【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,有價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先對其進行檢索和過濾。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,查詢的結(jié)果就會令人失望。而個性化推薦作為信息過濾中典型的應(yīng)用正好可以彌補這方面的不足。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)同過濾技術(shù)是實際應(yīng)用中最為廣泛的推薦技術(shù)。它根據(jù)產(chǎn)品評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,再從這些相似用戶評價高的產(chǎn)品中選出那些目標(biāo)用戶尚未評價過的產(chǎn)品推薦給用戶。但傳統(tǒng)的協(xié)同過濾沒有考慮到標(biāo)簽對推薦結(jié)果的影響,只根據(jù)用戶對資源的評分單方面挖掘用戶興趣,未能對用戶興趣進行有效劃分,同時也忽略了用戶興趣隨著時間推移發(fā)生的變化。為了解決以上問題,本文進行了如下研究:1.針對傳統(tǒng)的協(xié)同過濾忽略了用戶喜好因時間推移而發(fā)生的改變,本文提出了一種融合時間因子的協(xié)同過濾推薦算法。該算法考慮了產(chǎn)品評分時間和不同時段產(chǎn)品受關(guān)注的程度對用戶興趣偏好的影響,分別建立了時間遺忘模型和時間窗口模型,并把這兩種模型融合,生成時間因子。之后,在用戶相似度的計算中通過時間因子對產(chǎn)品評分進行時間上的過濾,從而能夠更加準(zhǔn)確地計算出目標(biāo)用戶的相似用戶,減小因時間因素造成的推薦質(zhì)量的下降。實驗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)簽聚類和興趣劃分的協(xié)同過濾推薦算法。該算法考慮了標(biāo)簽和用戶評分對推薦結(jié)果的影響,通過標(biāo)簽聚類劃分用戶興趣,并分別在標(biāo)簽和產(chǎn)品評分上對目標(biāo)用戶的相似用戶進行選擇。同時,在計算標(biāo)簽和產(chǎn)品評分權(quán)重時融入了時間因子,以適應(yīng)用戶的興趣變化。實驗部分,在Movielens數(shù)據(jù)集上通過交叉驗證和與其它推薦算法的對比說明了該算法能有效的劃分用戶興趣,減少時間因素對推薦質(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)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.3

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