本文選題：移動新聞推薦 + 新聞偏好獲取��； 參考：《北京郵電大學(xué)》2017年博士論文

【摘要】：隨著移動設(shè)備的普及和移動網(wǎng)絡(luò)的迅速發(fā)展,越來越多的用戶使用移動終端獲取新聞等信息資訊。在這一現(xiàn)狀下,如何根據(jù)移動新聞上下文感知的特點獲取移動用戶新聞偏好并進行移動新聞推薦,以提高推薦性能和移動用戶滿意度,成為上下文感知移動新聞推薦的主要研究任務(wù)。雖然傳統(tǒng)的網(wǎng)絡(luò)用戶新聞偏好獲取以及推薦已經(jīng)在學(xué)術(shù)界和工業(yè)界取得了巨大的成功,但移動用戶新聞偏好和移動新聞推薦通常受到多樣性的,個性化的且動態(tài)變化的上下文影響,所以網(wǎng)絡(luò)用戶新聞偏好獲取及推薦算法并不能直接應(yīng)用于上下文感知的移動用戶新聞偏好獲取與推薦中。因此,本文針對移動環(huán)境中各種各樣的上下文信息(如位置上下文,時間上下文和社會化上下文等),研究了上下文感知的移動用戶新聞偏好獲取及推薦等相關(guān)問題。本文的主要貢獻有:1)提出了三個基于位置上下文的移動用戶新聞偏好獲取及移動新聞推薦算法。用戶可以使用移動終端在任何地方瀏覽新聞信息,因此移動用戶的新聞偏好通常都與位置上下文緊密相關(guān)�；谖恢蒙舷挛牡囊苿有侣勍扑]主要可以分為基于物理距離的方法和基于地理主題的方法。其中顯式局部語義分析(Explicit Localized Semantic Analysis,ELSA)是最先進的基于地理主題的方法,它已經(jīng)被證明要優(yōu)于很多其他的主題模型,如顯式語義分析(Explicit Semantic Analysis,ESA)和潛在狄利克雷分配(Latent Dirichlet Allocation,LDA)。然而,基于維基百科主題空間的ELSA由于受到高維度、稀疏性和冗余性等多種問題影響,大大降低了其新聞推薦性能。因此,為了克服上述問題,本文在ELSA基礎(chǔ)上提出了三種地理主題特征模型,CLSA,ALSA和DLSA,分別將聚類、自編碼和面向推薦的深度神經(jīng)網(wǎng)絡(luò)與ELSA集成,然后從維基百科主題空間抽取密集,抽象,低維度且非常有效的主題特征來描述新聞和地理位置。實驗結(jié)果證明,在基于位置上下文的移動新聞推薦中,本文提出的三種算法的推薦性能均要優(yōu)于最先進的地理主題模型ELSA,其中采用面向推薦的深度神經(jīng)網(wǎng)絡(luò)算法DLSA的改善效果最顯著。特別地,由于本文提出的三種算法可以發(fā)掘用戶在新位置上的潛在新聞偏好,因此它們也可以有效緩解“冷啟動”問題。2)提出了兩個位置感知的移動用戶個性化新聞偏好獲取及個性化移動新聞推薦算法。因為移動用戶的新聞偏好通常與其位置上下文有關(guān),所以許多研究工作已投入到位置感知的移動新聞推薦中向用戶推薦離他們最近的新聞。然而,在現(xiàn)實情況中,移動用戶的新聞偏好不僅僅依賴于他們的位置,還跟他們的個人興趣緊密相關(guān)。因此,本文我們提出了一個基于顯式語義分析的位置感知個性化新聞推薦算法(Location-aware Personalized News Recommendation with Explicit Semantic Analysis, LP-ESA),同時使用用戶的個人興趣和他們的位置上下文進行新聞推薦。然而,LP-ESA中基于維基百科的主題空間存在高維度,稀疏性和冗余性問題,大大降低了 LP-ESA的性能。為了解決這些問題,我們進一步提出 了一個LP-DS A ( Location-aware Personalized News Recommendation with Deep Semantic Analysis )算法,利用面向推薦的深度神經(jīng)網(wǎng)絡(luò)來為用戶、新聞和位置抽取密集的,抽象的,低維度且有效的描述特征。實驗結(jié)果表明,LP-ESA和LP-DSA均顯著優(yōu)于基準方法。此外,與LP-ESA相比,LP-DSA可以在更短的時間里提供更有效的新聞推薦。3)提出了一個基于多維上下文的移動用戶新聞偏好獲取及移動新聞推薦算法。移動用戶新聞偏好通常受到多種上下文的影響,如位置上下文、社會化上下文、時效性等。但是目前許多研究中使用的社會化上下文都是虛擬的,不能反映用戶的真實社交關(guān)系,降低了新聞推薦性能。此外,大部分研究中新聞的時效性通過人為設(shè)定閾值進行時間過濾或時間建模來實現(xiàn),但是這些方法沒有考慮新聞時效性對移動用戶新聞偏好的影響,而且人為設(shè)定閡值會為時效性計算帶來一定誤差。因此,為了解決上述問題,本文根據(jù)上下文感知的移動用戶通信行為數(shù)據(jù)來推斷其真實的好友關(guān)系,并利用新聞的點擊流數(shù)據(jù)客觀地分析獲取新聞時效性,最后將移動用戶社會化上下文,用戶興趣相似性和新聞時效性等多種上下文融合進行移動新聞推薦。實驗結(jié)果證明,和現(xiàn)有方法相比,本文提出的方法顯著提高了基于多維上下文的移動新聞推薦性能和推薦結(jié)果的時效性。4)提出了一個基于新穎性上下文的移動用戶新聞偏好獲取及移動新聞推薦算法。個性化推薦系統(tǒng)通常根據(jù)用戶的歷史數(shù)據(jù)獲取用戶偏好然后進行推薦。但是在新聞領(lǐng)域,新聞來源廣泛,關(guān)于同一事件可能有多篇新聞報道,所以新聞候選集的冗余度比較高。在這一現(xiàn)狀下,如果根據(jù)用戶歷史偏好進行新聞推薦,用戶已瀏覽新聞會排在推薦列表的最前面。因此,新穎性檢測對于實現(xiàn)高質(zhì)量的個性化新聞推薦很重要。然而,現(xiàn)有的新聞新穎性檢測方法大多基于幾何距離或分布相似性,需要將當前新聞和用戶歷史數(shù)據(jù)中的新聞單獨進行比較,時間成本很高,無法滿足移動新聞推薦的實時性要求。因此,為了克服上述問題,本文利用LDA主題模型抽取新聞潛在主題,并將新聞看作樣本而它們的潛在主題當作樣本屬性。然后將粗糙集和信息熵理論結(jié)合起來應(yīng)用于新聞樣本上獲取屬性權(quán)值,并計算每條新聞的總信息量,所以給定新聞的新穎性可以通過其與用戶歷史數(shù)據(jù)中新聞的總信息量之差的絕對值來快速衡量。接著我們進一步提出了一個正則化矩陣分解模型利用獲取的新聞新穎性上下文和用戶興趣相似性來進行移動新聞推薦。實驗結(jié)果表明,本文提出的算法提高了新穎性檢測效率和基于新穎性上下文的移動新聞推薦性能以及推薦結(jié)果的新穎性。
[Abstract]:With the popularity of mobile devices and the rapid development of mobile networks, more and more users use mobile terminals to obtain information and information. In this situation, how to obtain mobile users' news preferences and recommend mobile news according to the characteristics of mobile news context awareness can improve the recommendation performance and mobile user satisfaction. While the traditional network user news preference acquisition and recommendation has been a great success in academia and industry, mobile user news preference and mobile news recommendation are often affected by diverse, personalized and dynamic contexts. Network user news preference acquisition and recommendation algorithms do not directly apply to context aware mobile user news preference acquisition and recommendation. Therefore, this paper studies context aware mobile user news for various contextual information (such as location context, time context and socialized context) in the mobile environment. The main contributions of this paper are: 1) the main contributions of this paper are as follows: 1) three mobile users' news preference acquisition and mobile news recommendation algorithm based on position context are proposed. Users can use mobile terminals to browse news information anywhere, so the new smell preference of mobile users is usually closely related to the location context. Mobile news recommendation based on location context can be divided into physical distance based methods and geographically based methods. Explicit local semantic analysis (Explicit Localized Semantic Analysis, ELSA) is the most advanced geographically based method, which has been proved to be superior to many other topic models, such as explicit Explicit Semantic Analysis (ESA) and potential Dirichlet distribution (Latent Dirichlet Allocation, LDA). However, ELSA based on the Wikipedia theme space is affected by a variety of problems, such as high dimension, sparsity and redundancy, greatly reducing its new recommendation performance. Therefore, in order to overcome the above problems, this paper is on the ELSA base. Three geographic feature models, CLSA, ALSA and DLSA, are proposed to integrate clustering, self coding and recommendation oriented deep neural networks with ELSA, and then extract dense, abstract, low dimension and very effective thematic features from the Wikipedia theme space to describe the news and geographic location. Experimental results show that the location is based on location. In the following mobile news recommendation, the recommendation performance of the three algorithms proposed in this paper is superior to the most advanced geographic topic model ELSA, in which the recommendation based depth neural network algorithm DLSA has the most remarkable improvement effect. In particular, the three algorithms proposed in this paper can discover the potential news preference of the user in the new location. Therefore, they can also effectively alleviate the "cold start" problem.2) and put forward two position aware mobile users' personalized news preference acquisition and personalized mobile news recommendation algorithm. Because the news preference of mobile users is usually related to the position context, many research workers have been put into position aware mobile news recommendation. However, in reality, the news preference of mobile users is not only dependent on their location, but also closely related to their personal interests. Therefore, we propose a position aware personalized news recommendation algorithm based on explicit semantic analysis (Location-aware Personalized). News Recommendation with Explicit Semantic Analysis, LP-ESA), using the user's personal interests and their location context for news recommendation. However, there is a high dimension, sparsity, and redundancy in the theme space based on Wikipedia in LP-ESA, which greatly reduces the performance of LP-ESA. In order to solve these problems, we enter One step proposed a LP-DS A (Location-aware Personalized News Recommendation with Deep Semantic Analysis) algorithm, which uses a recommended depth neural network for user, news and location extraction intensive, abstract, low dimensional and effective description features. Experimental results show that LP-ESA and LP-DSA are significantly better than benchmarks. In addition, compared with LP-ESA, LP-DSA can provide more effective news recommendation.3 in a shorter time. A mobile user news preference acquisition and mobile news recommendation algorithm based on multidimensional context is proposed. Mobile user news preference is usually influenced by various contexts, such as location context, socialized context, and time limitation. But at present, the socialized context used in many studies is virtual, which can not reflect the user's real social relations and reduce the performance of news recommendation. In addition, the timeliness of news in most of the studies is realized by time filtering or time modeling by artificial threshold, but these methods do not consider the news limitation. The effect of sex on the news preference of mobile users, and the artificial setting of the threshold will bring some error for the timeliness calculation. In order to solve the problem, this paper deduce the true friend relationship based on the context aware mobile user communication behavior data and analyze the news timeliness objectively using the News Click Stream Data. Finally, mobile news recommendation is carried out by a variety of context fusion, such as the socialization context of mobile users, user interest similarity and news timeliness, and the experimental results show that, compared with the existing methods, the proposed method significantly improves the performance of mobile news recommendation based on multidimensional context and the timeliness of the recommended results, which is based on the existing methods. Mobile user news preference acquisition and mobile news recommendation algorithm based on novelty context. Personalized recommendation system usually obtains user preferences based on user historical data and recommends. However, in the field of news, news sources are wide, and there may be multiple news reports on the same event, so the redundancy ratio of news candidate sets is compared. In this situation, if the news recommendation is based on the user's historical preference, the user has been browsing the news conference in front of the recommendation list. Therefore, novelty detection is important for the realization of high quality personalized news recommendation. However, the existing novelty detection methods are mostly based on geometric distance or distribution similarity. Comparing the news in the current news and the historical data of the user, the time cost is very high and can not meet the real-time requirement of the mobile news recommendation. Therefore, in order to overcome the above problems, this paper uses the LDA topic model to extract the news potential topics and regard the news as the sample and their potential topics as sample attributes. The theory of rough set and information entropy is combined to obtain attribute weights on news samples and calculate the total amount of information of each news, so the novelty of a given news can be quickly measured by the absolute value of the difference between the total information of the news in the user's historical data. Then we further propose a regularized matrix decomposition. The model uses the news novelty context and the user interest similarity to carry out the mobile news recommendation. The experimental results show that the proposed algorithm improves the novelty detection efficiency and the performance of the mobile news recommendation based on the novelty context and the novelty of the recommended results.
【學(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2017
【分類號】：TP391.3

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