【摘要】：【目的】識(shí)別并獲取細(xì)粒度的用戶偏好信息,優(yōu)化圖書個(gè)性化推薦的效果�！痉椒ā渴褂们楦蟹治龇椒▽�(duì)用戶圖書評(píng)論進(jìn)行屬性層文本挖掘,通過用戶本身的圖書評(píng)論獲取用戶對(duì)圖書屬性的偏好;基于每本圖書的所有評(píng)論的情感計(jì)算獲得其屬性評(píng)分;將用戶偏好矩陣、圖書屬性得分矩陣進(jìn)行匹配,從而實(shí)現(xiàn)用戶對(duì)圖書屬性情感偏好的個(gè)性化推薦�！窘Y(jié)果】利用亞馬遜圖書評(píng)論數(shù)據(jù)作為數(shù)據(jù)來源分別對(duì)傳統(tǒng)的協(xié)同過濾方法與本文提出的推薦方法進(jìn)行實(shí)驗(yàn)對(duì)比。結(jié)果表明,本文提出的方法在準(zhǔn)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(shí)間因素對(duì)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(píng)論數(shù)量和質(zhì)量的限制�！窘Y(jié)論】本文計(jì)算用戶對(duì)圖書屬性的情感得分,得到細(xì)粒度的用戶偏好信息,并通過與圖書屬性的得分進(jìn)行匹配,提升了圖書個(gè)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(xué)信息管理學(xué)院;華中師范大學(xué)青少年網(wǎng)絡(luò)心理與行為教育部重點(diǎn)實(shí)驗(yàn)室;
【基金】：國家自然科學(xué)基金項(xiàng)目“基于用戶偏好感知的Saa S服務(wù)選擇優(yōu)化研究”(項(xiàng)目編號(hào):71271099),國家自然科學(xué)基金項(xiàng)目“基于屏幕視覺熱區(qū)的網(wǎng)絡(luò)用戶偏好提取及交互式個(gè)性化推薦研究”(項(xiàng)目編號(hào):71571084)的研究成果之一
【分類號(hào)】：TP391.3

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本文編號(hào)：2317128