基于Word2vec的图书馆推荐系统多样性问题应用研究

Application Research Based on Word2vec Diversity in Library Recommender System

图书馆个性化推荐系统强调推荐的精准性,无法满足读者的多样性需求。本文将深度学习算法引入图书馆推荐系统,探讨推荐多样性的问题。首先,依据历史借阅数据,结合时间序列,形成读者借阅行为的共现矩阵;然后将共现矩阵看作上下文的语境,利用Word2vec的潜在语义分析特性,识别读者可能的兴趣;最后挖掘读者可能的兴趣,并提供多样性的推荐结果。本文选取上海浦东图书馆541万余条借阅数据进行实验,对比关联分析的结果,验证了该方法在推荐多样性方面具有较好的效果。

The library’s personalized recommendation system emphasizes recommendation accuracy,which cannot meet readers’ need for diversity.In this paper,we introduce the deep learning algorithm into the library recommendation system,and discuss the problem of the recommendation diversity.First,according to the historical checkout data,we form the reader’s borrowing behavior co-occurrence matrix combined with time series;then,the co-occurrence matrix is regarded as context,and we identify the reader’s potential interest by using the potential semantic analysis of Word2vec;finally,we identify the reader’s interest and provide varied recommendation results.In this paper,we use more than 5 410 000 data from the Pudong Library in Shanghai to experiment.Comparing to association rule algorithm,we find that our method has a good impact on the recommendation diversity.