情境感知的移动阅读个性化推荐算法研究
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摘要
针对移动环境下资源的个性化推荐问题,提出一种结合情境和协同过滤的移动阅读推荐算法。该算法汇聚了两阶段的情境感知思想,首先使用朴素贝叶斯方法推算用户某情境下偏好度最高的资源类别,然后对该类别下的资源,通过情境相似度的计算,过滤出仅当前情境或者与当前情境最相似的情境的"用户-资源"二维评分模型,再运用传统的基于用户的协同过滤算法产生推荐列表。抓取新浪博文进行实验测试,结果表明相同条件下所提出算法的平均绝对偏差值比其他相关算法要低,具有更高的推荐质量。
Aiming at the problem of personalized recommendation of resources in mobile environment, a mobile reading recommendation algorithm combining context and collaborative filtering is proposed. Two stages of context awareness are intergrated in the algorithm. Firstly, the naive Bayesian method is used to estimate the highest preference categories in a given context. Then, through the calculation of context similarity, the 'user resource' two-dimensional scoring model is filtered out which only belongs to the current context or the most similar context to the current context. Finally, the traditional user-based collaborative filtering algorithm is used to generate the recommendation list. Experiments on Sina Bowen are carried out, and the results show that the mean absolute error(MAE) of the proposed algorithm is lower than the related algorithm under the same conditions,and has higher recommendation quality.
Aiming at the problem of personalized recommendation of resources in mobile environment, a mobile reading recommendation algorithm combining context and collaborative filtering is proposed. Two stages of context awareness are intergrated in the algorithm. Firstly, the naive Bayesian method is used to estimate the highest preference categories in a given context. Then, through the calculation of context similarity, the 'user resource' two-dimensional scoring model is filtered out which only belongs to the current context or the most similar context to the current context. Finally, the traditional user-based collaborative filtering algorithm is used to generate the recommendation list. Experiments on Sina Bowen are carried out, and the results show that the mean absolute error(MAE) of the proposed algorithm is lower than the related algorithm under the same conditions,and has higher recommendation quality.
引文
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[ 2] Greg L,Brent S,Jeremy Y.Amazon.Com Recommen-dations:item-to-item collaborative filtering[J].IEEE Internet Computing,2003,7(1):76-80
[ 3] Deng A L,Zhu Y Y,Shi B L.A collaborative filtering recommendation algorithm based on item rating prediction[J].Journal of Software,2003,14(9):1621-1624
[ 4] Karypis G.Evaluation of item-based top-N recommendation algorithms[C].In:Proceedings of CIKM 2001,Atlanta,USA,2001.247-254
[ 5] Lee B H,Kim H N,Jung J G,et al.Location- based service with context data for a restaurant recommendation[C].In:Proceedings of the 17th International Conference DEXA,Krak6w,Poland,2006.430-438
[ 6] Dao T H,Jeong S R,Ahn H.A novel recommendation model of location-based advertising:context-aware collaborative filtering using GA approach[J].Expert Systems with Applications,2012,39 ( 3) :3731-3739
[ 7] Kim J,Lee D,Chung K Y.Item recommendation based oncontext-aware model for personalized u-healthcare service[J].Multimedia Tools and Applications,2014,71( 2) :855-872
[ 8] Karatzoglou A,Amatriain X,Baltrunas L,et al.Multiverse recommendation:n-dimensional tensor factorization for context-aware collaborative filtering [C].In:Proceedings of ACM Conference on Recommender Systems,Barcelona,Spain.2010.79-86
[ 9] 杨君,汪会玲,艾丹祥.一种基于情景的多维协同过滤新方法研究[J].图书情报工作,2011,55(21):110-114
[10] 曾子明,李鑫.移动环境下基于情境感知的个性化信息推荐[J].情报杂志,2012,31(8):166-170
[11] 田雪筠.基于情境感知的移动电子资源推荐技术研究[J].情报理论与实践,2015,38(5):86-89
[12] 洪亮,钱晨,樊星.移动数字图书馆资源的情境感知个性化推荐方法研究[J].现代图书情报技术,2016,32(7):110-119
[13] Koren Y.Factorization meets the neighborhood:a multifaceted collaborative filtering model[C].In:Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,Las Vegas,USA,2008.426-434
[14] Fazeli S,Loni B,et al.Implicit vs explicit trust in social matrix factorization[C].In:Proceedings of the 8th ACM Conference on Recommen-der System,Silicon Valley,USA,2014.317-320