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A holistic, similarity-based approach for personalized ranking in web databases.

機(jī)譯：一種基于整體，基于相似度的方法，用于在Web數(shù)據(jù)庫中進(jìn)行個(gè)性化排名。

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With the advent of the Web, the notion of "information retrieval" has acquired a completely new connotation and currently encompasses several disciplines ranging from traditional forms of text and data retrieval in unstructured and structured repositories to retrieval of static and dynamic information from the contents of the surface and deep Web. From the point of view of the end user, a common thread that binds all these areas is to support appropriate alternatives for allowing users to specify their intent (i.e., the user input) and displaying the resulting output ranked in an order relevant to the users.;In the context of specifying an user's intent, the paradigms of querying as well as searching have served well, as the staple mechanisms in the process of information retrieval over structured and unstructured repositories. Processing queries over known, structured repositories (e.g., traditional and Web databases) has been well-understood, and search has become ubiquitous when it comes to unstructured repositories (e.g., document collections and the surface Web). Furthermore, searching structured repositories has been explored to a limited extent. However, there is not much work in querying unstructured sources which, we believe is the next step in performing focused retrievals.;Correspondingly, one of the important contributions of this dissertation is a novel semantic-guided approach, termed Query-By-Keywords (or QBK), to generate queries from search-like inputs for unstructured repositories. Instead of burdening the user with schema details, this approach utilizes pre-discovered semantic information in the form of taxonomies, relationship of keywords based on context, and attribute & operator compatibility to generate query skeletons that are subsequently transformed into queries. Additionally, progressive feedback from users is used to further improve the accuracy of these query skeletons. The overall focus thus, is to propose an alternative paradigm for the generation of queries on unstructured repositories using as little information from the user as possible.;Irrespective of the template for intent specification (i.e., either a search or a query request), the number of results typically returned in response to such intents, are often, extremely large. This is particularly true in the context of the deep Web where a large number of results are returned for queries on Web databases and choosing the most useful answer(s) becomes a tedious and time-consuming task. Most of the time the user is not interested in all answers; instead s/he would prefer those results, that are ranked based on her/his interests, characteristics, and past usage, to be displayed before the rest. Furthermore, these preferences vary as users and queries change.;Accordingly, in this dissertation, we propose a novel similarity -based framework for supporting user- and query-dependent ranking of query results in Web databases. This framework is based on the intuition that---for the results of a given query, similar users display comparable ranking preferences, and a user displays analogous ranking preferences over results of similar queries. Consequently, this framework is supported by two novel and comprehensive models of: (1) Query Similarity, and (2) User Similarity, proposed as part of this work. In addition, this ranking framework relies on the availability of a small yet representative set of ranking functions collected across several user-query pairs, in order to rank the results of a given user query at runtime. Appropriately, we address the subsequent problem of establishing a relevant workload of ranking functions that assists the similarity model in the best possible way to achieve the goal of user- and query-dependent ranking. Furthermore, we advance a novel probabilistic learning model that infers individual ranking functions (for this workload) based on the implicit browsing behavior displayed by users. We establish the effectiveness of this complete ranking framework by experimentally evaluating it on Google Base's vehicle and real estate databases with the aid of Amazon's Mechanical Turk users.
機(jī)譯：隨著Web的出現(xiàn)，“信息檢索”的概念已獲得了全新的含義，目前涵蓋了從非結(jié)構(gòu)化和結(jié)構(gòu)化存儲(chǔ)庫中的傳統(tǒng)形式的文本和數(shù)據(jù)檢索到從Web內(nèi)容中檢索靜態(tài)和動(dòng)態(tài)信息的多種學(xué)科表面和深層的Web。從最終用戶的角度來看，綁定所有這些區(qū)域的通用線程將支持適當(dāng)?shù)奶娲桨?，以允許用戶指定其意圖（即，用戶輸入）并顯示按與用戶相關(guān)的順序排列的結(jié)果輸出在指定用戶的意圖的上下文中，作為在結(jié)構(gòu)化和非結(jié)構(gòu)化存儲(chǔ)庫上進(jìn)行信息檢索過程中的主要機(jī)制，查詢和搜索的范式已很好地發(fā)揮了作用。在已知的結(jié)構(gòu)化存儲(chǔ)庫（例如，傳統(tǒng)數(shù)據(jù)庫和Web數(shù)據(jù)庫）上處理查詢已廣為人知，當(dāng)涉及到非結(jié)構(gòu)化存儲(chǔ)庫（例如，文檔集合和表面Web）時(shí)，搜索變得無處不在。此外，在有限程度上探索了搜索結(jié)構(gòu)化存儲(chǔ)庫。但是，查詢非結(jié)構(gòu)化源的工作并不多，我們認(rèn)為這是進(jìn)行集中檢索的下一步。相應(yīng)地，本論文的重要貢獻(xiàn)之一是一種新穎的語義引導(dǎo)方法，稱為Query-By-Keywords（或QBK），以從類似搜索的輸入中生成針對(duì)非結(jié)構(gòu)化存儲(chǔ)庫的查詢。這種方法不會(huì)以分類細(xì)節(jié)，基于上下文的關(guān)鍵字關(guān)系以及屬性和運(yùn)算符兼容性的形式使用預(yù)先發(fā)現(xiàn)的語義信息，而不是使用戶負(fù)擔(dān)架構(gòu)細(xì)節(jié)，從而生成查詢框架，隨后將其轉(zhuǎn)換為查詢。此外，使用來自用戶的漸進(jìn)式反饋可進(jìn)一步提高這些查詢框架的準(zhǔn)確性。因此，總的重點(diǎn)是提出一種替代范例，以使用盡可能少的來自用戶的信息在非結(jié)構(gòu)化存儲(chǔ)庫上生成查詢。不論意圖規(guī)范的模板（即搜索或查詢請(qǐng)求）如何，響應(yīng)于這種意圖而通常返回的結(jié)果的數(shù)量通常非常大。在深層Web的環(huán)境中尤其如此，在深層Web中，要返回大量結(jié)果以查詢Web數(shù)據(jù)庫，選擇最有用的答案成為一項(xiàng)繁瑣且耗時(shí)的任務(wù)。大多數(shù)時(shí)候，用戶對(duì)所有答案都不感興趣。取而代之的是，他/她希望將根據(jù)她/他的興趣，特征和過去使用情況進(jìn)行排名的結(jié)果顯示在其余結(jié)果之前。此外，這些偏好隨著用戶和查詢的變化而變化。因此，本文提出了一種新穎的基于相似度的框架，用于支持Web數(shù)據(jù)庫中用戶和查詢相關(guān)的查詢結(jié)果排名。該框架基于以下直覺：對(duì)于給定查詢的結(jié)果，相似的用戶顯示可比的排名首選項(xiàng)，并且用戶顯示相似查詢的結(jié)果類似的排名首選項(xiàng)。因此，此框架由兩個(gè)新穎而全面的模型支持：（1）查詢相似性，以及（2）用戶相似性，作為該工作的一部分。此外，此排名框架依賴于在幾個(gè)用戶查詢對(duì)之間收集的一組小而有代表性的排名函數(shù)的可用性，以便在運(yùn)行時(shí)對(duì)給定用戶查詢的結(jié)果進(jìn)行排名。適當(dāng)?shù)?，我們解決了隨后的問題，即建立相關(guān)的排名功能工作量，以最佳方式協(xié)助相似性模型實(shí)現(xiàn)依賴用戶和查詢的排名目標(biāo)。此外，我們提出了一種新穎的概率學(xué)習(xí)模型，該模型基于用戶顯示的隱式瀏覽行為來推斷個(gè)人排名功能（針對(duì)此工作負(fù)載）。我們通過在Amazon Mechanical Turk用戶的幫助下在Google Base的車輛和房地產(chǎn)數(shù)據(jù)庫上進(jìn)行實(shí)驗(yàn)性評(píng)估，來建立此完整排名框架的有效性。

著錄項(xiàng)

  • 作者

    Telang, Aditya.;

  • 作者單位

    The University of Texas at Arlington.;

  • 授予單位 The University of Texas at Arlington.;
  • 學(xué)科 Information Technology.;Computer Science.
  • 學(xué)位 Ph.D.
  • 年度 2011
  • 頁碼 196 p.
  • 總頁數(shù) 196
  • 原文格式 PDF
  • 正文語種 eng
  • 中圖分類
  • 關(guān)鍵詞

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