利用知識庫與搜尋紀錄提升網頁搜尋的排序一致性

Abstract

本論文提出了一個創新的概念－在網頁搜尋中的排序一致性 (ranking consistency in web search) 。相關排序 (relevance ranking) 是在創建一個有效的網頁搜尋系統時會碰到最大的問題之一。給定一些具有相似搜尋意圖 (search intents) 的查詢 (queries) ，常見的作法是將個別的查詢分別去優化排序模型 (ranking models) 。因此，在現代的搜尋引擎中，會有不一致的排序結果。但我們預期具有相似意圖的查詢應該保有排序一致性。本論文的目的在於為了提升網頁搜尋的相關排序，而學習搜尋結果的排序一致性。藉由利用知識庫 (knowledge base)與搜尋紀錄 (search logs) ，我們提出了一個同時提升相關排序與排序一致性的重新排序模型 (re-ranking model) 。據我們所知，本論文提出了第一個藉由提升排序一致性來提升相關排序效能的解法。實驗的結果也顯示出我們所提出的方法顯著地提升了相關排序以及排序一致性。兩個在群眾發包平台 (crowd-sourcing platform) Amazon Mechanical Turk 所進行的調查也顯示出使用者對於排序一致性相當敏感，也比較喜愛由我們所提出之方法所得到較為一致的排序結果。

In this paper, we propose a new idea called ranking consistency in web search. Relevance ranking is one of the biggest problems in creating an effective web search system. Given some queries with similar search intents, conventional approaches typically only optimize ranking models by each query separately. Hence, there are inconsistent rankings in modern search engines. It is expected that the search results of different queries with similar search intents should preserve ranking consistency. The aim of this paper is to learn consistent rankings in search results for improving the relevance ranking in web search. We then propose a re-ranking model aiming to simultaneously improve relevance ranking and ranking consistency by leveraging knowledge bases and search logs. To the best of our knowledge, our work offers the first solution to improving relevance rankings with ranking consistency. Extensive experiments have been conducted using the Freebase knowledge base and the large-scale query-log of a commercial search engine. The experimental results show that our approach significantly improves relevance ranking and ranking consistency. Two user surveys on Amazon Mechanical Turk also show that users are sensitive and prefer the consistent ranking results generated by our model.