The use of implicit evidence for relevance feedback in web retrieval

In this paper we report on the application of two contrasting types of relevance feedback for web retrieval. We compare two systems; one using explicit relevance feedback (where searchers explicitly have to mark documents relevant) and one using implicit relevance feedback (where the system endeavours to estimate relevance by mining the searcher's interaction). The feedback is used to update the display according to the user's interaction. Our research focuses on the degree to which implicit evidence of document relevance can be substituted for explicit evidence. We examine the two variations in terms of both user opinion and search effectiveness

Mining Implicit Relevance Feedback from User Behavior for Web Question Answering

Training and refreshing a web-scale Question Answering (QA) system for a multi-lingual commercial search engine often requires a huge amount of training examples. One principled idea is to mine implicit relevance feedback from user behavior recorded in search engine logs. All previous works on mining implicit relevance feedback target at relevance of web documents rather than passages. Due to several unique characteristics of QA tasks, the existing user behavior models for web documents cannot be applied to infer passage relevance. In this paper, we make the first study to explore the correlation between user behavior and passage relevance, and propose a novel approach for mining training data for Web QA. We conduct extensive experiments on four test datasets and the results show our approach significantly improves the accuracy of passage ranking without extra human labeled data. In practice, this work has proved effective to substantially reduce the human labeling cost for the QA service in a global commercial search engine, especially for languages with low resources. Our techniques have been deployed in multi-language services.Comment: Accepted by KDD 202

Log Based Feedback Method For Online Web Image Ranking Using Query Speci?c Semantic Signatures

Image re-ranking, is an effective way to improve the results of web-based image search and has been adopted by cur-rent commercial search engines. Various methods like relevance feedback, context based image retrieval, query speci?c semantic signature has been proposed for giving better performance in web image re-ranking. However each of these methods has their own advantages and disadvantages. To overcome lacuna of the existing system we are proposing we propose log based image re-ranking. This paper provides the technical achievements in research area of the web image re-ranking and proposed log based relevance feedback method for online web image Re-ranking. DOI: 10.17762/ijritcc2321-8169.15073

Combining relevance information in a synchronous collaborative information retrieval environment

Traditionally information retrieval (IR) research has focussed on a single user interaction modality, where a user searches to satisfy an information need. Recent advances in both web technologies, such as the sociable web of Web 2.0, and computer hardware, such as tabletop interface devices, have enabled multiple users to collaborate on many computer-related tasks. Due to these advances there is an increasing need to support two or more users searching together at the same time, in order to satisfy a shared information need, which we refer to as Synchronous Collaborative Information Retrieval. Synchronous Collaborative Information Retrieval (SCIR) represents a significant paradigmatic shift from traditional IR systems. In order to support an effective SCIR search, new techniques are required to coordinate users' activities. In this chapter we explore the effectiveness of a sharing of knowledge policy on a collaborating group. Sharing of knowledge refers to the process of passing relevance information across users, if one user finds items of relevance to the search task then the group should benefit in the form of improved ranked lists returned to each searcher. In order to evaluate the proposed techniques we simulate two users searching together through an incremental feedback system. The simulation assumes that users decide on an initial query with which to begin the collaborative search and proceed through the search by providing relevance judgments to the system and receiving a new ranked list. In order to populate these simulations we extract data from the interaction logs of various experimental IR systems from previous Text REtrieval Conference (TREC) workshops

Tweetement: Pseudo-relevance Feedback for Twitter Search

Microblogging platforms such as Twitter let users communicate with short messages. Due to the messages’ short content and the users’ tendency to type short queries while searching, it is particularly challenging to locate useful tweets that match user queries. The fundamental problems of word mismatch due to ambiguity are especially acute. To solve this problem, this thesis explores and compares multiple automatic query expansion methods that involve the most frequent hashtags and keywords. We built a Web service that provides real-time Twitter Search results incorporating automatic query expansion. Six pseudo-relevance feedback methods were studied and the numbers indicate that results without query expansion perform just as well as results with query expansion. However, the expanded queries find different relevant tweets than the original query, indicating, from multiple methods, that combining the results is a fruitful area for future investigations. Keywords: microblog, Twitter Search, query expansion, pseudo-relevance feedback, Web servic