發(fā)布時(shí)間：2018-05-17 23:07

  本文選題：異構(gòu)隱式反饋 + 推薦系統(tǒng)　； 參考：《浙江大學(xué)》2017年碩士論文

【摘要】：互聯(lián)網(wǎng)時(shí)代的大潮帶來(lái)了數(shù)據(jù)的爆炸式增長(zhǎng),大數(shù)據(jù)的概念也在過(guò)去的幾年內(nèi)持續(xù)升溫,用戶從海量數(shù)據(jù)中獲得有用信息的代價(jià)也越來(lái)越高。推薦系統(tǒng)為解決這一問(wèn)題帶來(lái)了曙光。推薦系統(tǒng)通過(guò)用戶畫像、用戶的歷史行為數(shù)據(jù)及物品的相關(guān)數(shù)據(jù)等對(duì)用戶的偏好進(jìn)行建模,從而幫助用戶快速發(fā)現(xiàn)其真正感興趣的信息。目前非時(shí)間敏感的推薦算法通常認(rèn)為用戶的偏好不隨時(shí)間變化,在此假設(shè)下進(jìn)行相關(guān)算法的研究。然而,在真實(shí)的世界中,用戶的偏好隨著時(shí)間的推移不斷變化。時(shí)間敏感的推薦算法存在沒(méi)有考慮用戶長(zhǎng)期、有重復(fù)性的偏好,計(jì)算效率不高等問(wèn)題。因此,研究用戶的動(dòng)態(tài)偏好,對(duì)于提升個(gè)性化推薦算法的準(zhǔn)確度、召回率等具有十分重要的意義。本文首先對(duì)電商環(huán)境下用戶的動(dòng)態(tài)偏好進(jìn)行了詳細(xì)分析,然后圍繞基于用戶動(dòng)態(tài)偏好的異構(gòu)隱式反饋推薦算法展開(kāi)研究,主要工作包括:1)提出一種基于用戶偏好置信度時(shí)間衰減的推薦算法時(shí)間敏感的推薦算法通常會(huì)采用時(shí)間衰減的方式,通過(guò)降低用戶較遠(yuǎn)時(shí)間前的評(píng)分值來(lái)預(yù)測(cè)用戶未來(lái)購(gòu)買興趣。此外,現(xiàn)有的研究主要是將時(shí)間衰減用在相對(duì)簡(jiǎn)單的基于用戶的協(xié)同過(guò)濾算法中,尚沒(méi)有應(yīng)用于基于模型的推薦算法中。針對(duì)上述兩個(gè)問(wèn)題,本文提出一種基于置信度時(shí)間衰減的用戶偏好度量方法。我們認(rèn)為,用戶的評(píng)分是確定的,不隨時(shí)間變化,隨時(shí)間變化的是用戶對(duì)這個(gè)評(píng)分的置信程度,以此表征用戶的短期偏好隨時(shí)間變化。通過(guò)在基于模型推薦算法上的大量實(shí)驗(yàn),結(jié)果表明該方法可以更好地表征用戶的偏好,從而提升推薦了算法的準(zhǔn)確度、召回率等指標(biāo)。2)提出了一種基于隱馬爾可夫模型的用戶動(dòng)態(tài)偏好推薦算法對(duì)用戶的長(zhǎng)期、有重復(fù)性的偏好進(jìn)行建模對(duì)于提升個(gè)性化推薦系統(tǒng)的準(zhǔn)確度有重要作用�；跁r(shí)間衰減的偏好模型可以識(shí)別出用戶的短期偏好,但不足以識(shí)別出用戶的長(zhǎng)期、有重復(fù)性的偏好。針對(duì)上述問(wèn)題,本文提出了利用隱馬爾可夫模型來(lái)預(yù)測(cè)用戶未來(lái)偏好的方法。該方法利用歷史行為數(shù)據(jù),為每一個(gè)用戶建立隱馬爾克夫模型,通過(guò)該模型來(lái)預(yù)測(cè)該用戶的長(zhǎng)期、有重復(fù)性的偏好。通過(guò)在基于模型推薦算法上的大量實(shí)驗(yàn),結(jié)果表明該方法可以識(shí)別出用戶的長(zhǎng)期、有重復(fù)性的偏好,從而提升了推薦算法的準(zhǔn)確度、召回率等指標(biāo)。
[Abstract]:The tide of the Internet era has brought about the explosive growth of data, the concept of large data has also been rising in the past few years, and the price of users getting useful information from mass data is also getting higher and higher. The recommended system has brought dawn to solve this problem. The user preferences are modeled to help the user to quickly discover the information that they are really interested in. Currently, the non time sensitive recommendation algorithms usually think that the user's preference does not change with time, and the related algorithms are studied under this assumption. However, in the real world, the user's preference is not in the process of time. The time sensitive recommendation algorithm has no consideration of long term user, repetitive preference and low computational efficiency. Therefore, it is of great significance to study user's dynamic preference for improving the accuracy and recall of personalized recommendation algorithm. Detailed analysis, and then around the heterogeneous implicit feedback recommendation algorithm based on user dynamic preference, the main work includes: 1) a recommendation algorithm based on the time attenuation of user preferences is time sensitive, which usually uses a time attenuation method by reducing the value of the user before the longer time. In addition, the current research is mainly to use the time attenuation in a relatively simple user based collaborative filtering algorithm, and it is not yet applied to the model based recommendation algorithm. In this paper, a user preference measurement method based on the confidence time decline is proposed for the two problems. The score is determined and does not vary with time, and the user's confidence in the score is changed over time to represent the user's short-term preference over time. By a lot of experiments on the model based recommendation algorithm, the results show that the method can improve the user's preference, thus improving the accuracy of the proposed algorithm. Recall rate and other indicators.2) proposed a user dynamic preference recommendation algorithm based on Hidden Markov model for long-term users. Modeling with repetitive preference plays an important role in improving the accuracy of the personalized recommendation system. The time attenuated preference model can recognize the user's short-term preference, but not enough to identify the user's short-term preference. In this paper, a hidden Markov model is proposed to predict the user's future preference. This method uses historical behavior data to establish a hidden Markov model for each user to predict the user's long-term, repetitive preference through the model. A large number of experiments on the model recommendation algorithm show that the method can identify the long-term and repeatable preference of the user, thus improving the accuracy of the recommendation algorithm and the recall rate and so on.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.3

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