【摘要】：網(wǎng)絡(luò)技術(shù)的飛速發(fā)展影響并改變著人類的生活。網(wǎng)絡(luò)技術(shù)的先進(jìn)性已經(jīng)不再是網(wǎng)絡(luò)能給讓人們干什么,而是體現(xiàn)在人們?nèi)绾卧诰W(wǎng)絡(luò)上更好的體驗(yàn)生活。這是技術(shù)由簡(jiǎn)單到繁瑣,由粗放到精細(xì)的轉(zhuǎn)變。信息過(guò)載問(wèn)題儼然已經(jīng)影響了人們網(wǎng)絡(luò)生活的舒適度,而推薦系統(tǒng)的誕生為滿足人們的個(gè)性化需求帶來(lái)福音。協(xié)同過(guò)濾和基于內(nèi)容是兩種主要的推薦算法,雖然在不同領(lǐng)域都已經(jīng)被應(yīng)用,但是仍存在自適應(yīng)能力不強(qiáng),個(gè)性化推薦能力不足等問(wèn)題,另外,兩種算法雖然優(yōu)缺點(diǎn)比較互補(bǔ),但是由于受到非文本項(xiàng)目的特征提取困難的束縛,基于內(nèi)容推薦算法一般只用于文本類項(xiàng)目的推薦系統(tǒng)之中,因此,當(dāng)前融合兩種推薦算法的混合推薦的普適度不高。為了提高推薦算法的推薦質(zhì)量,發(fā)揮協(xié)同過(guò)濾與基于內(nèi)容兩種推薦算法各自的優(yōu)勢(shì),提出了一種混合推薦算法。該算法的主算法是協(xié)同過(guò)濾算法,在主算法尋找可信鄰居時(shí)融入了基于內(nèi)容推薦的算法思想,最后利用可信鄰居協(xié)同推薦。該策略在構(gòu)建中的創(chuàng)新點(diǎn)主要包括:第一,引入項(xiàng)目熱度優(yōu)化皮爾遜相關(guān)系數(shù)。第二,以項(xiàng)目標(biāo)簽作為非文本項(xiàng)目的屬性特征,并給出了為用戶建立二維興趣模型,衡量興趣模型相似度的方法。第三,結(jié)合興趣模型相似度公式的結(jié)構(gòu)特征,提出了利用方差來(lái)求解相似度權(quán)重系數(shù)的方法。最終的實(shí)驗(yàn)證明,混合推薦方法提高了推薦質(zhì)量,為有效方法,與兩個(gè)現(xiàn)存的混合策略相比具有推薦優(yōu)勢(shì)。而且在混合算法模型中的每一步優(yōu)化以及計(jì)算方法的有效性都通過(guò)實(shí)驗(yàn)得以驗(yàn)證。由于算法以項(xiàng)目標(biāo)簽來(lái)提取項(xiàng)目特征,所以對(duì)于非文本類項(xiàng)目的混合推薦具有一定的普適性。
[Abstract]:The rapid development of network technology influences and changes the human life. The advancement of network technology is no longer what the network can give people, but how people experience life better on the network. This is the technology from simple to cumbersome, from extensive to fine transformation. The problem of information overload has affected the comfort of people's online life, and the birth of recommendation system has brought good news to meet the personal needs of people. Collaborative filtering and content-based recommendation algorithms are two major recommendation algorithms. Although they have been applied in different fields, there are still some problems such as weak adaptive ability and insufficient personalized recommendation ability. In addition, the two algorithms complement each other in their advantages and disadvantages. However, due to the difficulty of feature extraction of non-text items, the content-based recommendation algorithm is generally only used in the recommendation system of text items. In order to improve the recommendation quality of the recommendation algorithm, a hybrid recommendation algorithm is proposed to give full play to the advantages of both collaborative filtering and content-based recommendation algorithms. The main algorithm of this algorithm is the collaborative filtering algorithm. When the main algorithm finds trusted neighbors, it integrates the idea of content-based recommendation. Finally, the trusted neighbor collaborative recommendation is used. The innovation of the strategy includes: first, introducing project heat to optimize Pearson correlation coefficient. Secondly, the item label is taken as the attribute feature of non-text items, and the method of building two-dimensional interest model for users to measure the similarity of interest model is given. Thirdly, based on the structural features of the similarity formula of interest model, a method is proposed to solve the similarity weight coefficient by using variance. The final experiments show that the hybrid recommendation method improves the quality of the recommendation. It is an effective method and has the advantages of recommendation compared with the two existing hybrid strategies. Moreover, the effectiveness of each step optimization and calculation method in the hybrid algorithm model is verified by experiments. Because the project feature is extracted by the item label, the mixed recommendation of non-text item has a certain universality.
【學(xué)位授予單位】：天津財(cái)經(jīng)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP391.3

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