本文關(guān)鍵詞： 推薦系統(tǒng) 協(xié)同過濾 隱式商品關(guān)系 顯式商品關(guān)系 關(guān)聯(lián)規(guī)則 商品類別 矩陣分解　出處：《燕山大學(xué)》2016年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著互聯(lián)網(wǎng)中的信息量劇增,用戶個(gè)性化需求日趨急切,推薦系統(tǒng)已經(jīng)成為信息過濾的熱門工具。協(xié)同過濾推薦算法是應(yīng)用最廣泛的推薦算法。為進(jìn)一步提升推薦結(jié)果的準(zhǔn)確度,大量基于用戶關(guān)系的協(xié)同過濾算法被提出,例如基于社交網(wǎng)絡(luò)用戶關(guān)系的推薦算法等。然而,被眾多的研究者忽略的商品關(guān)系對(duì)于提高推薦算法的準(zhǔn)確度卻有更好的效果。本文旨在將商品關(guān)系融合到傳統(tǒng)的協(xié)同過濾推薦算法中以進(jìn)一步提升推薦效果。通常商品關(guān)系可分為隱式關(guān)系和顯式關(guān)系。首先,為解決利用相似度算法發(fā)掘隱式商品關(guān)系方法中存在的不足,例如對(duì)稱性以及不能同時(shí)考慮多個(gè)商品間的關(guān)系等問題,本文利用改進(jìn)的關(guān)聯(lián)規(guī)則技術(shù)挖掘一對(duì)一及多對(duì)一的隱式商品關(guān)系,并以此關(guān)系作為正則項(xiàng)融合到矩陣分解模型中。同時(shí),為進(jìn)一步研究不同商品關(guān)系對(duì)提升推薦效果的影響,本文設(shè)計(jì)了四種不同的選取隱式商品關(guān)系的策略。其次,在真實(shí)的電子商務(wù)系統(tǒng)中,商品之間往往存在顯式的關(guān)系:具有相似特征的商品會(huì)被分配到同一類別中,反之則被分配到不同類別中。同時(shí)考慮到兩種不同的情形:一件商品可能只屬于一個(gè)類別即一對(duì)一商品類別關(guān)系或是同時(shí)屬于多個(gè)類別即一對(duì)多商品類別關(guān)系,本文提出了一個(gè)新穎的基于矩陣分解的商品推薦模型。其中,有別于傳統(tǒng)的矩陣分解方法,我們加入類別信息來更準(zhǔn)確地描述用戶和商品潛在特征向量,并將顯式商品關(guān)系用作正則項(xiàng)加以限制商品特征向量的學(xué)習(xí)過程。最后,為了驗(yàn)證提出算法的合理性及有效性,本文在四個(gè)真實(shí)世界的數(shù)據(jù)集上做了嚴(yán)密且充分的對(duì)比實(shí)驗(yàn)。
[Abstract]:With the rapid increase in the amount of information in the Internet, the personalized needs of users are becoming increasingly urgent. Recommendation system has become a popular tool for information filtering. Collaborative filtering recommendation algorithm is the most widely used recommendation algorithm. In order to further improve the accuracy of recommendation results. A large number of collaborative filtering algorithms based on user relationships have been proposed, such as recommendation algorithms based on user relationships in social networks. The commodity relationship neglected by many researchers has a better effect on improving the accuracy of recommendation algorithm. This paper aims to integrate commodity relationship into traditional collaborative filtering recommendation algorithm to further improve the recommendation effect. The constant commodity relation can be divided into implicit relation and explicit relation. In order to solve the problems of using similarity algorithm to explore the implicit commodity relations, such as symmetry and cannot consider the relationship between multiple commodities at the same time, and so on. In this paper, the improved association rule technique is used to mine the one-to-one and many-to-one implicit commodity relations, and the relations are fused into the matrix decomposition model as regular terms. In order to further study the impact of different commodity relationships on the promotion of recommendations, this paper designs four different strategies to select implicit commodity relationships. Secondly, in the real e-commerce system. There is often an explicit relationship between goods: goods with similar characteristics are assigned to the same category. On the contrary, they are assigned to different categories. Two different situations are considered: a commodity may belong to only one category, that is, a one-to-one commodity category relationship, or multiple categories, that is, a one-to-many commodity category relationship at the same time. In this paper, a novel commodity recommendation model based on matrix decomposition is proposed. Different from the traditional matrix decomposition method, we add class information to describe the potential feature vectors of users and commodities more accurately. The explicit commodity relationship is used as a regular term to limit the learning process of the commodity feature vector. Finally, in order to verify the rationality and effectiveness of the proposed algorithm. In this paper, four real world data sets are closely and fully contrasted with each other.
【學(xué)位授予單位】：燕山大學(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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