| 基于Item2Vec负采样优化的专题地图产品个性化推荐方法研究 |
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| 引用本文： | 毛文山,赵红莉,孙凤娇,蒋云钟,姜倩,朱彦儒.基于Item2Vec负采样优化的专题地图产品个性化推荐方法研究[J].地球信息科学,2020,22(11):2128-2139. |
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| 作者姓名： | 毛文山 赵红莉 孙凤娇 蒋云钟 姜倩 朱彦儒 |
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| 作者单位： | 1.兰州交通大学测绘与地理信息学院,兰州 7300702.地理国情监测技术应用国家地方联合工程研究中心,兰州 7300703.甘肃省地理国情监测工程实验室,兰州 7300704.中国水利水电科学研究院水资源研究所,北京 1000385.赤峰工业职业技术学院,赤峰 024005 |
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| 基金项目： | 兰州交通大学优秀平台(201806);中国工程科技知识中心建设项目-水利专业知识服务系统(CKCEST-2019-1-6) |
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| 摘 要： | 建立适用于专题地图产品检索的用户偏好推荐模型是提高专题地图质量的有效方式之一,在专题地图产品推荐场景中,存在严重的内容冷启动和评论数据稀疏问题,现有的推荐算法无法为特定类用户推荐不同特征的专题地图产品,导致用户从专题地图中获取偏好信息受到限制。因此,本文构建基于负采样的连续词袋模型和基于Word2Vec的Item2Vec相结合的用户偏好推荐方法,用于专题地图产品推荐。① 计算用户行为日志文件中交互行为数据的隐性评分,以代替专题地图推荐信息流场景中稀疏的用户评论数据;② 基于负采样的连续词袋模型提取目标专题地图的前后地图序列感知特征信息,通过控制正负样本比例为1:2,提升目标专题地图潜在评分的预测精度;③ 通过Item2Vec将带有用户行为特征信息的专题地图映射到向量空间,计算用户对专题地图的相似度矩阵,根据用户偏好程度完成推荐。在构建的专题地图评分实验数据集Thematic CMaps和4个公开验证数据集MovieLens上的测试结果表明:与LFM、Personal Rank、Content Based和SVD 4种传统推荐算法相比,本文所提方法可有效提高潜在评分的预测精度,推荐性能最高达到27.85%;与以霍夫曼采样方式的Item2Vec基础方法和YouTubeNet 2种神经网络推荐算法相比,评分预测精度有一定提高,且推荐性能不断提升,最高达到2.97%和5.78%。以经典算法奇异值分解（SVD）为例,将MovieLens-20M数据集切分后,在数据量不断增大的数据子集中,本文所用方法的评分预测精度和性能均优于SVD方法。
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| 关 键 词： | 地图个性化推荐 专题地图产品检索 深度学习 负采样 Item2Vec CBOW模型 用户事件行为 隐性评分 |
| 收稿时间： | 2019-11-07 |
Personalized Recommendation Method of Thematic Map Products based on Item2Vec with Negative Sampling Optimization |
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| MAO Wenshan,ZHAO Hongli,SUN Fengjiao,JIANG Yunzhong,JIANG Qian,ZHU Yanru.Personalized Recommendation Method of Thematic Map Products based on Item2Vec with Negative Sampling Optimization[J].Geo-information Science,2020,22(11):2128-2139. |
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| Authors: | MAO Wenshan ZHAO Hongli SUN Fengjiao JIANG Yunzhong JIANG Qian ZHU Yanru |
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| Abstract: | Establishing a user preference recommendation model suitable for thematic map product search is one of the effective ways to improve the quality of the thematic map products. In the thematic map product recommendation scenario, there are serious problems of content cold-start and sparse comment data. The existing recommendation algorithms cannot recommend thematic map products with different features for specific types of users, resulting in users' limited preference for obtaining preference information from the thematic maps. Hence, this paper presents a user preference recommendation method based on the combination of CBOW with Negative Sampling and Iten2Vec based on Word2Vec. Firstly, calculating implicit ratings of the interaction behavior data in the user behavior log, to replace sparse user ratings in thematic disaster scenarios; Secondly, extracting context-aware feature information of central thematic map based on CBOW model with Negative Sampling. By controlling the ratio of positive and negative samples to 1:2, the prediction accuracy of the potential score of the target thematic map is improved; Finally, mapping Thematic CMaps with user behavior characteristics information to vector space via Item2Vec, calculating the user's similarity matrix to the thematic map and completing recommendations based on user preference. Test results on thematic map scoring experiment dataset Thematic CMaps and four validation dataset MovieLens show that, compared with the four traditional recommendation algorithm of LFM, Personal Rank, Content Based, and SVD, this proposed method can effectively improve the precision potential scoring, and the highest recommending performance is 27.85%. Compared with Item2Vec with Huffman sampling method and YouTubeNet two neural network recommendation algorithms, the score prediction accuracy has improved to a certain extent, and the recommendation performance has been continuously improved, reaching the maximums of 2.97% and 5.78%. Taking the singular value decomposition (SVD) of the classic algorithm as an example, in the increasing data subset after the segmentation of MovieLens-20M dataset, the score prediction accuracy and performance of the method used in this paper are better than SVD method. |
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| Keywords: | map personalized recommendation thematic map products retrieval deep learning negative sampling method Item2Vec CBOW model user event behavior implicit ratings |
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