本文關鍵詞： 基于位置服務 POI推薦 嵌入學習 圖嵌入 序列嵌入　出處：《浙江大學》2017年碩士論文　論文類型：學位論文

【摘要】：隨著智能移動設備的快速普及以及基于位置社交網絡服務(Location-based Social Networking Services,LBSNs)的快速發(fā)展,基于 check-in 數據挖掘的 POI(Point of Interest)推薦成為幫助用戶發(fā)現新場所和探索不熟悉區(qū)域的重要方式。然而,POI推薦面臨嚴重的數據稀疏性問題,用戶旅行局部性現象更是惡化了這一問題。最近許多相關工作試圖通過考慮社交、時間、地理、序列、語義等方面影響來解決上述數據稀疏性問題,但是他們僅利用了部分方面影響,沒有一個并能準確整合多方面影響的方法。為了解決上述挑戰(zhàn),我們提出了一個基于圖和序列聯合嵌入的POI推薦方法。我們通過對7張二分圖(用戶-用戶圖、用戶-時間段圖、POI-時間段圖、POI-區(qū)域層次圖、POI-類別層次圖、用戶-性別圖以及用戶-POI圖)和check-in序列進行聯合嵌入學習,整合了社交、時間、地理、語義、用戶性別、用戶偏好以及序列方面影響。為了捕獲check-in序列中的語義信息,我們方法利用了序列嵌入方法(word2vec),而其它方面影響則利用圖嵌入方法,然后通過聯合訓練算法對上述多方面影響進行聯合嵌入學習。需要注意的是我們方法具有一定的擴展性,可以很方便地整合其它方面影響,從而更好地解決數據稀疏性問題,為用戶提供高質量的POI推薦。為了驗證我們方法的效果,我們在來自Foursquare的大規(guī)模真實數據集上進行了充分的實驗。實驗結果表明,本文提出方法明顯超過了其它對比方法。此外,我們還通過實驗研究了本文考慮的各方面影響對推薦效果提升的作用大小,結果發(fā)現時間和語義影響相對其它方面影響在推薦效果的提升上作用更明顯。
[Abstract]:With the rapid spread of smart mobile devices and location-based Social Networking Services. The rapid development of LBSNs. POI(Point of Interest-based check-in data mining is an important way to help users discover new places and explore unfamiliar areas. POI recommends serious data sparsity, which is exacerbated by the phenomenon of user travel locality. Recently, a lot of related work has attempted to consider social, time, geography, and sequence. Semantic impact to solve the problem of data sparsity, but they only take advantage of some aspects of the impact, there is no way to accurately integrate the various aspects of the impact. In order to solve the above challenges. We propose a POI recommendation method based on graph and sequence embedding. We use seven bipartite graphs (user-user graph, user-time graph) and POI-time graph. POI- regional hierarchy map (POI- category hierarchy map, user-gender map and user-POI map) and check-in sequence are jointly embedded learning, integrating social, time, geography. In order to capture semantic information in check-in sequences, our method utilizes sequence embedding method (Word2vec.). Other aspects of the influence is based on graph embedding method, and then the joint training algorithm is used to study the above effects. It is important to note that our method has a certain expansibility. Can easily integrate other aspects of the impact, so as to better solve the problem of data sparsity, provide users with high-quality POI recommendations, in order to verify the effectiveness of our method. We have carried out sufficient experiments on the large scale real data set from Foursquare. The experimental results show that the proposed method is obviously superior to other comparison methods. We also study the effect of each aspect on the improvement of recommendation effect through experiments. The results show that the effect of time and semantics on the promotion of recommendation effect is more obvious than that of other aspects.
【學位授予單位】：浙江大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.3

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