【摘要】：伴隨信息技術(shù)和互聯(lián)網(wǎng)應(yīng)用的發(fā)展,網(wǎng)絡(luò)上的信息發(fā)生了爆炸式的增長(zhǎng)。然而面對(duì)海量信息,個(gè)人用戶(hù)所能接觸到的不過(guò)滄海一粟。在這樣需求的推動(dòng)下,個(gè)性化推薦技術(shù)(Personal Recommendation Technology)應(yīng)運(yùn)而生。傳統(tǒng)的推薦技術(shù)僅僅是依靠單一領(lǐng)域的信息為本領(lǐng)域的用戶(hù)進(jìn)行推薦。然而隨著互聯(lián)網(wǎng)信息的發(fā)展,越來(lái)越多的信息平臺(tái)交互連接,用戶(hù)也越來(lái)越不滿(mǎn)足于單一領(lǐng)域的信息來(lái)源,傳統(tǒng)的單一領(lǐng)域推薦技術(shù)一直存在著數(shù)據(jù)稀疏、冷啟動(dòng)等問(wèn)題,為了提高個(gè)性化推薦系統(tǒng)的準(zhǔn)確性和多樣性,跨領(lǐng)域信息推薦技術(shù)成為了當(dāng)前的一個(gè)研究熱點(diǎn)�？珙I(lǐng)域推薦的優(yōu)勢(shì)在于能夠綜合分析來(lái)自多個(gè)領(lǐng)域的數(shù)據(jù),對(duì)用戶(hù)或者預(yù)測(cè)對(duì)象進(jìn)行更加充分的建模,提高推薦結(jié)果的準(zhǔn)確性;還能夠?yàn)橛脩?hù)提供來(lái)自不同領(lǐng)域的預(yù)測(cè)對(duì)象的建議,提高推薦結(jié)果的多樣性�；谝陨戏N種優(yōu)勢(shì),跨領(lǐng)域的推薦技術(shù)研究成為工業(yè)界和學(xué)術(shù)界的研究熱點(diǎn)。一般的推薦算法,無(wú)論是單領(lǐng)域或是跨領(lǐng)域,主要是基于用戶(hù)的評(píng)分?jǐn)?shù)據(jù)來(lái)實(shí)現(xiàn)的,大部分情況下推薦算法被簡(jiǎn)化為了評(píng)分預(yù)測(cè)問(wèn)題。然而這種形式使得推薦算法一直受制于評(píng)分?jǐn)?shù)據(jù)稀疏的問(wèn)題。因此,在推薦算法的發(fā)展過(guò)程中,其他類(lèi)型的數(shù)據(jù)源也被納入到考慮之中,期望以此來(lái)提高推薦算法的表現(xiàn)。其中,基于標(biāo)簽信息的推薦算法一直是研究的熱點(diǎn)之一。標(biāo)簽是一種幫助用戶(hù)描述和分類(lèi)信息的關(guān)鍵字。用戶(hù)可以自由的選擇和描述最符合自己情況的標(biāo)簽,因此標(biāo)簽是一種可以強(qiáng)烈反應(yīng)用戶(hù)興趣的信息。當(dāng)前在各個(gè)網(wǎng)站和平臺(tái)中充滿(mǎn)了豐富的標(biāo)簽信息,這也為結(jié)合標(biāo)簽的推薦系統(tǒng)提供了可能。本文充分利用了多領(lǐng)域的標(biāo)簽信息,從而有效挖掘用戶(hù)在不同領(lǐng)域中對(duì)信息對(duì)象的評(píng)價(jià)方式,提高了跨領(lǐng)域信息推薦的準(zhǔn)確性,在一定程度上擴(kuò)展了標(biāo)簽信息利用的新方式。最后,在真實(shí)數(shù)據(jù)集上驗(yàn)證了本文提出的跨領(lǐng)域推薦算法的有效性。
[Abstract]:With the development of information technology and Internet application, the information on the network has explosive growth. However, in the face of massive information, personal users can access but a drop in the ocean. Driven by this demand, personalized recommendation technology (Personal Recommendation Technology) came into being. Traditional recommendation technology only relies on single domain information to recommend users in this field. However, with the development of Internet information, more and more information platforms are connected, and users are not satisfied with the information source in a single field. The traditional single-domain recommendation technology has many problems, such as sparse data, cold start and so on. In order to improve the accuracy and diversity of personalized recommendation system, cross-domain information recommendation technology has become a research hotspot. The advantage of cross-domain recommendation is that it can analyze the data from many fields synthetically, model users or forecast objects more fully, and improve the accuracy of recommendation results. It can also provide users with suggestions from different areas of prediction objects, and improve the diversity of recommended results. Based on the above advantages, cross-domain recommendation technology research has become a research hotspot in industry and academia. General recommendation algorithms, whether single-domain or cross-domain, are mainly implemented on the basis of the user's rating data. In most cases, the recommendation algorithm is simplified to the problem of score prediction. However, this form makes the recommendation algorithm always subject to the problem of sparse rating data. Therefore, in the development of recommendation algorithms, other types of data sources are also taken into account in order to improve the performance of recommendation algorithms. Among them, the recommendation algorithm based on label information has been one of the hot research topics. Tags are keywords that help users describe and classify information. Users are free to choose and describe labels that best suit their needs, so tags are information that strongly reflects the user's interest. At present, various websites and platforms are full of rich tag information, which also provides the possibility for tag-based recommendation system. In this paper, we make full use of multi-domain tag information, so as to effectively mine users' evaluation methods of information objects in different fields, improve the accuracy of cross-domain information recommendation, and extend the new way of label information utilization to a certain extent. Finally, the effectiveness of the proposed cross-domain recommendation algorithm is verified on real data sets.
【學(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類(lèi)號(hào)】：TP391.3

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