计算机工程 ›› 2019, Vol. 45 ›› Issue (7): 212-216.doi: 10.19678/j.issn.1000-3428.0051504

• 人工智能及识别技术 • 上一篇    下一篇

基于Location2vec的地点推荐算法

丁勇, 王翔, 蒋翠清   

  1. 合肥工业大学 管理学院, 合肥 230009
  • 收稿日期:2018-05-09 修回日期:2018-06-20 出版日期:2019-07-15 发布日期:2019-07-23
  • 作者简介:丁勇(1969-),男,副教授,主研方向为数据挖掘;王翔,硕士研究生;蒋翠清,教授、博士生导师。
  • 基金项目:
    国家自然科学基金“基于社交媒体大数据的产品创新需求发现方法研究”(71571059)。

Location Recommendation Algorithm Based on Location2vec

DING Yong, WANG Xiang, JIANG Cuiqing   

  1. School of Management, Hefei University of Technology, Hefei 230009, China
  • Received:2018-05-09 Revised:2018-06-20 Online:2019-07-15 Published:2019-07-23

摘要: 在地点推荐应用中,传统的协同过滤推荐算法由于签到数据稀疏导致推荐效果不佳。为提高推荐效果并克服传统协同过滤推荐算法受到热门地点影响的不足,提出一种新的地点推荐算法。将签到地点转换为向量,通过向量的余弦相似性计算签到地点的地点相似性。标记签到频次较低的地点为冷门地点,以计算签到地点的用户相似性,结合地理因素的影响,生成对用户的推荐列表。实验结果表明,相比传统协同过滤推荐算法,该算法F1值提升了0.009以上,推荐效果更好。

关键词: 地点推荐, 协同过滤, 冷门地点, 地点转换向量, 用户偏好, 基于位置的社交网络

Abstract: In the location recommendation application,the traditional collaborative filtering recommendation algorithms are not effective due to the sparseness of the check-in data.In order to improve the recommendation effect and overcome the shortcomings of the traditional collaborative filtering recommendation algorithms affected by popular locations,this paper proposes a new location recommendation algorithm.The check-in location is transformed into a vector,the similarity between the locations is calculated by the cosine similarity of the vectors.The locations with low check-in frequency are marked as unpopular locations,which can be used to calculate the similarity of the users at the check-in location.The user's recommendation list is generated in conjunction with the influence of the geographical factors.Experimental results show that compared with the traditional collaborative filtering recommendation algorithms,the F1 value of the algorithm is improved by more than 0.009,and the recommended effect is better.

Key words: location recommendation, collaborative filtering, unpopular location, Location2vec, user preference, location-based social networks

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