本文選題：微博關注推薦 + 社交相似度��； 參考：《山東大學》2017年碩士論文

【摘要】：隨著大數(shù)據(jù)時代的到來,新技術層出不窮,社交網(wǎng)絡的發(fā)展如火如荼。微博是最熱門的社交平臺之一,擁有著龐大的用戶群體,每天產(chǎn)生無數(shù)熱點信息。在微博中,人們可以發(fā)布原創(chuàng)消息;用戶可以在系統(tǒng)中找出自己感興趣的對象,成為其粉絲;轉發(fā)、評論、@等行為極大地豐富了用戶之間的互動體驗,也使得微博用戶之間的交互更加多元化。然而,信息的泛濫也讓用戶難以選擇,出現(xiàn)了信息過載的現(xiàn)象。推薦系統(tǒng)是用戶和項目之間的橋梁,能夠挖掘和捕捉用戶的偏好,主動給用戶推薦相關內(nèi)容,目前已經(jīng)被應用在很多場景下。協(xié)同過濾算法是其中最為經(jīng)典的算法之一,然而該算法非常依賴用戶-項目之間的評分數(shù)據(jù),并且面臨著嚴峻的數(shù)據(jù)稀疏性問題。在微博中,不存在用戶對于項目的評分數(shù)據(jù),因此不能簡單地將協(xié)同過濾算法應用在微博關注推薦中。微博的社交網(wǎng)絡特征給推薦問題提供了更多解決方案,融入社交行為、社交信任、鄰居意見、隱語義模型等都會大大改善推薦的性能。本文首先對推薦系統(tǒng)的發(fā)展以及微博關注個性化推薦進行了研究,介紹了協(xié)同過濾算法的相關技術和原理,闡述了當前算法面臨的困難與挑戰(zhàn)。通過騰訊微博數(shù)據(jù)集分析了微博社交網(wǎng)絡的相關特征、社交圖譜、用戶關系等,重新定義了微博關注推薦的相關術語,對微博中的不同社交行為進行建模,并介紹了系統(tǒng)的整體流程、技術平臺、系統(tǒng)環(huán)境等。針對Top-N推薦問題,提出了基于社交相似度的微博關注Top-N推薦算法。根據(jù)微博關注行為、互動行為以及歷史推薦記錄分別計算相似度,通過計算出來的相似度找出最近鄰集合,在此基礎上給用戶進行推薦。在微博數(shù)據(jù)集上對比了不同相似度計算方法的準確率、召回率和Fl-measure,并在Hadoop平臺上利用MapReduce對算法進行了并行化設計,提高了算法的執(zhí)行效率。針對評分預測問題,提出了融合社交信任和隱語義模型的微博關注推薦算法。將用戶的歷史推薦記錄建模為評分矩陣,引入社會化推薦,通過用戶之間的互動行為數(shù)據(jù)(包括@、評論和轉發(fā))計算用戶之間的隱式信任,從用戶的直接社交關系中得到用戶之間的顯式信任,將顯式信任和隱式信任結合來構建擴展信任矩陣并融入SVD++模型。最終在KDD Cup 2012數(shù)據(jù)集上的實驗表明算法在RMSE和MSE上得到了更好的結果。
[Abstract]:With the arrival of big data era, new technologies emerge in endlessly, the development of social network is in full swing.Weibo is one of the most popular social platforms, with a large group of users, generating countless hot messages every day.In Weibo, people can post original messages; users can find out who they are interested in in the system and become fans; retweets, comments and other behaviors greatly enrich the interactive experience between users.It also makes the interaction between Weibo users more diversified.However, the flood of information also makes it difficult for users to choose, and appears the phenomenon of information overload.Recommendation system is a bridge between users and projects. It can mine and capture users' preferences and actively recommend relevant content to users. It has been used in many scenarios.Collaborative filtering algorithm is one of the most classical algorithms. However, it relies heavily on the scoring data between users and items, and faces a severe problem of data sparsity.In Weibo, there is no user rating data, so we can not simply apply collaborative filtering algorithm to Weibo recommendation.Weibo's social network features provide more solutions to the recommendation problem, which can greatly improve the performance of recommendation by integrating social behavior, social trust, neighbor opinion, implicit semantic model and so on.This paper first studies the development of recommendation system and Weibo pays attention to personalized recommendation, introduces the technology and principle of collaborative filtering algorithm, and expounds the difficulties and challenges that the current algorithm is facing.By analyzing the relevant features, social atlas, user relationship and so on, the related features, social map, user relationship and so on are analyzed by Tencent Weibo data set, then the relevant terms concerned and recommended by Weibo are redefined, and the different social behaviors in Weibo are modeled.The whole process, technology platform and system environment of the system are also introduced.Aiming at the problem of Top-N recommendation, a Top-N recommendation algorithm for Weibo based on social similarity is proposed.According to Weibo's attention behavior, interactive behavior and history recommendation record, the similarity is calculated, and the nearest neighbor set is found out by the calculated similarity, and then the user is recommended.The accuracy recall rate and Fl-measurement of different similarity calculation methods are compared on Weibo data set. The parallel design of the algorithm is carried out on Hadoop platform using MapReduce to improve the efficiency of algorithm execution.Aiming at the problem of score prediction, a recommendation algorithm based on Weibo is proposed, which combines social trust and implicit semantic model.The historical recommendation records of users are modeled as scoring matrices, and social recommendations are introduced to calculate implicit trust between users through interactive behavior data between users (including @, comment and forwarding).The explicit trust between users is obtained from the direct social relationship of users, and the extended trust matrix is constructed by combining explicit trust with implicit trust, and the extended trust matrix is integrated into the SVD model.Finally, experiments on KDD Cup 2012 dataset show that the algorithm has better results on RMSE and MSE.
【學位授予單位】：山東大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.3

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