Meet BESS滿足貝絲

We hear so much about collaborative web-sites, wikis, user-generated content, the vast dialog that is the blogosphere, social media and social bookmarking, online networking, the whole web 2.0 revolution, that it is hard to imagine a time when finding useful information meant simply tapping in a few keywords into a search engine.我們聽到這麼多關於合作網站， Wikis的，用戶產生的內容，廣大對話，這是博客，社會，媒體和社會書籤，在線聯網，整個Web 2.0的革命，這是很難想像的時候，發現有用的資料，意味著簡單地竊聽在幾個關鍵字，成為一個搜索引擎。 But, I hear you scream, Google still reigns supreme and the vast majority of web users are still doing just that.但是，我聽到你的尖叫時， Google仍是至高無上的和廣大網絡用戶仍然這樣做。

Yes, you’re probably right, there is still a lot of educating to do, to persuade the average non-techie user, present excepted, of course, that collaborative methods of finding the best, most relevant, most informative, and perhaps even the most entertaining, information is the future.是的，您可能正確的，還有很多教育的事，說服平均非高科技的使用者，目前的例外，當然，合作的方法來尋找最好，最相關，最翔實，甚至最娛樂，信息是未來。

Part of the problem lies with the whole notion that beauty, or relevance more precisely, is in the eye of the beholder.問題的一部分在於與整個概念，即美，或關聯性更確切地說，是在眼睛的beholder 。 Web-spam aside, who’s to say that the first page of search engine hits is going to be relevant to members of a particular niche.網絡垃圾郵件之外，誰的說，第一頁的搜索引擎的訪問，將會給各位委員的有關某一特定利基。 Even with specialist search engines for movie buffs, photographers, artists, scientists, musicians, etc, there is still the problem of information overload.甚至與專科搜索引擎為電影發燒友，攝影師，藝術家，科學家，音樂家等，還有的問題，信息超載。 No search engine yet invented provides the perfect answers for everyone, each and every time.沒有搜索引擎尚未發明提供了完美的答案，每一個人，每一個時間。

Now,現在， Roman Shtykh羅馬shtykh and和 Qun Jin群進 of the Networked Information Systems Laboratory at Waseda University, in Japan, have also recognized that a one-size-fits-all to dealing with information overload is not the most effective way of satisfying anyone’s individual information needs.該網絡信息系統實驗室在早稻田大學在日本，也認識到， 一個尺寸適合所有人 ，以處理信息超載是不是最有效的方式滿足任何人的個人信息的需要。 But, rather than simply griping about the problem, they propose a new a new form of collaborative personalized search that attempts to understand your search.但，而不是簡單地griping有關問題，他們提出了一種新的一種新形式的合作個性化搜索，試圖以了解您的搜索。

They have developed a web information retrieval framework called Better Search and Sharing (BESS) that captures interactions between users and the system.他們制定了一個Web信息檢索的框架，所謂的更好的搜索和共享（貝絲）捕獲用戶之間的互動和制度。 This can then be used to produce user profile and then tailor results to personal interests dynamically, the same mechanism could be used to co-evaluate documents found valuable within a specific search context by users with similar interests, their subjective index.這便可以被用來生產使用者設定檔，然後度身訂造的結果，以個人利益的動態，同樣的機制可以用來合作，評估發現的文件寶貴的一個特定的搜索背景下，由用戶與類似的利益，自己的主觀指標。

Currently, systems such as目前，系統，如 Swiki swiki (social wiki, （社會的wiki ， Eurekster was unavailable at the time of writing eurekster是無法在當時的寫作 ), ） ， Rollyo rollyo , and the ，和 Google Custom Search Engine Google自定義搜索引擎 correspond to the vertical and mostly community-oriented approach to search personalization.對應於垂直和大多以社區為導向的方法搜索的個性化。 They allow communities to create personalized search engines around specific interests, Shtykh and Jin say.他們讓社區的創建個性化的搜索引擎圍繞著具體利益， shtykh和金說。 However, the advent of collaborative不過，來臨的合作 web 2.0 sites Web 2.0的網站 that favor the transition of each person’s activities from passive browsing to active participation have changed the search situation radically.有利於過渡，每個人的活動由被動瀏覽的積極參與，改變了搜索的情況徹底。

BESS is different.貝絲是不同的。 It is a community-oriented system, but has the features of a horizontal search system, like Google personalized search and a vertical search system like Google custom search rolled into one.這是一個面向社區的制度，但有特點的橫向搜索系統一樣， Google個性化搜索和垂直搜索系統一樣， Google自定義搜索合而為一。 “It performs searches on the information assets of both horizontal (the objective index unedited, non-controlled) and vertical (subjective index, evaluated, commented). “它會執行搜查，對信息資產的橫向（客觀指標，未經編輯的，非控制）和縱向的（主觀的指數，評價，評論） 。

The notion of the subjective index in our research is similar to the “social search” of the vertical community-oriented systems presented above, but differ in the higher degree of personalization for every user, the high granularity of the vertical search model and, finally, the way of collecting and (re-)evaluating the information pieces. 的概念，主觀指數在我們的研究是類似“社會搜索”的垂直面向社區的系統介紹了上述情況，但在不同程度較高的個性，為每一位用戶，高粒度的垂直搜索模式，並最終，方式，收集和（重新）評估的資料片。

In other words, groups of users are formed dynamically without user intervention, based on matching interests and expertise.在其他換句話說，用戶群體形成的動態無需用戶干預的基礎上，匹配的利益和專門知識。 The role of the community becomes indispensable for improving search quality and the evolution of the system, in general, the researchers add.的作用，成為社會不可缺少的改善搜索的質量和演變的系統，在一般情況，研究人員補充。

So, how does BESS work and what does it (she?) do?因此，如何貝絲的工作是什麼？用它（她）做什麼呢？ The proposed system consists of a contribution software component on the client side and the BESS collaborative information retrieval framework on the server side.擬議的系統包括一個貢獻的軟件組件在客戶端和貝絲協同信息檢索的框架內對服務器端。 The user contribution component would be a Firefox browser extension that allowed new elements (html) to be embedded into the search (engine) result pages (SERPs) next to every result.用戶貢獻的組成部分將是一個Firefox瀏覽器的擴展，允許新的元素（ HTML ）的被嵌入到搜索（引擎）的結果頁面（ serps ）旁邊的每一個結果。

On the server side, BESS will utilize a web proxy, software to analyze user behavior (not for spyware purposes, of course, but to allow the system to function), a profile construction engine.對服務器端，貝絲將利用Web代理服務器，軟件分析用戶行為（不為間諜軟件的目的，當然，但容許該系統功能） ， 1概況1 ，建設引擎。 It then captures user search and post-search behavior, analyzes them statistically generates updated profiles and then searches the growing subjective index to find the optimum hits.然後捕捉用戶的搜索和後的搜索行為，分析了他們在統計上產生的最新情況簡介，然後搜索日益增長的主觀指數，以尋找最佳安打。

The team has demonstrated proof of principle using the example of a search for Mitsubishi air conditioners.該小組已證明，證明的原則，使用的例子，搜索三菱空調。 They assume that user Annabelle is looking for information on air-conditioning units in Mitsubishi cars.他們以為用戶安娜貝麗是尋找資料，空氣調節系統的單位，在三菱汽車。 She searches for “Mitsubishi air conditioner”.她搜索“三菱空調” 。 Annabelle, however, is unaware that Mitsubishi Electric produces air-conditioning systems for buildings too and is thinking only of her car.安娜貝麗，不過，不知道三菱電機產生的空氣調節系統的建築物，也和思想，不僅是她的車。

Conventional SERPs will be a mixed bag of reviews, information, and documents about both vehicular and building A/C units.常規serps將是一個好壞參半的評語，信息和文件的有關車輛和建設/ C單位。 BESS, however, knows that Annabelle is keen on her car and has already been searching for information she flagged as interesting in her subjective index covering other option extras in cars.貝絲，不過，知道安娜貝麗是熱衷於她的車，並已一直在尋找她的資料，標示為有趣，在她的主觀指數涵蓋其他選擇額外的在車。 So, BESS presents hits on in-car air-conditioners from Mitsubishi instead of its domestic appliances entries.因此，貝絲介紹安打就在車內冷氣機從三菱不是它的家庭電器參賽作品。

Annabelle’s friend Carl who is in the same automobile enthusiasts group may subsequently be looking for car A/C units but searching by company name and the phrase air-conditioners too, but BESS, aware of their shared interest will again fire up the car equipment instead of the domestic units.安娜貝麗的朋友卡爾究竟是誰在同一汽車愛好者集團可能隨後將尋找汽車/ C單位，但搜索公司名稱和詞組的冷氣機，但貝絲，知道他們的共同利益，將再次火起來的汽車設備而不是國內的單位。 The flagging and tagging of positive hits could be automatic (done by BESS) or deliberate on the part of Annabelle and Carl.不振和標記的積極安打，可自動（所做的貝絲） ，或故意對部分安娜貝麗和卡爾。 Either way, BESS learns about their behavior and builds their community profiles accordingly.無論採用哪種方式，貝絲學習了解他們的行為，並建立他們的社會概況。

There is a drawback to BESS.有一個缺點，貝絲。 “Any web personalization system requires storing and analyzing personal information,” the researchers say, and “this is seen to be a problem by privacy advocates.” They point out that client-side personalization can alleviate some of the privacy concerns but would preclude BESS from working well with a community. “任何Web個性化系統需要存儲和分析的個人資料， ”研究人員說， “這是被視為一個問題隱私倡導者。 ”他們指出，客戶端的個性化可以紓緩部分的隱私問題，但會妨礙貝絲從工作，以及與社區。 “We have to consider how to ensure users’ privacy, probably by combining the client-side and server-side approaches for storing and processing user-sensitive information,” they conclude. “我們必須考慮如何確保usersâ € ™的隱私，可能相結合的客戶端和服務器端的方法，儲存和處理用戶的敏感資料， ”他們的結論。 However, there are millions of people using the likes of Google custom search and countless web 2.0 sites, so while privacy is a concern, the benefits of a more powerful and useful search system might outweigh such concerns for many users.但是，也有以百萬計的人使用喜歡的Google自定義搜索和無數的Web 2.0網站，因此，雖然隱私是一個關注的問題，受益於一個更強大的和有用的搜索系統，可能得不償失這種關切對於許多用戶。

Harnessing user contributions and dynamic profiling to better satisfy individual information search needs, in 利用用戶貢獻和動態profiling技術，以更好地滿足個人信息搜索的需要， International Journal of Web and Grid Services 國際雜誌的網絡和網格服務 , 2008, vol. 2008年，第二卷。 4, pp 63-79 4 ，第63-79