本文選題：協(xié)同過濾 + 概率矩陣分解模型��； 參考：《內(nèi)蒙古大學(xué)》2017年碩士論文

【摘要】：隨著信息過載的產(chǎn)生,在越來越開放的互聯(lián)網(wǎng)中,想要獲取我們真正需要的信息變得越來越困難,個(gè)性化推薦的出現(xiàn)有效地解決了信息過載的問題,主動(dòng)為用戶推薦其感興趣的信息和商品。協(xié)同過濾是個(gè)性化推薦中使用最廣泛的方法,但由于協(xié)同過濾通常只將用戶-項(xiàng)目評(píng)分作為推薦的唯一數(shù)據(jù)信息,因此存在冷啟動(dòng)、數(shù)據(jù)稀疏等問題。本文在一個(gè)結(jié)合主題模型(LDA)和矩陣分解模型(PMF)的分層貝葉斯模型——協(xié)同主題回歸模型(CTR)的基礎(chǔ)上,使用其中的主題模型對(duì)項(xiàng)目的標(biāo)簽信息進(jìn)行處理,并對(duì)概率矩陣分解模型進(jìn)行改進(jìn),不僅考慮用戶-項(xiàng)目的評(píng)分信息,還將用戶的信任關(guān)系、時(shí)間序列、項(xiàng)目的標(biāo)簽信息等其他對(duì)推薦具有影響的因素加入到模型中。用戶可以根據(jù)好友及其信任用戶的推薦選擇自己感興趣的商品,基于時(shí)間因素的用戶評(píng)價(jià)先后關(guān)系也會(huì)對(duì)用戶的選擇產(chǎn)生影響,將時(shí)間序列對(duì)用戶關(guān)系的影響與好友間的信任度線性融合并加入到PMF模型中,生成用戶潛在特征向量。此外用戶對(duì)項(xiàng)目定義的標(biāo)簽信息在一定程度上也可以反映用戶的偏好,因此利用主題模型LDA處理項(xiàng)目的標(biāo)簽文本信息得到項(xiàng)目的潛在特征向量。最后將改進(jìn)的LDA和PMF模型的特點(diǎn)融合在CTR模型中,根據(jù)CTR模型的原理提出N-CTR模型,并采用梯度下降方法和最大期望算法最優(yōu)化用戶、項(xiàng)目潛在特征矩陣和主題分布向量,進(jìn)行評(píng)分預(yù)測。在Last.fm數(shù)據(jù)集上進(jìn)行實(shí)驗(yàn),實(shí)驗(yàn)結(jié)果顯示混合了用戶信任關(guān)系、時(shí)間序列、項(xiàng)目標(biāo)簽信息和評(píng)分?jǐn)?shù)據(jù)等多因素的N-CTR模型的推薦準(zhǔn)確率MAE和RMSE比只采用用戶-項(xiàng)目評(píng)分?jǐn)?shù)據(jù)的PMF模型分別提高了 7.36%和7.94%,說明本模型在一定程度上緩解了推薦過程中的數(shù)據(jù)稀疏問題且該模型比傳統(tǒng)的協(xié)同過濾推薦算法準(zhǔn)確率更高。
[Abstract]:With the emergence of information overload, in the more and more open Internet, it becomes more and more difficult to obtain the information we really need. The emergence of personalized recommendation effectively solves the problem of information overload.Actively recommend information and products of interest to users.Collaborative filtering is the most widely used method in personalized recommendation, but because collaborative filtering usually only takes user-item score as the only data information, there are some problems such as cold start and sparse data.In this paper, based on a hierarchical Bayesian model-cooperative theme regression model (CTRR), which combines the topic model (LDA) and the matrix decomposition model (PMF), we use the topic model to process the label information of the project.The probabilistic matrix decomposition model is improved to consider not only the user-item scoring information, but also the user's trust relationship, time series, item label information and other factors that affect the recommendation.Users can choose the items they are interested in according to the recommendation of their friends and trusted users, and the relationship of users' evaluation priority based on time factors will also have an impact on the choice of users.The influence of time series on user relationship and the trust degree among friends are linear fused and added to the PMF model to generate the potential feature vector of the user.In addition, the label information of the project definition can also reflect the user's preference to a certain extent, so the potential feature vector of the item can be obtained by using the topic model LDA to process the tag text information of the item.Finally, the features of the improved LDA and PMF models are fused into the CTR model. According to the principle of the CTR model, the N-CTR model is proposed, and the gradient descent method and the maximum expectation algorithm are used to optimize the users, the project potential feature matrix and the topic distribution vector.Predict the score.The experimental results on Last.fm data set show that the user trust relationship is mixed, and time series are used.The recommendation accuracy of N-CTR model with multiple factors, such as item label information and rating data, is 7.36% and 7.94% higher than that of PMF model which only uses user-item rating data. It shows that this model alleviates recommendation to some extent.The model is more accurate than the traditional collaborative filtering recommendation algorithm.
【學(xué)位授予單位】：內(nèi)蒙古大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.3

【參考文獻(xiàn)】

相關(guān)期刊論文 前5條

1 唐曉波;向坤;;基于LDA模型和微博熱度的熱點(diǎn)挖掘[J];圖書情報(bào)工作;2014年05期

2 王振振;何明;杜永萍;;基于LDA主題模型的文本相似度計(jì)算[J];計(jì)算機(jī)科學(xué);2013年12期

3 孫光福;吳樂;劉淇;朱琛;陳恩紅;;基于時(shí)序行為的協(xié)同過濾推薦算法[J];軟件學(xué)報(bào);2013年11期

4 曾春,邢春曉,周立柱;個(gè)性化服務(wù)技術(shù)綜述[J];軟件學(xué)報(bào);2002年10期

5 顧r，

本文編號(hào)：1755121