Query Recommendation Research Articles

Query Recommendation Based on Document Content User Focuses on

To the problem of text elements outside main content of clicked documents have big negative impact on the precision of query recommendation, this paper proposed a query recommendation method based on document content user focuses on, which fused similarity computation among queries with the filter of text elements outside main content of clicked documents. Experimental result shows that the method proposed by this paper can raise the precision of similarity computation among queries, and then raise the precision of query recommendation.

A Survey Report on the Novel Approach on Use of Recommendation Outline in Query Recommender System

The DBMS applications are becoming increasingly popular in the scientific community to support the interactive exploration of huge data. A result of heavy usage has also lead to a lot of tavles generated in the warehouse. This has tremendously increased the need for recommendation system & various tools for the user as user is not capable to explore such huge data by using Structured Query language. This motivates to turn towards the idea of query recommendation system which will assist the technical & non-technical users to generate top-N query recommendations as per their needs. In this survey paper, the focus is given on opportunities that exist in domain of query recommendation, brief description of recommendation outline and techniques. Along with this, the terminology to identify the similarity and comparison of fragment based approach & matrix factorization approach is also explored. The Database Management Systems are used to organize, maintain and retrieve the huge amount of data. To acquire the essential data the user need to interact with the database by taking the support of declarative Structured Query Language (SQL). Most of times, it is observed that user faces the difficulties in interacting with the database by using SQL query language as the database have hundreds of schema's and thousands of attributes. This problem has been solved with the support of Database Management Applications such as SAP, Easy Query, and Microsoft Access. These applications involve the user interaction and manual editing of the SQL query components but when the user is unacquainted about the underlying database structure then it becomes difficult for the user to formulate the query. To formulate the query, the research work has been done (1) and that results into the query assistant tool which helps the user to either formulate the query or to recommend the relevant query items based on formulated query. This work is the reflection of the web recommendation techniques those are used in gaining the on-line marketing intelligence. The web recommendation techniques and algorithms have their strong relation with the Information Retrieval Science (IR). In IR, the recommendation techniques and algorithms are used to retrieve the most relevant top K documents as per the formulated query. Most of times the Business Verticals have their own off-line database and the user will be interested in exploration of the database and faces the difficulties. Therefore, it may also possible to extend the idea specified by the authors (1) to provide the Query Recommendation Assistant Tool that enable the user to formulate and refine the SQL query. This take off to cataloged view on how to make the imperative use of IR and the web recommendation techniques to generate and recommend the top K relevant query objects by identifying the similarities among the SQL query fragments. This becomes the novel approach that handles crucial responsibility in proposing the assistant tool to retrieve data over the database. The rest of the paper is organized as follows: section-2 describes the related work and the section-3 concentrates on recommendation outline. The section-4 describes the recommendation techniques and similarity metrics those have been used in finding the similarity among the query fragments. The section-5 focuses on terminology used to calculate weights of items required for finding similarity. The section-6 states the comparison between the fragment based approach and matrix factorization approach.

Analyzing, Detecting, and Exploiting Sentiment in Web Queries

The Web contains an increasing amount of biased and opinionated documents on politics, products, and polarizing events. In this article, we present an indepth analysis of Web search queries for controversial topics, focusing on query sentiment. To this end, we conduct extensive user assessments and discriminative term analyses, as well as a sentiment analysis using the SentiWordNet thesaurus, a lexical resource containing sentiment annotations. Furthermore, in order to detect the sentiment expressed in queries, we build different classifiers based on query texts, query result titles, and snippets. We demonstrate the virtue of query sentiment detection in two different use cases. First, we define a query recommendation scenario that employs sentiment detection of results to recommend additional queries for polarized queries issued by search engine users. The second application scenario is controversial topic discovery, where query sentiment classifiers are employed to discover previously unknown topics that trigger both highly positive and negative opinions among the users of a search engine. For both use cases, the results of our evaluations on real-world data are promising and show the viability and potential of query sentiment analysis in practical scenarios.

Search Query Recommendations using Hybrid User Profile with Query Logs

The exhaustive information available in the World Wide Web indeed, unfolds the challenge of exploring the apposite, precise and relevant data in every search result. Apparently, in such instances of web-searching, Query Recommendations is the ultimate application in information retrieval. The Query Recommendation technique provides alternative queries to the user to frame a meaningful and relevant query in the future and rapidly satisfies their information needs. Similar query

An Architecture for Unstructured Data Management

As the information age is coming, there is a vast amount of information available in the Internet. Most of data on Web are unstructured. But the significant data should be organized and stored in a suitable way for future purposes. One of the unsolved problems is the management of unstructured data. The unstructured data such as presentation, spreadsheet, text document, memo, images and web pages are difficult to manage while the data become a large scale and the users have different requirements and interests. In this paper, we proposed an architecture for unstructured data management by integrating source query, data collection and data management to solve these problems. The data collection layer extracts the data we care about, we use the existing tools to extract automatic and we can also add the data to the repository manually. The data management layer manage all the collection data by classifying the data, selecting nodes to store and managing centralized as index. The source query layer allows users to query and get the data diversity according the adaptive query service and recommendation service. Finally, we implemented a prototype system OCourse based on this system architecture to show its feasible and efficient.

An effective query recommendation approach using semantic strategies for intelligent information retrieval

With the explosive growth of web information, search engines have become the mainstream tools of information retrieval (IR). However, a notable problem emerged in the current IR systems is that the input queries are usually too short and too ambiguous to express their actual idea which largely affects the performance of IR systems. In this study, a novel query recommendation technology which suggests a list of related queries is proposed to resolve these problems. The query concepts can be firstly extracted from the web-snippets of the search result returned by the input query. A bipartite graph is subsequently built to identify the related queries, and the query similarity can be calculated by such bipartite graph. Moreover, by analyzing the URLs clicked by users, we find that some tokens appeared in URLs are very meaningful, especial for some typical topic-based pages. Therefore, these potential tokens which can provide a brief description from the subject of the URL are also considered. In order to reveal the real semantics between queries, the approach TF-IQF model is further discussed, and three features of a query, i.e. clicked documents, associated query and reversed query, are utilized in our approach in depth. Such a method could hopefully acquire the comprehensive idea of a query. To investigate how these three features could be used effectively for query recommendation in search engine, we adopt the benchmark evaluation criterions in our experiments, and the experimental results show its promising results in comparison with state of the art methods.

A robust approach for finding conceptually related queries using feature selection and tripartite graph structure

The information explosion on the Internet has placed high demands on search engines. Despite the improvements in search engine technology, the precision of current search engines is still unsatisfactory. Moreover, the queries submitted by users are short, ambiguous and imprecise. This leads to a number of problems in dealing with similar queries. The problems include lack of common keywords, selection of different documents by the search engine and lack of common clicks etc. These problems render the traditional query clustering methods unsuitable for query recommendations. In this paper, we propose a new query recommendation system. For this, we have identified conceptually related queries by capturing users’ preferences using click-through graphs of web search logs and by extracting the best features, relevant to the queries, from the snippets. The proposed system has an online feature extraction phase and an offline phase in which feature filtering and query clustering are performed. Query clustering is carried out by a new tripartite agglomerative clustering algorithm, Query-Document-Concept Clustering, in which the documents are used innovatively to decouple queries and features/concepts in a tripartite graph structure. This results in clusters of similar queries, associated clusters of documents and clusters of features. We model the query recommendation problem in four different ways. Two models are non-personalized and personalized content-ignorant models. Other two are non-personalized and personalized content-aware models. Three similarity measures are introduced to estimate different kinds of similarities. Experimental results show that the proposed approach has better precision, recall and F-measure than the existing approaches.

Similarity measures for OLAP sessions

OLAP queries are not normally formulated in isolation, but in the form of sequences called OLAP sessions. Recognizing that two OLAP sessions are similar would be useful for different applications, such as query recommendation and personalization; however, the problem of measuring OLAP session similarity has not been studied so far. In this paper, we aim at filling this gap. First, we propose a set of similarity criteria derived from a user study conducted with a set of OLAP practitioners and researchers. Then, we propose a function for estimating the similarity between OLAP queries based on three components: the query group-by set, its selection predicate, and the measures required in output. To assess the similarity of OLAP sessions, we investigate the feasibility of extending four popular methods for measuring similarity, namely the Levenshtein distance, the Dice coefficient, the tf–idf weight, and the Smith–Waterman algorithm. Finally, we experimentally compare these four extensions to show that the Smith–Waterman extension is the one that best captures the users’ criteria for session similarity.

QUBiC: An adaptive approach to query-based recommendation

Search engine users often encounter the difficulty of phrasing the precise query that could lead to satisfactory search results. Query recommendation is considered an effective assistant in enhancing keyword-based queries in search engines and Web search software. In this paper, we present a Query-URL Bipartite based query reCommendation approach, called QUBiC. It utilizes the connectivity of a query-URL bipartite graph to recommend related queries and can significantly improve the accuracy and effectiveness of personalized query recommendation systems comparing with the conventional pairwise similarity based approach. The main contribution of the QUBiC approach is its three-phase framework for personalized query recommendations. The first phase is the preparation of queries and their search results returned by a search engine, which generates a historical query-URL bipartite collection. The second phase is the discovery of similar queries by extracting a query affinity graph from the bipartite graph, instead of operating on the original bipartite graph directly using biclique-based approach or graph clustering. The query affinity graph consists of only queries as its vertices and its edges are weighted according to a query-URL vector based similarity (dissimilarity) measure. The third phase is the ranking of similar queries. We devise a novel rank mechanism for ordering the related queries based on the merging distances of a hierarchical agglomerative clustering (HAC). By utilizing the query affinity graph and the HAC-based ranking, we are able to capture the propagation of similarity from query to query by inducing an implicit topical relatedness between queries. Furthermore, the flexibility of the HAC strategy makes it possible for users to interactively participate in the query recommendation process, and helps 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 adaptively recommending related queries on demand. Our experimental evaluation results show that the QUBiC approach is highly efficient and more effective compared to the conventional query recommendation systems, yielding about 13.3 % as the most improvement in terms of precision.

Ranking on Data Manifold with Sink Points

Ranking is an important problem in various applications, such as Information Retrieval (IR), natural language processing, computational biology, and social sciences. Many ranking approaches have been proposed to rank objects according to their degrees of relevance or importance. Beyond these two goals, diversity has also been recognized as a crucial criterion in ranking. Top ranked results are expected to convey as little redundant information as possible, and cover as many aspects as possible. However, existing ranking approaches either take no account of diversity, or handle it separately with some heuristics. In this paper, we introduce a novel approach, Manifold Ranking with Sink Points (MRSPs), to address diversity as well as relevance and importance in ranking. Specifically, our approach uses a manifold ranking process over the data manifold, which can naturally find the most relevant and important data objects. Meanwhile, by turning ranked objects into sink points on data manifold, we can effectively prevent redundant objects from receiving a high rank. MRSP not only shows a nice convergence property, but also has an interesting and satisfying optimization explanation. We applied MRSP on two application tasks, update summarization and query recommendation, where diversity is of great concern in ranking. Experimental results on both tasks present a strong empirical performance of MRSP as compared to existing ranking approaches.

Development of the Potential Query Recommendation System using User's Search History

In this paper, a user search history based potential query recommendation system is proposed to enable the user of information search system to represent one's potential desire for information in terms of query and to facilitate the desired information to be searched. The proposed system has analyzed the association with the existing users's search histories based on the users' search query, and it has extracted the users's potential desire for information. The extracted potential desire for information is represented in terms of recommended query and thereby made recommendations to users. In order to analyze the effectiveness of the system proposed in this paper, we conducted behavioral experiments by using search histories of 27656. As a result of behavioral experiments, the experiment subjects were found to show a statistically significant higher level of satisfaction when using the proposed system as compared to using general search engines.

Query Recommendation for Optimizing the Search Engine Results

Search engine query have been paying awareness in current days. Since web contents develop, the importance of search engines became more essential and at the same instance user performance reduces. Query recommendation is a method to improve search results in web. This paper presents a method for mining search engine query logs to obtain fast query recommendation on a large scale. Search engines generally return long list of ranked pages, finding the important information related to a particular topic is becoming increasingly difficult and therefore, optimized search engines become one of the most popular solution available. In this work, an algorithm has been applied to recommend related queries to a query submitted by user. For this, the technology used for allowing query recommendations is query log which contains attributes like query name, clicked URL, rank, time. Then, the similarity based on keywords as well as clicked URL’s is calculated. Additionally, clusters have been obtained by combining the similarities of both keywords and clicked URL’s to perform query clustering and further the sequential order of clicked URLs in each cluster has been discovered using the modified version of an existing sequential pattern mining technique. The final by product is further optimized by re-ranking the pages using the discovered sequential patterns. The proposed system here, is based on learning from query logs that predicts user information requirements and reduces the seek time of the user within the search result list.

Adaptive domain modelling for information retrieval

Modern search engines employ a number of interactive features to assist users in exploring the document collection and expressing their information needs. Providing these features require knowledge about the document collection in the domain, i.e. a domain model. These models may be difficult to obtain and even if they are available, they may become out of date when the document collection changes or the users start to view the domain differently. In this thesis we propose to use implicit feedback left by users while they interact with search engines to build domain models that evolve over time. These models can adapt to changes in the domain as reflected in the search trend of the user population. We validate these models in two different IR tasks. The major application is query recommendation where previous studies focused on Web search in general or did not consider the temporal aspect of recommendations. Our models address these issues as they are targeted to specific domains such as enterprise search or digital libraries. We furthermore devise an automatic evaluation methodology that allows us to perform extensive evaluation of our adaptive models and observe their performance over time. Using query logs collected from two academic institutions, the evaluation framework assesses the impact of different factors on their performance. The second application of these models is query session retrieval. The query session retrieval problem extends the traditional ad-hoc retrieval by taking into account the previous user interactions with the retrieval system within the same session when answering the query. In this context rather than explicitly providing the user with relevant queries to their information needs, our adaptive models implicitly derive query expansions relevant to the user information needs as identified in the sessions by mapping the session to similar sessions inferred from the models.

Query Recommendation for Long Tail Queries-A Review Paper

Large volume of queries over large volume of users allows search engine to build methods for generating query suggestion for input query. Query recommendation methods are powerful technique to generate related queries or alternate queries as a query suggestion for original query which is given by user in the search engine first time. In this review paper we discussed about query recommendation methods and comparison between different methods how the query suggestion is given by these methods for the input query by user in search engine. Some query recommendation method do not covers the unseen and rare queries but some of them covers these queries by some additional feature added such as generalize the query token of input query by a suitable place holder from hierarchy tool wordnet3.0 or wekepedia by yago mapping. These generalization techniques are also discussed in this paper.

Generating suggestions for queries in the long tail with an inverted index

This paper proposes an efficient and effective solution to the problem of choosing the queries to suggest to web search engine users in order to help them in rapidly satisfying their information needs. By exploiting a weak function for assessing the similarity between the current query and the knowledge base built from historical users’ sessions, we re-conduct the suggestion generation phase to the processing of a full-text query over an inverted index. The resulting query recommendation technique is very efficient and scalable, and is less affected by the data-sparsity problem than most state-of-the-art proposals. Thus, it is particularly effective in generating suggestions for rare queries occurring in the long tail of the query popularity distribution. The quality of suggestions generated is assessed by evaluating the effectiveness in forecasting the users’ behavior recorded in historical query logs, and on the basis of the results of a reproducible user study conducted on publicly-available, human-assessed data. The experimental evaluation conducted shows that our proposal remarkably outperforms two other state-of-the-art solutions, and that it can generate useful suggestions even for rare and never seen queries.

Fast Query Recommendation by Search

Query recommendation can not only effectively facilitate users to obtain their desired information but alsoincrease ads’ click-through rates. This paper presentsa general and highly efficient method for query recommendation. Given query sessions, we automatically generate many similar and dissimilar query-pairs as the prior knowledge. Then we learn a transformation from the prior knowledge to move similar queries closer such that similar queries tend to have similar hash values.This is formulated as minimizing the empirical error on the prior knowledge while maximizing the gap between the data and some partition hyperplanes randomly generated in advance. In the recommendation stage, we search queries that have similar hash values to the given query, rank the found queries and return the top K queries as the recommendation result. All the experimental results demonstrate that our method achieves encouraging results in terms of efficiency and recommendation performance.

How do users describe their information need: Query recommendation based on snippet click model

Query recommendation helps users to describe their information needs more clearly so that search engines can return appropriate answers and meet their needs. State-of-the-art researches prove that the use of users’ behavior information helps to improve query recommendation performance. Instead of finding the most similar terms previous users queried, we focus on how to detect users’ actual information need based on their search behaviors. The key idea of this paper is that although the clicked documents are not always relevant to users’ queries, the snippets which lead them to the click most probably meet their information needs. Based on analysis into large-scale practical search behavior log data, two snippet click behavior models are constructed and corresponding query recommendation algorithms are proposed. Experimental results based on two widely-used commercial search engines’ click-through data prove that the proposed algorithms outperform practical recommendation methods of these two search engines. To the best of our knowledge, this is the first time that snippet click models are proposed for query recommendation task.

Random walk based rank aggregation to improving web search

In Web search, with the aid of related query recommendation, Web users can revise their initial queries in several serial rounds in pursuit of finding needed Web pages. In this paper, we address the Web search problem on aggregating search results of related queries to improve the retrieval quality. Given an initial query and the suggested related queries, our search system concurrently processes their search result lists from an existing search engine and then forms a single list aggregated by all the retrieved lists. We specifically propose a generic rank aggregation framework which consists of three steps. First we build a so-called Win/Loss graph of Web pages according to a competition rule, and then apply the random walk mechanism on the Win/Loss graph. Last we sort these Web pages by their ranks using a PageRank-like rank mechanism. The proposed framework considers not only the number of wins that an item won in competitions, but also the quality of its competitor items in calculating the ranking of Web page items. Experimental results show that our search system can clearly improve the retrieval quality in a parallel manner over the traditional search strategy that serially returns result lists. Moreover, we also provide empirical evidences as to demonstrate how different rank aggregation methods affect the retrieval quality.

Guiding Students To Answers: Query Recommendation

This paper reports on a guided navigation system built on the textbook search engine developed at Anadolu University to support distance education students. The search engine uses Turkish Language specific language processing modules to enable searches over course material presented in Open Education Faculty textbooks. We implemented a guided navigation engine by using query log mining to our application. It makes use of previous users‘ sessions to help students find information they are looking for by using fewer queries. We used item-based similarity to do query recommendation. This paper describes the search application, query expansion and evaluation of the system over existing query logs. We show that our system suggested relevant queries with a success rate of 85%.

Query Recommendation for Improving Search Engine Results

As web contents grow, the importance of search engines become more critical and at the same time user satisfaction decreases. Query recommendation is a new approach to improve search results in web. In this paper a method is proposed that, given a query submitted to a search engine, suggests a list of queries that are related to the user input query. The related queries are based on previously issued queries, and can be issued by the user to the search engine to tune or redirect the search process. The proposed method is based on clustering processes in which groups of semantically similar queries are detected. The clustering process uses the content of historical preferences of users registered in the query log of the search engine. This facility provides queries that are related to the ones submitted by users in order to direct them toward their required information. This method not only discovers the related queries but also ranks them according to a similarity measure. The method has been evaluated using real data sets from the search engine query log.