认同度修正下的近相邻改进推荐算法研究

doi:10.3778/j.issn.1002-8331.2101-0519

摘要/Abstract

摘要： 如何融合多方因素准确地为用户提供个性化产品一直是关注的热点问题，由此，一个新的近相邻改进算法融入了大众化认同度和个性化认同度，利于更加高效地挖掘隐藏信息。实验结果表明，相对于传统近相邻算法，认同度修正算法虽然查全率小幅度上下波动，但其他多个评价指标都得到极大提升，假正率和深度有所减少，查准率、[F1]值和提升度得以增加，并且，受试者特征曲线和提升曲线也都说明此修正算法具有更为显著的推荐效果。

关键词: 推荐算法, 认同度, 近相邻算法, 电影推荐, 推荐效果

Abstract: How to integrate various factors for providing users accurately with personalized products that is always a hot issues. A new near-neighbor improved algorithm is brought forward through adopting popularization and personalization recognition degree correction, which can mine the hidden information more efficiently. After shown by the experimental results, although the recall fluctuates slightly up and down on that correction algorithm relative to the traditional near-neighbor algorithm, a number of other evaluation indexes improve dramatically. The false positive rate and the depth decrease, meanwhile, the precision, F1 value and the lift increase totally. In addition, the receiver operating characteristic curve and the lifting curve both suggest that the improved algorithm has more significant recommending effects.

Key words: recommendation algorithm, recognition degree, near-neighbor algorithm, movie recommendation, recommending effect

李剑锋, 封林慧, 于天一. 认同度修正下的近相邻改进推荐算法研究[J]. 计算机工程与应用, 2022, 58(7): 116-121.

LI Jianfeng, FENG Linhui, YU Tianyi. Research on Near Neighbor Improved Recommendation Algorithm Based on Recognition Degree Correction[J]. Computer Engineering and Applications, 2022, 58(7): 116-121.

参考文献

[1] MARYAM K N，MOHD N M，SURIAYATI C，et al.Improving the accuracy of collaborative filtering recommendations using clustering and association rules mining on implicit data[J].Computers in Human Behavior，2017，67：113-128.
[2] 赵守香，唐胡鑫，熊海涛.大数据分析与应用[M].北京：航空工业出版社，2015.
ZHAO S X，TANG H X，XIONG H T.Big data analysis and application[M].Beijing：Aviation Industry Press，2015.
[3] HWANG T G，PARK C S，HONG J H，et al.An algorithm for movie classification and recommendation using genre correlation[J].Multimedia Tools and Applications，2016，75：12843-12858.
[4] 何明，肖润，刘伟世，等.融合类别信息和用户兴趣度的协同过滤推荐算法[J].计算机科学，2017，44（8）：230-235.
HE M，XIAO R，LIU W S，et al.The fusion of category information and user interest degree of collaborative filtering recommendation algorithm[J].Computer Science，2017，44（8）：230-235.
[5] 杨宝强.基于语义分析的用户兴趣演化模型[J].江苏商论，2020（4）：22-24.
YANG B Q.User interest evolution model based on semantic analysis[J].Jiangsu Commercial Forum，2020（4）：22-24.
[6] 江周峰，杨俊，鄂海红.结合社会化标签的基于内容的推荐算法[J].软件，2015，36（1）：1-5.
JIANG Z F，YANG J，E H Y.Combined with social tag recommendation algorithm based on content[J].Software，2015，36（1）：1-5.
[7] 张丹，周从华.基于改进的隐马尔可夫模型的新闻推荐算法[J].计算机与数字工程，2020，48（10）：2332-2337.
ZHANG D，ZHOU C H.News recommendation algorithm based on improved hidden Markov model[J].Computer & Digital Engineering，2020，48（10）：2332-2337.
[8] 闵磊.基于社区发现的个性化推荐技术研究[J].科技资讯，2020，30：217-218.
MIN L.Research on personalized recommendation based on community detection[J].Science & Information，2020，30：217-218.
[9] 范波，程久军.用户间多相似度协同过滤推荐算法[J].计算机科学，2012，39（1）：23-26.
FAN B，CHENG J J.Much similarity between user collaborative filtering recommendation algorithm[J].Computer Science，2012，39（1）：23-26.
[10] 刘国梁.大数据环境下基于用户行为的个性化推荐算法研究[D].兰州：兰州交通大学，2017.
LIU G L.Big data environment personalized recommendation algorithm based on user behavior research[D].Lanzhou：Lanzhou Jiaotong University，2017.
[11] 李征，段垒.基于用户兴趣评分填充的改进混合推荐方法[J].工程科学与技术，2019，51（1）：189-196.
LI Z，DUAN L.Improved hybrid recommendation method based on user interest score filling[J].Engineering Science and Technology，2019，51（1）：189-196.
[12] 陆航，师智斌，刘忠宝.融合用户兴趣和评分差异的协同过滤推荐算法[J].计算机工程与应用，2020，56（7）：24-29.
LU H，SHI Z B，LIU Z B.Collaboration filtering recommendation algorithm based on user interest and ratings difference[J].Computer Engineering and Applications，2020，56（7）：24-29.
[13] CRAMER H，EVERS V，RAMLAL S，et al.The effects of transparency on trust in and acceptance of a content-based art recommender[J].User Modeling and User Adapted Interaction，2008，18（5）：455-496.
[14] PINATA W，TIFFANY Y T.The role of user mood in movie recommendations[J].Expert Systems with Applications，2010，37：6086-6092.
[15] JONICE O，CARLA D，ANA C A.A recommendation approach for consuming linked open data[J].Expert Systems with Applications，2017，72：407-420.