3,305 research outputs found
Intent Models for Contextualising and Diversifying Query Suggestions
The query suggestion or auto-completion mechanisms help users to type less
while interacting with a search engine. A basic approach that ranks suggestions
according to their frequency in the query logs is suboptimal. Firstly, many
candidate queries with the same prefix can be removed as redundant. Secondly,
the suggestions can also be personalised based on the user's context. These two
directions to improve the aforementioned mechanisms' quality can be in
opposition: while the latter aims to promote suggestions that address search
intents that a user is likely to have, the former aims to diversify the
suggestions to cover as many intents as possible. We introduce a
contextualisation framework that utilises a short-term context using the user's
behaviour within the current search session, such as the previous query, the
documents examined, and the candidate query suggestions that the user has
discarded. This short-term context is used to contextualise and diversify the
ranking of query suggestions, by modelling the user's information need as a
mixture of intent-specific user models. The evaluation is performed offline on
a set of approximately 1.0M test user sessions. Our results suggest that the
proposed approach significantly improves query suggestions compared to the
baseline approach.Comment: A short version of this paper was presented at CIKM 201
On Suggesting Entities as Web Search Queries
The Web of Data is growing in popularity and dimension,
and named entity exploitation is gaining importance in many research
fields. In this paper, we explore the use of entities that can be extracted
from a query log to enhance query recommendation. In particular, we
extend a state-of-the-art recommendation algorithm to take into account
the semantic information associated with submitted queries. Our novel
method generates highly related and diversified suggestions that we as-
sess by means of a new evaluation technique. The manually annotated
dataset used for performance comparisons has been made available to
the research community to favor the repeatability of experiments
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SEARCHING BASED ON QUERY DOCUMENTS
Searches can start with query documents where search queries are formulated based on document-level descriptions. This type of searches is more common in domain-specific search environments. For example, in patent retrieval, one major search task is finding relevant information for new (query) patents, and search queries are generated from the query patents One unique characteristic of this search is that the search process can take longer and be more comprehensive, compared to general web search. As an example, to complete a single patent retrieval task, a typical user may generate 15 queries and examine more than 100 retrieved documents. In these search environments, searchers need to formulate multiple queries based on query documents that are typically complex and difficult to understand. In this work, we describe methods for automatically generating queries and diversifying search results based on query documents, which can be used for query vi suggestion and for improving the quality of retrieval results. In particular, we focus on resolving three main issues related to query document-based searches: (1) query generation, (2) query suggestion and formulation, and (3) search result diversification. Automatic query generation helps users by reducing the burden of formulating queries from query documents. Using generated queries as suggestions is investigated as a method of presenting alternative queries. Search result diversification is important in domain-specific search because of the nature of the query documents. Since query documents generally contain long complex descriptions, diverse query topics can be identified, and a range of relevant documents can be found that are related to these diverse topics. The proposed methods we study in this thesis explicitly address these three issues. To solve the query generation issue, we use binary decision trees to generate effective Boolean queries and labeling propagation to formulate more effective phrasal-concept queries. In order to diversify search results, we propose two different approaches: query-side and result-level diversification. To generate diverse queries, we identify important topics from query documents and generate queries based on the identified topics. For result-level diversification, we extract query topics from query documents, and apply state-of-the-art diversification algorithms based on the extracted topics. In addition, we devise query suggestion techniques for each query generation method. To demonstrate the effectiveness of our approach, we conduct experiments for various domain-specific search tasks, and devise appropriate evaluation measures for domain-specific search environments
Efficient Diversification of Web Search Results
In this paper we analyze the efficiency of various search results
diversification methods. While efficacy of diversification approaches has been
deeply investigated in the past, response time and scalability issues have been
rarely addressed. A unified framework for studying performance and feasibility
of result diversification solutions is thus proposed. First we define a new
methodology for detecting when, and how, query results need to be diversified.
To this purpose, we rely on the concept of "query refinement" to estimate the
probability of a query to be ambiguous. Then, relying on this novel ambiguity
detection method, we deploy and compare on a standard test set, three different
diversification methods: IASelect, xQuAD, and OptSelect. While the first two
are recent state-of-the-art proposals, the latter is an original algorithm
introduced in this paper. We evaluate both the efficiency and the effectiveness
of our approach against its competitors by using the standard TREC Web
diversification track testbed. Results shown that OptSelect is able to run two
orders of magnitude faster than the two other state-of-the-art approaches and
to obtain comparable figures in diversification effectiveness.Comment: VLDB201
Diversifying query suggestions based on query documents
Many domain-specific search tasks are initiated by document-length queries, e.g., patent invalidity search aims to find prior art related to a new (query) patent. We call this type of search Query Document Search. In this type of search, the initial query docu-ment is typically long and contains diverse aspects (or sub-topics). Users tend to issue many queries based on the initial document to retrieve relevant documents. To help users in this situation, we propose a method to suggest diverse queries that can cover multi-ple aspects of the query document. We first identify multiple que-ry aspects and then provide diverse query suggestions that are effective for retrieving relevant documents as well being related to more query aspects. In the experiments, we demonstrate that our approach is effective in comparison to previous query suggestion methods
Query Click and Text Similarity Graph for Query Suggestions
Query suggestion is an important feature of the search engine with the explosive and diverse growth of web contents. Different kind of suggestions like query, image, movies, music and book etc. are used every day. Various types of data sources are used for the suggestions. If we model the data into various kinds of graphs then we can build a general method for any suggestions. In this paper, we have proposed a general method for query suggestion by combining two graphs: (1) query click graph which captures the relationship between queries frequently clicked on common URLs and (2) query text similarity graph which finds the similarity between two queries using Jaccard similarity. The proposed method provides literally as well as semantically relevant queries for users’ need. Simulation results show that the proposed algorithm outperforms heat diffusion method by providing more number of relevant queries. It can be used for recommendation tasks like query, image, and product suggestion
Theory-based user modeling for personalized interactive information retrieval
In an effort to improve users’ search experiences during their information seeking process, providing a personalized information retrieval system is proposed to be one of the effective approaches. To personalize the search systems requires a good understanding of the users. User modeling has been approved to be a good method for learning and representing users. Therefore many user modeling studies have been carried out and some user models have been developed. The majority of the user modeling studies applies inductive approach, and only small number of studies employs deductive approach. In this paper, an EISE (Extended Information goal, Search strategy and Evaluation threshold) user model is proposed, which uses the deductive approach based on psychology theories and an existing user model. Ten users’ interactive search log obtained from the real search engine is applied to validate the proposed user model. The preliminary validation results show that the EISE model can be applied to identify different types of users. The search preferences of the different user types can be applied to inform interactive search system design and development
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