PRESY: A Context Based Query Reformulation Tool for Information Retrieval on the Web

Problem Statement: The huge number of information on the web as well as the growth of new inexperienced users creates new challenges for information retrieval. It has become increasingly difficult for these users to find relevant documents that satisfy their individual needs. Certainly the current search engines (such as Google, Bing and Yahoo) offer an efficient way to browse the web content. However, the result quality is highly based on uses queries which need to be more precise to find relevant documents. This task still complicated for the majority of inept users who cannot express their needs with significant words in the query. For that reason, we believe that a reformulation of the initial user's query can be a good alternative to improve the information selectivity. This study proposes a novel approach and presents a prototype system called PRESY (Profile-based REformulation SYstem) for information retrieval on the web. Approach: It uses an incremental approach to categorize users by constructing a contextual base. The latter is composed of two types of context (static and dynamic) obtained using the users' profiles. The architecture proposed was implemented using .Net environment to perform queries reformulating tests. Results: The experiments gives at the end of this article show that the precision of the returned content is effectively improved. The tests were performed with the most popular searching engine (i.e. Google, Bind and Yahoo) selected in particular for their high selectivity. Among the given results, we found that query reformulation improve the first three results by 10.7% and 11.7% of the next seven returned elements. So as we can see the reformulation of users' initial queries improves the pertinence of returned content.Comment: 8 page

Ephemeral relevance and user activities in a search session

We study relevance judgment and user activities in a search session. We focus on ephemeral relevance—a contextual measurement regarding the amount of useful information a searcher acquired from a clicked result at a particular time—and two primary types of search activities—query reformulation and click. The purpose of the study is both explanatory and practical. First, we examine the influence of different factors on ephemeral relevance and user activities in a search session. Second, we leverage short-term search history and implicit feedback in a session to predict ephemeral relevance and future search activities. The main findings include: 1. As a contextual usefulness measurement, ephemeral relevance differs from both topical relevance judgment and context-independent usefulness assessment. We show ephemeral relevance significantly relates to a wide range of factors, including topical relevance, novelty, understandability, reliability, effort spent, and search task. The difference between ephemeral relevance and context-independent usefulness assessment is linked to judgment criteria, novelty, effort spent, and changes in user’s perceptions of a search result. 2. Ephemeral relevance can be predicted accurately using implicit feedback signals without any manual explicit judgments. We generalize existing implicit feedback methods from using information related to a single result to those based on user activities in a whole session, achieving a correlation as high as 0.5 between the predicted and real judgments. 3. We show choices of word changes in query reformulation and click decisions significantly relate to recent search history, such as the contents and effectiveness of previous search queries, the contents of the results viewed and clicked in previous searches, etc. 4. Leveraging short-term search history in a session and other information, we can predict word changes in query reformulation and click decisions with different levels of accuracies. These findings help disclose and explain the dynamics of relevance and user activities in a search session. The developed techniques provide effective support for developing interactive IR systems

An Extended Relevance Model for Session Search

The session search task aims at best serving the user's information need given her previous search behavior during the session. We propose an extended relevance model that captures the user's dynamic information need in the session. Our relevance modelling approach is directly driven by the user's query reformulation (change) decisions and the estimate of how much the user's search behavior affects such decisions. Overall, we demonstrate that, the proposed approach significantly boosts session search performance

Task-Oriented Query Reformulation with Reinforcement Learning

Search engines play an important role in our everyday lives by assisting us in finding the information we need. When we input a complex query, however, results are often far from satisfactory. In this work, we introduce a query reformulation system based on a neural network that rewrites a query to maximize the number of relevant documents returned. We train this neural network with reinforcement learning. The actions correspond to selecting terms to build a reformulated query, and the reward is the document recall. We evaluate our approach on three datasets against strong baselines and show a relative improvement of 5-20% in terms of recall. Furthermore, we present a simple method to estimate a conservative upper-bound performance of a model in a particular environment and verify that there is still large room for improvements.Comment: EMNLP 201

Enhancing Query Reformulation Performance by Combining Content and Hypertext Analyses

Information retrieval techniques play a critical role in the development of the information systems. Different searches have focused on the way of improving the retrieval effectiveness. Query expansion via relevance feedback is a good technique that proved to be a good way to improve the retrieval performance. In this paper, we investigate new methods to improve the query reformulation process. A two step process is employed to reformulate query. In a preliminary step, a local set of documents is built from the retrieved result. In a second step, a co-occurrence analysis is performed on the local document set to deduce the terms to be used for the query expansion. To build the local set we use firstly a content-based analysis. It is a similarity study between the retrieved documents and the query. The second method combines content and hypertext analyses to achieve the local set construction. The TREC1 frame is used to evaluate the proposed processes