【摘要】：隨著互聯(lián)網(wǎng)的發(fā)展，信息過載問題越來越嚴(yán)重，用戶找到自己想要的商品或信息所花費(fèi)的時間越來越多。將來人們獲取自己感興趣信息的途徑有可能由單一的搜索引擎變?yōu)樗阉饕媾c推薦系統(tǒng)相結(jié)合。推薦系統(tǒng)的價值在于不僅能夠推薦給用戶符合用戶興趣的物品，而且還要能夠發(fā)現(xiàn)長尾商品，長尾商品更能體現(xiàn)小群體用戶的個性化需求。現(xiàn)在推薦算法的研究越來越受到人們的重視，特別是推薦算法的一些比賽如Netflix、2012年KDD track1等比賽的出現(xiàn)，更加促進(jìn)了推薦算法的快速發(fā)展。在推薦算法中時間信息作為一種上下文信息能夠提高推薦的質(zhì)量。一方面推薦系統(tǒng)要能準(zhǔn)確的給用戶推薦與其興趣相關(guān)的產(chǎn)品，而且要能在正確的時間給用戶做推薦；另一方面用戶在不同時間對相同的推薦結(jié)果做出的反饋不同。因此，時間信息受到越來越多研究者的關(guān)注，現(xiàn)在也有很多考慮時間因素的推薦算法被提出來。有些在模型中直接加入時間特征，有些模型不考慮時間特征，但以時間特征去選擇用來建模的數(shù)據(jù)集。 本文針對目前推薦算法中引入時間因素的方法做出改進(jìn)。時間因素的引入主要體現(xiàn)在模擬用戶興趣度隨時間的變化、物品流行度隨時間的變化和社會群體興趣度隨時間的變化。社會群體興趣度隨時間變化容易模擬，難點在于用戶興趣度隨時間的變化以及物品流行度隨時間變化，因為不同的用戶有不同的興趣度變化趨勢，，不同的物品也有不同的流行度變化趨勢。當(dāng)前的很多引入時間因素的推薦算法，沒有考慮這些不同，只是對所有的用戶采用相同的興趣度變化模型，對所有的物品采用相同的流行度變化模型。針對這個問題本文提出了對每個用戶的興趣度變化趨勢分別建模以及對每個物品流行度變化趨勢分別建模的方法。因為用戶的當(dāng)前行為受用戶近期行為的影響，所以本文通過為用戶近期行為賦予不同的權(quán)重來對當(dāng)前時刻用戶的興趣進(jìn)行模擬，也就是通過用戶近期行為對當(dāng)前的興趣貢獻(xiàn)程度的不同來間接模擬出不同用戶的不同興趣度變化趨勢。通過對物品近期流行度賦予不同的權(quán)重來模擬當(dāng)前物品的流行度。這些權(quán)重的求解方法是以每個用戶以及每個電影評分的時間序列數(shù)據(jù)作為訓(xùn)練集，首先把數(shù)據(jù)集按時間分隔，然后以時間片為單位求得各時間片對應(yīng)的評分均值，最后通過隨機(jī)梯度下降算法求解模型中各參數(shù)。
[Abstract]:With the development of the Internet, the problem of information overload becomes more and more serious, and it takes more and more time for users to find the goods or information they want. In the future, it is possible for people to get information of their own interest from a single search engine to a search engine and a recommendation system. The value of the recommendation system is not only to recommend to the user to meet the interests of the user, but also to find long-tailed goods, which can more reflect the personalized needs of small groups of users. Nowadays, people pay more and more attention to the research of recommendation algorithm, especially the appearance of some competitions such as Netflix, 2012 KDD track1, which promotes the fast development of recommendation algorithm. As a kind of context information, time information can improve the quality of recommendation in recommendation algorithm. On the one hand, the recommendation system should be able to recommend the products related to their interest to users accurately, and make recommendations to users at the right time; on the other hand, the feedback of users on the same recommendation results at different times is different. Therefore, more and more researchers pay attention to time information. Some models directly add time features, some models do not consider time features, but use time features to select the data set used for modeling. This paper improves the method of introducing time factor into the recommendation algorithm. The introduction of time factor is mainly reflected in the change of interest degree of simulated user with time, the change of item popularity with time and the change of interest degree of social group with time. It is easy to simulate the change of social group interest with time. The difficulty lies in the change of user interest with time and the change of article popularity with time, because different users have different trends of interest. Different items also have different trends of popularity. Many current recommendation algorithms that introduce time factor do not take these differences into account, but use the same interest change model for all users and the same popularity change model for all items. In order to solve this problem, this paper presents a method to model the trend of interest change for each user and to model the trend of change in popularity of each item separately. Because the current behavior of the user is influenced by the user's recent behavior, this paper simulates the interest of the user at the current time by assigning different weights to the user's recent behavior. In other words, the change trend of different users' interest degree is indirectly simulated by the difference of the user's recent behavior to the current interest contribution degree. By giving different weights to the near-term popularity of articles, this paper simulates the popularity of current articles. The method of calculating these weights is to take the time series data of each user and each movie score as the training set. Firstly, the data sets are separated by time, and then the mean value of each time slice is obtained by using time slice as the unit. Finally, the parameters of the model are solved by the stochastic gradient descent algorithm.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：TP391.3

【引證文獻(xiàn)】

相關(guān)期刊論文 前1條

1 劉喬;劉彬;;基于時間加權(quán)的協(xié)同過濾推薦算法的改進(jìn)[J];計算機(jī)工程與設(shè)計;2016年07期

相關(guān)碩士學(xué)位論文 前4條

1 劉喬;基于時間加權(quán)與評分預(yù)測的協(xié)同過濾推薦算法研究[D];貴州師范大學(xué);2016年

2 魏Y伶

本文編號：2371071