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基于社交关系的职位推荐系统的架构与实现

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摘要 从QQ,人人到微博,我们中很多人已经可以在线上找到自己的真实社交网络,这些社交网络上的应用也渗透到了各个领域,好友买卖,偷菜等带来的社交游戏,蘑菇街,美丽说为首的社交购物,都真的给我们带来了不一样的体验,本文的目的是希望在求职招聘领域,利用用户的社交关系向推荐个性化的职位信息。据调查,社交圈内好友的就业倾向往往趋于一致。当人们寻找工作时,圈内好友所在的公司往往也更受青睐,因为相较于陌生公司,好友所在公司的运营状况,薪酬待遇更加透明,公司也乐于采用内部推荐这种成本低廉的招聘方案,而构建基于社交关系的职位推荐系统的目的就是给用户推荐自己圈子里的好友工作过的公司正在招聘的职位,而非完全没有个性化的职位告示板。 From QQ, R_enren to Weibo, A lot of us already find our offline network through these online social network, and already bring us benefit in a lot areas. For example, in social games, you can play 'Friend Trade', 'Vegetable Strealing' with your fiends. And share what you want to buy with you friend on 'Mogujie' and 'Meilishuo'. So what this paper want to achieve is to take advantage of Social Network on job seeking, let user get job recommendation based on their social network, to find job in companys that their friends are worked.There is an old saying "Birds of a feather flock together", so it's likely that you gonna find job in company where people in you network are working at. And think about it, now, when you see a job from your favourite companys, you must want to check if you have inside connections there, and it even better to get internal referral. And Internal referral is more cheaper and effective way for company too. So make social network based job recommendation system is to recommend user the jobs from where their friends worked imtead of non-personalization job board.
作者 王超
出处 《数字技术与应用》 2013年第11期123-125,127,共4页 Digital Technology & Application
关键词 职位 找工作 推荐系统 微博 好友的公司 Job seeking job recommendation system weibo friends company
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  • 1Resnick P, lakovou N, Sushak M, et al. GroupLens: An open architecture for collaborative filtering of netnews. Proc 1994 Computer Supported Cooperative Work Conf, Chapel Hill, 1994: 175-186
  • 2Hill W, Stead L, Rosenstein M, et al. Recommending and evaluating choices in a virtual community of use. Proc Conf Human Factors in Computing Systems. Denver, 1995:194 -201
  • 3梅田望夫.网络巨变元年-你必须参加的大未来.先觉:先觉出版社,2006
  • 4Adomavicius G, Tuzhilin A. Expert-driven validation of Rule Based User Models in personalization applications. Data Mining and Knowledge Discovery, 2001, 5(1-2):33-58
  • 5Adomavicius G, Tuzhilin A. Toward the next generation of recommender systems: A survey of the state-of-the art and possible extensions. IEEE Trans on Knowledge and Data Engineering, 2005, 17(6): 734-749
  • 6Rich E. User modeling via stereotypes. Cognitive Science, 1979, 3(4) : 329-354
  • 7Goldberg D, Nichols D, Oki BM, et al. Using collaborative filtering to weave an information tapestry. Comm ACM, 1992, 35(12):61-70
  • 8Konstan JA, Miller BN, Maltz D, el al. GroupLens: Applying collaborative filtering to usenet news. Comm ACM, 1997, 40(3) : 77-87
  • 9Shardanand U, Maes P. Social information filtering: Algorithms for automating ‘Word of Mouth'. Proe Conf Human Factors in Computing Systems Denver, 1995: 210-217
  • 10Linden G, Smith B, York J. Amazon. corn recommendations: hem-to-item collaborative filtering. IEEE Internet Computing, 2003, 7(1): 76-80

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