【摘要】：隨著互聯(lián)網(wǎng)的發(fā)展,大量信息出現(xiàn)在人們的視野中。信息爆炸使人們能更方便地接收多方面的信息。但與此同時(shí),有價(jià)值信息的快速獲取也變得更加困難。為了解決這種情況,人們通常在獲取信息時(shí)先對(duì)其進(jìn)行檢索和過(guò)濾。搜索引擎作為信息檢索技術(shù)的代表可以很好地幫助人們從海量的信息中檢索出有用的信息。但當(dāng)搜索的關(guān)鍵詞不能恰當(dāng)?shù)姆磻?yīng)出搜索需求時(shí),查詢的結(jié)果就會(huì)令人失望。而個(gè)性化推薦作為信息過(guò)濾中典型的應(yīng)用正好可以彌補(bǔ)這方面的不足。目前主流的推薦算法包括基于內(nèi)容的推薦、協(xié)同過(guò)濾推薦、基于規(guī)則的推薦、混合推薦等。在這些推薦算法中,協(xié)同過(guò)濾技術(shù)是實(shí)際應(yīng)用中最為廣泛的推薦技術(shù)。它根據(jù)產(chǎn)品評(píng)分和相似性算法選出與目標(biāo)用戶有著相似興趣偏好的用戶集合,再?gòu)倪@些相似用戶評(píng)價(jià)高的產(chǎn)品中選出那些目標(biāo)用戶尚未評(píng)價(jià)過(guò)的產(chǎn)品推薦給用戶。但傳統(tǒng)的協(xié)同過(guò)濾沒(méi)有考慮到標(biāo)簽對(duì)推薦結(jié)果的影響,只根據(jù)用戶對(duì)資源的評(píng)分單方面挖掘用戶興趣,未能對(duì)用戶興趣進(jìn)行有效劃分,同時(shí)也忽略了用戶興趣隨著時(shí)間推移發(fā)生的變化。為了解決以上問(wèn)題,本文進(jìn)行了如下研究:1.針對(duì)傳統(tǒng)的協(xié)同過(guò)濾忽略了用戶喜好因時(shí)間推移而發(fā)生的改變,本文提出了一種融合時(shí)間因子的協(xié)同過(guò)濾推薦算法。該算法考慮了產(chǎn)品評(píng)分時(shí)間和不同時(shí)段產(chǎn)品受關(guān)注的程度對(duì)用戶興趣偏好的影響,分別建立了時(shí)間遺忘模型和時(shí)間窗口模型,并把這兩種模型融合,生成時(shí)間因子。之后,在用戶相似度的計(jì)算中通過(guò)時(shí)間因子對(duì)產(chǎn)品評(píng)分進(jìn)行時(shí)間上的過(guò)濾,從而能夠更加準(zhǔn)確地計(jì)算出目標(biāo)用戶的相似用戶,減小因時(shí)間因素造成的推薦質(zhì)量的下降。實(shí)驗(yàn)表明該法能有效地適應(yīng)用戶興趣變化,提高智能Web系統(tǒng)在推薦中的準(zhǔn)確率。2.考慮到用戶與標(biāo)簽之間的關(guān)系,本文提出了一種基于標(biāo)簽聚類(lèi)和興趣劃分的協(xié)同過(guò)濾推薦算法。該算法考慮了標(biāo)簽和用戶評(píng)分對(duì)推薦結(jié)果的影響,通過(guò)標(biāo)簽聚類(lèi)劃分用戶興趣,并分別在標(biāo)簽和產(chǎn)品評(píng)分上對(duì)目標(biāo)用戶的相似用戶進(jìn)行選擇。同時(shí),在計(jì)算標(biāo)簽和產(chǎn)品評(píng)分權(quán)重時(shí)融入了時(shí)間因子,以適應(yīng)用戶的興趣變化。實(shí)驗(yàn)部分,在Movielens數(shù)據(jù)集上通過(guò)交叉驗(yàn)證和與其它推薦算法的對(duì)比說(shuō)明了該算法能有效的劃分用戶興趣,減少時(shí)間因素對(duì)推薦質(zhì)量的影響,提高推薦的準(zhǔn)確度。
[Abstract]:With the development of the Internet, a lot of information appears in people's vision. Information explosion makes it easier for people to receive many kinds of information. But at the same time, rapid access to valuable information has become more difficult. In order to solve this problem, information is usually retrieved and filtered. As the representative of information retrieval technology, search engine can help people to retrieve useful information from a large amount of information. However, when the search keywords do not reflect the search requirements properly, the results of the query will be disappointing. Personalized recommendation as a typical application of information filtering can make up for this deficiency. The current mainstream recommendation algorithms include content-based recommendation, collaborative filtering recommendation, rule-based recommendation, mixed recommendation and so on. Among these recommendation algorithms, collaborative filtering is the most widely used recommendation technology. According to the product score and similarity algorithm, the users with similar interests and preferences are selected, and those products that have not been evaluated by the target users are selected from the products with high evaluation. However, the traditional collaborative filtering does not take into account the impact of labels on the recommended results, only according to the user's score of resources unilaterally mining user interest, failed to effectively divide user interest. It also ignores the changes in user interest over time. In order to solve the above problems, this paper has carried out the following research: 1. In view of the fact that the traditional collaborative filtering neglects the change of user preferences due to the passage of time, a collaborative filtering recommendation algorithm combining time factors is proposed in this paper. Taking into account the influence of product scoring time and the degree of product attention in different time periods on user interest preference, the time forgetting model and time window model are established, and the two models are combined to generate time factors. After that, in the calculation of user similarity, time factor is used to filter the product score, so that the similar users of target users can be calculated more accurately, and the quality of recommendation caused by time factors can be reduced. Experiments show that this method can effectively adapt to the change of user interest and improve the accuracy of intelligent Web system in recommendation. 2. Considering the relationship between users and tags, this paper proposes a collaborative filtering recommendation algorithm based on tag clustering and interest partition. The algorithm takes into account the influence of labels and user ratings on the recommended results, classifies user interests by label clustering, and selects similar users of target users in terms of labels and product ratings. At the same time, time factor is incorporated in the calculation of label and product rating weight to adapt to the change of user's interest. Experimental results show that the proposed algorithm can effectively divide user interest reduce the influence of time factors on recommendation quality and improve recommendation accuracy through cross-validation and comparison with other recommendation algorithms on Movielens data set.
【學(xué)位授予單位】：安徽理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TP391.3

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