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多維度行事曆助理張文祥 Unknown Date (has links)
隨著資訊科技的發展,網際網路成為個人獲得資訊的主要來源之一。但是過多的資訊產生資訊爆炸(information overload)的現象,人們除了要在眾多資訊中找尋想要的資訊外,還需要擔心所尋找到的資訊的品質是否良好。因此,推薦系統提供了一個良好的解決方法。推薦系統透過分群與推薦的技術來達到減少資訊量與推估使用者潛在興趣的目的。目前推薦系統多應用在單一維度的推薦,本論文希望藉由某一情境來探討多維度推薦的應用,所以選擇助理軟體來實現多維度推薦的應用。選擇助理軟體是由於其已經成為個人日常生活中時常使用的工具,且由於助理軟體管理個人日常生活中的大小事務,成為最貼近個人的工具。若專注在個人行事曆的安排上,我們可以發現個人行事曆安排牽涉到有人、事、時、地、物五個維度。因此我們以五維度做分群,透過合作推薦(Collaborative Recommender)的方式將可以達到個人潛在興趣的多維度(Multi-Dimensions)推薦。本研究將以行事歷排定為情境,來說明如何將五個維度的各種可能組合依照其契合個人興趣的程度來進行推薦,這將使得助理軟體的內容更加豐富,且能貼近使用者的需求,提供意想不到的資訊組合。 / With the development of information science and technology, assistant software becomes a tool which often uses in personal daily life, and because all kinds of affairs in personal daily life that assistant software is managed, so assistant software becomes a tool which personally close to people. Intelligent assistant software hopes to make assistant software have intelligence which is similar to the mankind. Just like a personal general secretary, arrange the most proper individualized journey. Further, it can combine the idea of Recommender system to recommend the journey of the potential interest while arranging in the personal journey. This research proposes an intelligent assistant software with five- dimensions include of people, thing, when, location and things, uses cooperative Recommender approach to reach multi-dimension recommendation of personal potential interest. This research will give example of meeting as the situation to explain how to make five-dimensions recommendation according to personal interest. This will make the content of assistant software more abundant, and can press close to the user's demand.
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AppReco: 基於行為識別的行動應用服務推薦系統 / AppReco: Behavior-aware Recommendation for iOS Mobile Applications方子睿, Fang, Zih Ruei Unknown Date (has links)
在現在的社會裡,手機應用程式已經被人們接受與廣泛地利用,然而目前市面上的手機 App 推薦系統,多以使用者實際使用與回報作為參考,若有惡意行為軟體,在使用者介面後竊取使用者資料,這些推薦系統是難以查知其行為的,因此我們提出了 AppReco,一套可以系統化的推薦 iOS App 的推薦系統,而且不需要使用者去實際操作、執行 App。
整個分析流程包括三個步驟:(1) 透過無監督式學習法的隱含狄利克雷分布(Latent Dirichlet Allocation, LDA)做出主題模型,再使用增長層級式自我組織映射圖(Growing Hierarchical Self-Organizing Map, GHSOM)進行分群。(2)使用靜態分析程式碼,去找出其應用程式所執行的行為。(3)透過我們的評分公式對於這些 App,進行評分。
在分群 App 方面,AppReco 使用這些應用程式的官方敘述來進行分群,讓擁有類似屬性的手機應用程式群聚在一起;在檢視 App 方面,AppReco 透過靜態分析這些 App 的程式碼,來計算其使用行為的多寡;在推薦 App 方面,AppReco 分析類似屬性的 App 與其執行的行為,最後推薦使用者使用較少敏感行為(如使用廣告、使用個人資料、使用社群軟體開發包等)的 App。
而本研究使用在 Apple App Store 上面數千個在各個類別中的前兩百名 App 做為我們的實驗資料集來進行實驗。 / Mobile applications have been widely used in life and become dominant software applications nowadays. However there are lack of systematic recommendation systems that can be leveraged in advance without users’ evaluations. We present AppReco, a systematic recommendation system of iOS mobile applications that can evaluate mobile applications without executions.
AppReco evaluates apps that have similar interests with static binary analysis, revealing their behaviors according to the embedded functions in the executable. The analysis consists of three stages: (1) unsupervised learning on app descriptions with Latent Dirichlet Allocation for topic discovery and Growing Hierarchical Self-organizing Maps for hierarchical clustering, (2) static binary analysis on executables to discover embedded system calls and (3) ranking common-topic applications from their matched behavior patterns.
To find apps that have similar interests, AppReco discovers (unsupervised) topics in official descriptions and clusters apps that have common topics as similar-interest apps. To evaluate apps, AppReco adopts static binary analysis on their executables to count invoked system calls and reveal embedded functions. To recommend apps, AppReco analyzes similar-interest apps with their behaviors of executables, and recommend apps that have less sensitive behaviors such as commercial advertisements, privacy information access, and internet connections, to users.
We report our analysis against thousands of iOS apps in the Apple app store including most of the listed top 200 applications in each category.
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以Web2.0民眾分類法建置音樂推薦系統之研究 / A Music Recommendation System Based on the Web 2.0 Folksonomy Approach鄭學侖, Cheng,Allen Unknown Date (has links)
近年來,數位格式的音樂使得音樂市場活動逐漸由實體轉移到線上,消費者也開始會透過線上服務自己搜尋並取得在網路上大量的音樂。但是由於過量的音樂資訊,使得消費者在下載音樂試聽後,往往真正會去購買的比例是微乎極微,因此造成唱片業者對於音樂下載的觀點仍非常保守。因此,如何去提升在消費者下載之歌曲數量與真正消費之音樂的比例,將是線上音樂市場的一項發展重點。
本研究希望透過近年在Web 2.0網站上常見之標籤系統,實作一個由群眾定義音樂分類的音樂資訊交流平台,並基於此標籤式的分類法,發展一套推薦系統,來提高消費者接觸到喜歡之音樂的比例,近一步解決上述之問題。在系統發展中,本研究提出一套用於推薦系統之演算法則,並在建置之實驗音樂資訊交流平台上驗證其可行性。最後,本研究亦針對未來研究議題方向,提出一些建議。
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改良式個案推薦機制: 階層式擷取條件與階段式的個案推理演算法 / Enhanced Case-Based Recommender Mechanism:Hierarchical Case-Retrieved Criteria and Multiple-Stage CBR Algorithm王貞淑, Wang, Chen Shu Unknown Date (has links)
各類電子商務網站上的推薦機制應用已日趨廣泛且成熟。而隨著決策問題日漸複雜,現行的推薦機制發展已經可以看到應用的界限,再也無法貼近使用者所面臨的複雜問題。現行的推薦機制架構需要被重新審視、定義與設計其核心演算法。本研究用更寬廣的角度看待推薦機制,並將改良後的推薦機制視為解決問題的新典範。
首先,本研究定義了改良後的推薦機制所應支援的功能,包括:階層式條件的多維度推薦以及多階段的推薦。多維度推薦機制能夠讓使用者從不同的面向去看待決策問題,而階層式條件則允許使用者針對每個維度再往下設定階層式條件,幫助決策者更貼切的描述所遭遇的問題,如此一來推薦機制所提供的推論結果才能更符合決策者的原意。而多階段推薦則是協助決策者進行一連串的規劃方案,而這樣的推薦結論能夠提供可行方案的遠景,讓決策者能夠預先為可能發生的狀況進行準備,進而深化決策者對目前推薦結論的信心。
除了力求每個(或多個)階段推薦結論的正確性,推薦系統也要與所有的決策階段緊密結合(不僅止於資料搜集階段),所以必須能夠提供決策者行為面的建議,確切的建議決策者應該採取的行動。確切的行為面資訊推薦結論對於決策活動的參考價值更高。
所以,本研究修改了傳統的案例推導法(CBR),試圖讓傳統CBR演算法成為符合改良後個案推薦機制的規範,因為CBR演算法最符合人類求解問題的邏輯程序,因此本研究在改良式個案推薦機制中重現CBR演算法中的4R推理循環。而且為了真正落實修正後的CBR演算法,本研究還結合了基因演算法提出GCBR的概念,幫助改良式個案推薦系統能夠更快速有效的收斂出推薦的結論。
最後,本研究也預期所提出的推薦機制能夠應用於各種不同的領域,而為了驗證所提出的推薦機制執行效率與可行性,本研究也列舉了數個實驗進行的規範方案。本研究所提出的改良式個案推薦機制核心演算法為一概化模型,能夠求解不同型態的決策問題。 / Recommender system can be regarded as fundamental technology of electronic commence web site. Some researchers also claimed that recommender system push the electronic web site to another development peak. Recommender system would need some mechanisms. These recommender mechanisms should be reviewed, redefined and expanded to include particularly case-based mechanism that focus on reality problem solving.
Recently, CBR applications had been extended to provide recommendation mechanism based on previous cases. The abstract recommendation problems are usually hard to be formulated in strict mathematic models, and often solved via word-mouse experience. Case-Based Reasoning (CBR) is a paradigm, concept and instinctive mechanism for ill-defined and unstructured problem solving. Similarly to human problem solving process, CBR retrieves past experiences to reuse for target problem. Of course, the solutions of past cases may need to be revised for applying. The successful problem-solving experiences are then retained for further reusing. These are well-known 4R processes (retrieve, reuse, revise, and retain) of traditional CBR.
Nevertheless, the case-based recommender mechanism is particularly suitable for reality problem reference because case-style can be used to describe unstructured problem. The next generation recommender mechanism should focus on the real life problem solving and applications. Thus, case-based recommender mechanism can be regarded as a new problem solving paradigm.
To enhance traditional CBR algorithm to case-based recommender mechanism, the original CBR should be redesigned. In the traditional CBR algorithm, based on multiple objectives, the retrieved cases could provide to decision maker for references. However, as the decision problem is getting complex, pure multiple objective problem representation is too unsophisticated to reflect reality. Thus, a revised CBR algorithm equipped with capability to deal with more complexity is needed. Additionally, decision makers would wish to achieve the actionable information. The existing recommender mechanism can not provide the actionable direction to decision maker. Based on previous cases provided by CBR, decision maker would further hope that recommender mechanism could tell them how to do. These capabilities should be included into traditional CBR algorithm.
Furthermore, traditional CBR has to evaluate all cases in case base to return the most similar case(s). The efficiency of CBR is obviously negatively related to the size of case base. Thus, a number of approaches have devoted to decrease the effort for case evaluation. This research proposes a revised CBR mechanism, named GCBR, which can be regarded as next generation CBR algorithm. GCBR can be applied to reality applications, particularly case-based recommender mechanism. Thus, it can be treated as a new problem solving paradigm. It also intends to improve traditional CBR efficiency stability no matter what kinds of case representation and indexing approaches.
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廣告中角色人物之 可愛與否,故事有無、與推薦方式 對消費者態度之影響吳裕傑 Unknown Date (has links)
角色行銷已經成為常見的行銷手法。可愛通常是消費者對於角色人物的必備的要件,而角色人物的運用適合發展相對應的故事情節,此外,角色人物的推薦方式不盡相同,因此本研究試圖探討可愛與否、故事有無與推薦方式對消費者態度之影響。研究結果顯示可愛與否並不會影響到消費者態度,故事有無只有對購買意願產生影響,而主動推薦方式會讓消費者產生較好的廣告可信度、品牌態度與購買意願。所以企業在運用角色人物時應考慮主動的推薦方式,此外,也能在角色造形上加強主動推薦的元素,提升對消費者態度之影響。
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基於個人電腦使用者操作情境之音樂推薦 / Context-based Music Recommendation for Desktop Users謝棋安, Hsieh, Chi An Unknown Date (has links)
隨著電腦音樂技術的蓬勃發展,合乎情境需求的音樂若被能自動推薦給使用者,將是知識工作者所樂見的。我們提出了一個定義使用者操作情境的情境塑模,定義使用者操作情境,並利用累計專注視窗的轉變,找出使用者的操作情境。同時,我們也提出了音樂推薦塑模,依據使用者的操作情境與聆聽的音樂,分析探勘情境與音樂特徵間的關聯特性,利用探勘出的關聯推薦適合情境的音樂給使用者。在此音樂推薦塑模中,我們採用Content-based Recommendation的作法。我們分析音樂的特徵值,並發展MAML(Multi-attribute Multi-label)的分類演算法以及Probability Measure二種方法來探勘情境屬性與音樂特徵間的關聯特性。根據探勘出的關聯特性,找出適合情境的音樂特徵,再從音樂資料庫中推薦符合音樂特徵的音樂給使用者。本論文的符合使用者操作情境的音樂推薦系統是利用Windows Hook API實作。經實驗證明,本論文方法在符合情境的音樂推薦上,擁有近七成準確率。 / With the development of digital music technology, knowledge workers will be delighted if the music recommendation system is able to automatically recommend music based on the operating context in the desktop. The context model and context identification algorithm are proposed to define the operating context of users and to detect the transition of context based on the changes of focused windows. Two association discovery mechanisms, MMAL (Multi-attribute Multi-label) algorithm and PM (Probability Measure), are proposed to discover the relationships between context features and music features. Based on the discovered rules, the proposed music recommendation mechanism recommends music to the user from the music database according to the operating context of users. The context-based recommendation system is implemented using Windows Hook API. Experimental results show that near 70% accuracy can be achieved.
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基於音樂特徵以及文字資訊的音樂推薦 / Music recommendation based on music features and textual information張筑鈞, Chang, Chu Chun Unknown Date (has links)
在WEB2.0的時代,網際網路中充斥著各式各樣的互動式平台。就音樂網站而言,使用者除了聽音樂外,更開始習慣於虛擬空間中交流及分享意見,並且在這些交流、分享的過程中留下他們的足跡,間接的提供許多帶有個人色彩的資訊。利用這些資訊,更貼近使用者的推薦系統因應而生。本研究中,將針對使用者過去存取過的音樂特徵以及使用者於系統中留下的文字評論特徵這兩個部份的資料,做音樂特徵的擷取、找尋具有價值的音樂特徵區間、建立使用者音樂特徵偏好,以及文字特徵的擷取、建立使用者文字特徵偏好。接著,採用協同式推薦方式,將具有相同興趣的使用者分於同一群,推薦給使用者與之同群的使用者的喜好物件,但這些推薦之物件為該使用者過去並沒有任何記錄於這些喜好物件上之物件。我們希望對於音樂推薦考慮的開始不只是音樂上之特徵,更包含了使用者交流、互動中留下的訊息。 / In the era of Web2.0, it is flooded with a variety of interactive platforms on the internet. In terms of music web site, in addition to listening to music, users got used to exchanging their comments and sharing their experiences through virtual platforms. And through the process of exchanging and sharing, they left their footprints. These footprints indirectly provide more information about users that contains personal characteristics. Moreover, from this information, we can construct a music recommendation system, which provides personalized service.
In this research, we will focus on user’s access histories and comments of users to recommend music. Moreover, the user’s access histories are analyzed to derive the music features, then to find the valuable range of music features, and construct music profiles of user interests. On the other hand, the comments of users are analyzed to derive the textual features, then to calculate the importance of textual features, and finally to construct textual profiles of user interests. The music profile and the textual profile are behaviors for user grouping. The collaborative recommendation methods are proposed based on the favorite degrees of the users to the user groups they belong to.
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建置結合社群互動圈的個人化餐廳推薦系統 / Design and Implementation of a Personalized Restaurant Recommendation System黃資雅 Unknown Date (has links)
選擇到哪家餐廳用餐的問題,不論旅遊或家居都經常會遇到。大多數的人會先上網,尋找符合自己喜好且評價好的美食。然而網際網路發達,在人人都可上網分享的情況下,造成資訊氾濫超載。使得使用者上網瀏覽資料時,很容易找到不切合需求的資訊。解決此資訊超載的方法之一是餐廳推薦系統。儘管目前有很多的推薦應用程式或是分享平台,諸如TripAdvisor、iPeen愛評網、foursquare…等等,資料豐富但卻沒有針對個人偏好做推薦。
本研究有鑒於許多人在品嘗美食之前,會先拍照並在Facebook或Instagram打卡做紀錄、分享給朋友,打卡的次數可能意味著此餐廳的熱門度。且使用者選擇的美食類型偏好也可能受到聚餐目的的影響。因此開發出一款結合社群互動圈以及考量用餐情境的餐廳推薦系統。此系統先利用使用者所選擇的聚餐場合、價位、餐廳類型、熱門商圈等元素篩選出合適的餐廳,再利用Facebook打卡資料取得與使用者偏好相似的好友,依據好友的相似度推算出使用者對餐廳的喜好程度,推薦符合使用者興趣及需求的餐廳,協助使用者能夠更容易地找到自己所喜好的店家。
本研究的實作系統,經過評估測試,結果發現結合社群互動圈及考量用餐情境的個人化推薦能讓使用者更容易找到自己所喜好的餐廳,而在推薦內容中顯示好友對餐廳的評論,更有效的幫助使用者作決策。未來本推薦系統所使用之結合情境元素所設計的模式亦可應用至其他領域的推薦平台,如旅遊景點推薦或旅遊住宿推薦。 / Most people face the issue of deciding which restaurant to eat. Searching through the Internet is the first step that people usually do. However the rapid growth of information has overloaded the Internet users, it makes difficult to find the most appropriate information for decision-making. Certainly there are several restaurant recommender systems have been developed to solve the problem, such as TripAdvisor, iPeen, foursquare, etc; but few systems provide personalized and context-based recommendations.
The research intends to develop a restaurant recommender system that considers the factors of social network and context. Nowadays, when people eat, they like to take a picture and check in on Facebook or Instagram to share with friends, the numbers of check-in for a restaurant may mean the restaurant’s popularity. In addition, the gathering purpose and personal preferences may also affect the users’ decisions. Therefore the recommender system first used the variables of eating criteria such as place, price, types of food, eating environments to filter restaurants. The system then got the user’s similar friends from check-in data of Facebook. Through calculating friends’ similarity and their preference of restaurants, the recommender system finds the most fitted ones for the user to choose from.
The afterward system’s users testing data prove that this personalized and context-based recommendation system provides better information to help the user make their decisions. The same model can be replicated to other domain of recommender platforms.
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Facebook社群人脈網絡與粉絲頁推薦之研究 / The Study of Recommendation on Social Connections and Fan Pages on Facebook曾子洋, Tseng, Tzu Yang Unknown Date (has links)
Facebook自從在台灣推出以來,已有超過一千三百萬的使用者帳號,是最熱門的社群網站,其中蘊含了龐大的使用者資料。從使用者學歷、工作經歷和喜歡的粉絲頁中可以一定程度上地判斷出使用者的背景與喜好,若能利用分析過的資訊將使用者分群,以供交友或導向到可能喜歡的粉絲頁,就能開發潛在客戶進而掌握商機。
本研究旨在完成一個線上系統,透過Facebook上可供擷取個人的資料:學歷、工作經歷以及喜歡的粉絲頁等資訊,針對這些量化過的資訊,經Kmeans將使用者分群分類,藉以作為協同過濾式推薦。目前實驗結果將有效個人資料4417筆進行分群,以使用者喜歡的粉絲頁比例(本研究整合成48種)加上工作經歷與學歷,最終分成10群,以作為交叉推薦之憑據和延伸研究。研究過程分實驗組與對照組,實驗組是本研究推薦的10筆粉絲頁,也就是使用者與所屬群集質心比例相差較多的粉絲頁類型;對照組則是選取使用者與母體中有較多比例差距的10筆,以證明本研究的推薦模型有效。
最後由使用者針對兩組推薦結果進行滿意度評分之比較,總共收回使用者回饋68筆,實驗組與對照組的平均推薦滿意度分數分別為0.5743、0.4268,對兩者作信心水準為95%的t檢定,結果為有充分證據支持實驗組大於對照組,可證明本研究對於推薦準確性的幫助,達成本研究目的。
由此實驗可以確定在Facebook上以使用者屬性為基礎的粉絲頁與人脈推薦是有意義與價值的,也說明真實數據能應用在社群網站的研究。希冀本研究的結果能帶動其他社群網站研究朝使用真實數據去分析佐證,讓社群網站的研究結果能更貼近使用者的真實行為。 / Facebook is one of the most popular social websites in Taiwan, and it has over 13 million accounts with lots of user data. One can tell a user’s background and preference by his education, work experience, and preferred fan pages. If we direct the right user to the right fan pages by analyzing information and clustering users through recommendation or personal connections, we will be able to reach potential customers and to further business opportunities.
The goal of this study is to complete an online system to assume collaborative fan page recommendation. Base on users’ education degree, work experience and preferred fan pages, users’ background. Then use the Kmeans algorithm to cluster quantified personal information to recommend fan pages and social relationships. Currently, the result of the experiment shows 10 clusters, which contain 4417 users, and we use it as a foundation of crossing recommendation. To prove the effect of this study, we divide study into two groups, an experimental group and control group. The former one represents recommended top 10 fan pages that include the fan page types with highest difference of percentage between user’s attributes and cluster centroid; the latter one represents top 10 fan pages that include the fan page types with highest difference of percentage between users’ attributes and proportion respectively.
Finally, we use users score satisfaction for each group to compare. There are 68 pieces of feedback, and the average satisfaction scores of the experimental group and the control group are 0.5743 and 0.4268, respectively. On both a confidence level of 95% for t-test, the result shows there is more sufficient evidence to support the satisfaction of experimental group than the control group. We can prove accuracy for recommendation to achieve the goal in this study.
This experiment determines not only the fan page recommendation based on user attributes on Facebook is meaningful and valuable, but also shows real data can be used in social networking studies. We hope the results of this study can lead other social networking studies to analyze with real users’ data in order to make future study on social networking better reflect real users’ behavior.
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基於文件相似度的標籤推薦-應用於問答型網站 / Applying Tag Recommendation base on Document Similarity in Question and Answer Website葉早彬, Tsao, Pin Yeh Unknown Date (has links)
隨著人們習慣的改變,從網路上獲取新知漸漸取代傳統媒體,這也延伸產生許多新的行為。社群標籤是近幾年流行的一種透過使用者標記來分類與詮釋資訊的方式,相較於傳統分類學要求物件被分類到預先定義好的類別,社群標籤則沒有這樣的要求,因此容易因應內容的變動做出調整。
問答型網站是近年來興起的一種個開放性的知識分享平台,例如quora、Stack Overflow、yahoo 奇摩知識+,使用者可以在平台上與網友做問答的互動,在問與答的討論中,結合大眾的經驗與專長,幫助使用者找到滿意的答案,使用單純的問答系統的好處是可以不必在不同且以分類為主的論壇花費時間尋找答案,和在關鍵字搜索中的結果花費時間尋找答案。
本研究希望能針對問答型網站的文件做自動標籤分類,運用標籤推薦技術來幫助使用者能夠更有效率的找到需要的問題,也讓問答平台可以把這些由使用者所產生的大量問題分群歸類。
在研究過程蒐集Stack Exchange問答網站共20638個問題,使用naïve Bayes演算法與文件相似度計算的方式,進行標籤推薦,推薦適合的標籤給新進文件。在研究結果中,推薦標籤的準確率有64.2%
本研究希望透過自動分類標籤,有效地分類問題。幫助使用者有效率的找到需要的問題,也能把這些由使用者所產生的大量問題分群歸類。 / With User's behavior change. User access to new knowledge from the internet instead of from the traditional media. This Change leads to a lot new behavior. Social tagging is popular in recent years through a user tag to classify and annotate information. Unlike traditional taxonomy requiring items are classified into predefined categories, Social tagging is more elastic to adjust through the content change.
Q & A Website is the rise in recent years. Like Quora , Stack Overflow , yahoo Knowledge plus. User can interact with other people form this platform , in Q & A discussion, with People's experience and expertise to help the user find a satisfactory answer.
This study hopes to build a tag recommendation system for Q & A Website. The recommendation system can help people find the right problem efficiently , and let Q & A platform can put these numerous problems into the right place.
We collect 20,638 questions from Stack Exchange. Use naïve Bayes algorithm and document similarity calculation to recommend tag for the new document. The result of the evaluation show we can effectively recommend relevant tags for the new question.
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