Improving web search through collaborative query recommendation

Adequate query formulation for web search is a very important prerequisite for successful retrieval of desired results. One productive way to improve it that is used in most popular search engines is query suggestion. Common search engines are quite good at suggesting words to general, highly popular queries, but their suggesting capability deteriorates swiftly when more specific queries are involved. In this paper, an approach is proposed to suggest words that are derived from actual search results. The approach guarantees some words will be suggested for any query, not just for most general or popular ones.

2003

Abstract Web search engines struggle to satisfy the needs of Web users. Users are notoriously poor at representing their needs in the form of a query, and search engines are poor at responding to vague queries. However progress has been made by introducing context into the search process. In this paper we describe and evaluate a novel approach to using context in Web search that adapts a generic search engine for the needs of a specialist community of users.

—Web recommender systems predict the needs of web users and provide them with recommendations to personalize their pages. Such systems had been expected to have a bright future, especially in ecommerce and E-learning environments. However, although they have been intensively explored in the Web Mining and Machine learning fields, and there have been some commercialized systems, the quality of the recommendation and the user satisfaction of such systems are still not conclusive. In this paper we proposed a more robust approach that leverages search query logs for automatically identifying query groups for a number of different users and record the query logs and their respective sessions. The system uses query reformulation and click graphs which contain useful information on user behavior when searching online. Such information can be used effectively for the task of organizing user search histories into query groups. The proposed technique finds value in combining with keyword semantic similarity and filtering which applies knowledge gained from these query groups in various applications such as providing query suggestions for web personalization by favoring the ranking of search results.

LWA, 2013

For meta search systems like digital library solutions, techniques like recommendation and especially query expansion are complex to realize because often the content of the information objects is not present or directly accessible. This approach takes new roads by integrating suggestion terms from two distinct sources in an interactive hybrid recommendation system. The terms are acquired through lexical-syntactical analysis using WordNet, as well as through association rule mining among the query logs.

2012

This paper presents a user study aiming to investigate the query reformulation in collaborative Web search. 7 pairs of participants were recruited and each pair worked as a team on two collaborative exploratory Web search tasks. Through the log analysis, we compared possible sources for participants to draw query terms from. The results show that both search and collaborative actions are possible resources for new query terms. Traditional resources for query expansion such as previous search histories and relevant documents are still important resources for new query terms. The content in chat and workspace generated by participants themselves seems more likely to be the resource for new query terms than that of their partners. Task types also affect the influences on query reformulations. For the academic task, previously saved relevance documents are the most important resources for new query terms while chat histories are the most important resources for the leisure task.

2002

Information retrieval (IR) systems utilize user feedback for generating optimal queries with respect to a particular in- formation need. However the methods that have been de- veloped in IR for generating these queries do not memo- rize information gathered from previous search processes, and hence can not use such information in new search pro- cesses. Thus each new search process

Second International Conference on the Innovative Computing Technology (INTECH 2012), 2012

Query Expansion Methods are proposed to solve many problems of information retrieval systems, but most of these methods do not use the information of interactions between the users and the system. In our approach, we applied a Query Recommendation Algorithm on a list of past user queries, to extract the most associated queries to the input query, and used it in a Probabilistic Query Expansion method, that is constructed as a language model to search in set of candidate terms for the most relevant terms for the initial query that we have to expand. The output of this approach is a table of terms that are candidates for the expansion of the user query, and their values of correlation with the whole query. We did our experiments using the database CISI of the standard collection of test SMART. The results show that the best values are reached by adding fifteen terms to the input queries, using the five most relevant documents of the input query and the five most relevant documents for each of the recommended queries used, for short and long queries. Our experiments shows also, that for short queries we need to use just the best recommended query in the process of expansion to have a very high value of the Interpolated Average Precision (IAP), but concerning long queries we need to employ also the second best recommended query to have the highest value of IAP. The final experiment was a comparison between our approach and the Rocchio Algorithm, and it shows that our approach gives best results except in the case of using four Recommended Queries (RQs) for short input queries.

Proceedings of the …, 2009

2004

In this paper we propose a method that, given a query submitted to a search engine, suggests a list of related queries. The related queries are based in previously issued queries, and can be issued by the user to the search engine to tune or redirect the search process. The method proposed is based on a query clustering process in which groups of semantically similar queries are identified. The clustering process uses the content of historical preferences of users registered in the query log of the search engine. The method not only discovers the related queries, but also ranks them according to a relevance criterion. Finally, we show with experiments over the query log of a search engine the effectiveness of the method.

Query recommendation is considered an effective assistant in enhancing keyword based queries in search engines and Web search software. Conventional approach to query recommendation has been focused on query-term based analysis over the user access logs. In this paper, we argue that utilizing the connectivity of a query-URL bipartite graph to recommend relevant queries can significantly improve the accuracy and effectiveness of the conventional query-term based query recommendation systems. We refer to the Query-URL Bipartite based query reCommendation approach as QUBIC. The QUBIC approach has two unique characteristics. First, instead of operating on the original bipartite graph directly using biclique based approach or graph clustering, we extract an affinity graph of queries from the initial query-URL bipartite graph. The affinity graph consists of only queries as its vertices and its edges are weighted according to a query-URL vector based similarity (distance) measure. By utilizing the query affinity graph, we are able to capture the propagation of similarity from query to query by inducing an implicit topical relatedness between queries. We devise a novel rank mechanism for ordering the related queries based on the merging distances of a hierarchical agglomerative clustering. We compare our proposed ranking algorithm with both naïve ranking that uses the query-URL similarity measure directly, and the single-linkage based ranking method. In addition, we make it possible for users to interactively participate in the query recommendation process, to bridge the gap between the determinacy of actual similarity values and the indeterminacy of users' information needs, allowing the lists of related queries to be changed from user to user and query to query, thus personalizing the query recommendation on demand. The experimental results from two query collections demonstrate the effectiveness and feasibility of our approach.