3.6 本章小结
本章总结了常用的基于内容的推荐算法原理、实现方案、应用场景,并对基于内容的推荐算法的优缺点及实践过程中需要关注的问题进行了分析讨论。基于内容的推荐算法一般用于推荐召回阶段,通过内容特征来为用户选择可能喜欢的标的物。第26章和第27章会讲解两个基于内容推荐算法的真实案例,读者可参考学习,以更好地掌握内容推荐系统的精髓。
参考文献
[1]David M Blei,Andrew Y Ng,Michael I Jordan.Latent Dirichlet Allocation[C].[S.l.]:Journal of Machine Learning Research,2003.
[2]Jinhui Yuan,Fei Gao,Qirong Ho,et al.LightLDA:Big Topic Models on Modest Computer Clusters[C].[S.l.]:ACM,2015.
[3]Tomas Mikolov,Chen Kai,Corrado Greg,et al.Efficient Estimation of Word Representations in Vector Space[C].[S.l.]:Arxiv,2013.
[4]Tomas Mikolov,Ilya Sutskever,Kai Chen,et al.Distributed Representations of Words and Phrases and their Compositionality[C].[S.l.]:NIPS,2013.
[5]Quoc V Le,Tomas Mikolov.Distributed Representations of Sentences and Documents[C].[S.l.]:ICML,2014.
[6]Pasquale.Content-based recommender systems:State of the art and trends[C].[S.l.]:Recommender Systems Handbook,2011.
[7]Michael J Pazzani,Daniel Billsus.Content-based recommendation systems[C].[S.l.]:The adaptive web:methods and strategies of web personalization,2009.
[8]RadimŘehůřek,Lev Konstantinovskiy,et al.gensim[A/OL].Github(2011-02-14).https://github.com/RaRe-Technologies/gensim.
[9]Matthijs Douze,Lucas Hosseini,et al.faiss[A/OL].Github(2018-02-23).https://github.com/facebookresearch/faiss.