基于用戶偏好與商品屬性情感匹配的圖書個性化推薦研究
發(fā)布時間：2018-11-07 17:57
【摘要】：【目的】識別并獲取細粒度的用戶偏好信息,優(yōu)化圖書個性化推薦的效果�！痉椒ā渴褂们楦蟹治龇椒▽τ脩魣D書評論進行屬性層文本挖掘,通過用戶本身的圖書評論獲取用戶對圖書屬性的偏好;基于每本圖書的所有評論的情感計算獲得其屬性評分;將用戶偏好矩陣、圖書屬性得分矩陣進行匹配,從而實現用戶對圖書屬性情感偏好的個性化推薦。【結果】利用亞馬遜圖書評論數據作為數據來源分別對傳統(tǒng)的協(xié)同過濾方法與本文提出的推薦方法進行實驗對比。結果表明,本文提出的方法在準確性、召回率、覆蓋率上分別提高了0.030、0.097、0.2812�！揪窒蕖课纯紤]時間因素對用戶偏好的影響,并且屬性類型的全面程度受亞馬遜圖書評論數量和質量的限制。【結論】本文計算用戶對圖書屬性的情感得分,得到細粒度的用戶偏好信息,并通過與圖書屬性的得分進行匹配,提升了圖書個性化推薦的效果。
[Abstract]:[objective] to identify and obtain fine-grained user preference information and optimize the effect of personalized book recommendation. The user's preference for the book attribute is obtained through the user's own book review. The attribute score was obtained based on the emotional calculation of all the comments in each book; Matching user preference matrix, book attribute score matrix, [results] the traditional collaborative filtering method is used as the data source to compare the traditional collaborative filtering method with the recommendation method proposed in this paper. [results] using Amazon book review data as the data source, we can make a comparison between the traditional collaborative filtering method and the recommendation method proposed in this paper. The results show that the accuracy, recall rate and coverage rate of the proposed method are increased by 0.030 / 0.097 / 0.2812 respectively. [limitation] the influence of time factors on user preference is not considered. And the comprehensive degree of attribute type is limited by the quantity and quality of Amazon book review. [conclusion] this paper calculates the user's emotion score to the book attribute, and obtains the fine granularity user preference information. And by matching with the score of book attributes, the effect of personalized book recommendation is improved.
【作者單位】： 華中師范大學信息管理學院;華中師范大學青少年網絡心理與行為教育部重點實驗室;
【基金】：國家自然科學基金項目“基于用戶偏好感知的Saa S服務選擇優(yōu)化研究”(項目編號:71271099),國家自然科學基金項目“基于屏幕視覺熱區(qū)的網絡用戶偏好提取及交互式個性化推薦研究”(項目編號:71571084)的研究成果之一
【分類號】：TP391.3
本文編號：2317128
[Abstract]:[objective] to identify and obtain fine-grained user preference information and optimize the effect of personalized book recommendation. The user's preference for the book attribute is obtained through the user's own book review. The attribute score was obtained based on the emotional calculation of all the comments in each book; Matching user preference matrix, book attribute score matrix, [results] the traditional collaborative filtering method is used as the data source to compare the traditional collaborative filtering method with the recommendation method proposed in this paper. [results] using Amazon book review data as the data source, we can make a comparison between the traditional collaborative filtering method and the recommendation method proposed in this paper. The results show that the accuracy, recall rate and coverage rate of the proposed method are increased by 0.030 / 0.097 / 0.2812 respectively. [limitation] the influence of time factors on user preference is not considered. And the comprehensive degree of attribute type is limited by the quantity and quality of Amazon book review. [conclusion] this paper calculates the user's emotion score to the book attribute, and obtains the fine granularity user preference information. And by matching with the score of book attributes, the effect of personalized book recommendation is improved.
【作者單位】： 華中師范大學信息管理學院;華中師范大學青少年網絡心理與行為教育部重點實驗室;
【基金】：國家自然科學基金項目“基于用戶偏好感知的Saa S服務選擇優(yōu)化研究”(項目編號:71271099),國家自然科學基金項目“基于屏幕視覺熱區(qū)的網絡用戶偏好提取及交互式個性化推薦研究”(項目編號:71571084)的研究成果之一
【分類號】：TP391.3
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