本文關(guān)鍵詞： 基于位置服務(wù) POI推薦 嵌入學(xué)習(xí) 圖嵌入 序列嵌入　出處：《浙江大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著智能移動(dòng)設(shè)備的快速普及以及基于位置社交網(wǎng)絡(luò)服務(wù)(Location-based Social Networking Services,LBSNs)的快速發(fā)展,基于 check-in 數(shù)據(jù)挖掘的 POI(Point of Interest)推薦成為幫助用戶發(fā)現(xiàn)新場(chǎng)所和探索不熟悉區(qū)域的重要方式。然而,POI推薦面臨嚴(yán)重的數(shù)據(jù)稀疏性問(wèn)題,用戶旅行局部性現(xiàn)象更是惡化了這一問(wèn)題。最近許多相關(guān)工作試圖通過(guò)考慮社交、時(shí)間、地理、序列、語(yǔ)義等方面影響來(lái)解決上述數(shù)據(jù)稀疏性問(wèn)題,但是他們僅利用了部分方面影響,沒(méi)有一個(gè)并能準(zhǔn)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(gè)基于圖和序列聯(lián)合嵌入的POI推薦方法。我們通過(guò)對(duì)7張二分圖(用戶-用戶圖、用戶-時(shí)間段圖、POI-時(shí)間段圖、POI-區(qū)域?qū)哟螆D、POI-類別層次圖、用戶-性別圖以及用戶-POI圖)和check-in序列進(jìn)行聯(lián)合嵌入學(xué)習(xí),整合了社交、時(shí)間、地理、語(yǔ)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(yǔ)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(guò)聯(lián)合訓(xùn)練算法對(duì)上述多方面影響進(jìn)行聯(lián)合嵌入學(xué)習(xí)。需要注意的是我們方法具有一定的擴(kuò)展性,可以很方便地整合其它方面影響,從而更好地解決數(shù)據(jù)稀疏性問(wèn)題,為用戶提供高質(zhì)量的POI推薦。為了驗(yàn)證我們方法的效果,我們?cè)趤?lái)自Foursquare的大規(guī)模真實(shí)數(shù)據(jù)集上進(jìn)行了充分的實(shí)驗(yàn)。實(shí)驗(yàn)結(jié)果表明,本文提出方法明顯超過(guò)了其它對(duì)比方法。此外,我們還通過(guò)實(shí)驗(yàn)研究了本文考慮的各方面影響對(duì)推薦效果提升的作用大小,結(jié)果發(fā)現(xiàn)時(shí)間和語(yǔ)義影響相對(duì)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(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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