recommendation Algorithm Based on User Preference Under Trust Relationship 计算机科学, 2021, 48(6A): 240-245. https://doi.org/10.11896/jsjkx.200700113 |
| [11] | 武建新, 张志鸿. 融合用户评分与显隐兴趣相似度的协同过滤推荐算法 Collaborative Filtering Recommendation Algorithm Based on User Rating and Similarity of Explicit and Implicit Interest 计算机科学, 2021, 48(5): 147-154. https://doi.org/10.11896/jsjkx.200300072 |
| [12] | 肖诗涛, 邵蓥侠, 宋卫平, 崔斌. 面向协同过滤推荐的新型混合评分函数 Hybrid Score Function for Collaborative Filtering Recommendation 计算机科学, 2021, 48(3): 113-118. https://doi.org/10.11896/jsjkx.200900067 |
| [13] | 郝志峰, 廖祥财, 温雯, 蔡瑞初. 基于多上下文信息的协同过滤推荐算法 Collaborative Filtering Recommendation Algorithm Based on Multi-context Information 计算机科学, 2021, 48(3): 168-173. https://doi.org/10.11896/jsjkx.200700101 |
| [14] | 韩立锋, 陈莉. 融合用户属性与项目流行度的用户冷启动推荐模型 User Cold Start Recommendation Model Integrating User Attributes and Item Popularity 计算机科学, 2021, 48(2): 114-120. https://doi.org/10.11896/jsjkx.200900152 |
| [15] | 李康林, 古天龙, 宾辰忠. 多空间交互协同过滤推荐 Multi-space Interactive Collaborative Filtering Recommendation 计算机科学, 2021, 48(12): 181-187. https://doi.org/10.11896/jsjkx.201100031 |
|
||
首页