Intent-aware query similarity

@inproceedings{Guo2011IntentawareQS,
  title={Intent-aware query similarity},
  author={J. Guo and Xueqi Cheng and Gu Xu and Xiaofei Zhu},
  booktitle={CIKM '11},
  year={2011}
}
Query similarity calculation is an important problem and has a wide range of applications in IR, including query recommendation, query expansion, and even advertisement matching. Existing work on query similarity aims to provide a single similarity measure without considering the fact that queries are ambiguous and usually have multiple search intents. In this paper, we argue that query similarity should be defined upon search intents, so-called intent-aware query similarity. By introducing… 

Figures and Tables from this paper

Finding similar queries based on query representation analysis
TLDR
The Click Model extracts credible transition probability from queries to URLs, and describes a query without considering web contents, while the Term Model focuses on representing a query via term distribution over its main entities and purposes, which can better capture information needs behind short and ambiguous keyword queries.
Personalized query suggestion diversification in information retrieval
TLDR
The experimental results show that the proposed personalized query suggestion diversification (PQSD) model achieves its best performance when only queries with clicked documents are taken as search context rather than all queries, especially when more query suggestions are returned in the list.
An Optimization Framework for Propagation of Query-Document Features by Query Similarity Functions
TLDR
This paper introduces two different approaches (linear weighting approach and tree-based approach) to learn a function of values of the similarity function andvalues of the feature for the similar queries w.r.t. the given document.
User Intent in Multimedia Search
TLDR
A thorough survey of multimedia information retrieval research directed at the problem of enabling search engines to respond to user intent is presented, including a differentiation from related, often-confused concepts of search intent.
More than relevance: high utility query recommendation by mining users' search behaviors
TLDR
This paper proposes a novel generative model, referred to as Query Utility Model (QUM), to capture query utility by simultaneously modeling users' reformulation and click behaviors, and shows that, this approach is more effective in helping users find relevant search results and thus satisfying their information needs.
Learning to Rank User Queries to Detect Search Tasks
TLDR
Experiments on a real-world search engine log show that query similarity functions learned using L2R lead to better performing GQC implementations when compared to similarity functions induced by other state-of-the-art machine learning solutions, such as logistic regression and decision trees.
Diversifying Query Auto-Completion
TLDR
A greedy query selection approach is developed that predicts query completions based on the current search popularity of candidate completions and on the aspects of previous queries in the same search session, which beats the baseline in terms of well-known metrics used in query auto-completion and diversification.
Improve Web Search Diversification with Intent Subtopic Mining
TLDR
This work aims to improve the performance of search results diversification by generating an intent subtopics list with fusion of multiple resources, and proposed an efficient Bayesian optimization approach to maximize resources selection performances and a new technique to cluster sub topics based on the top results snippet information and Jaccard Similarity coefficient.
DQR: a probabilistic approach to diversified query recommendation
TLDR
This work proposes the DQR framework, which mines a search log to achieve two goals: (1) It clusters search log queries to extract query concepts, based on which recommended queries are selected, and (2) It employs a probabilistic model and a greedy heuristic algorithm to achieve recommendation diversification.
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 36 REFERENCES
Query similarity by projecting the query-flow graph
TLDR
The information present in query logs is exploited in order to develop a measure of semantic similarity between queries that captures a notion of semantic Similarity between queries and it is useful for diversifying query recommendations.
A structured approach to query recommendation with social annotation data
TLDR
Empirical experimental results indicate that the structured approach to query recommendation with social annotation data can better satisfy users' interests and significantly enhance users' click behavior on recommendations.
Learning latent semantic relations from clickthrough data for query suggestion
TLDR
This paper develops a novel, effective and efficient two-level query suggestion model by mining clickthrough data, in the form of two bipartite graphs (user-query and query-URL bipartITE graphs) extracted from theclickthrough data.
Mining search engine query logs for query recommendation
TLDR
This paper presents a simple and intuitive method for mining search engine query logs to get fast query recommendations on a large scale industrial strength search engine, and combines this method with a traditional content based similarity method to compensate for the high sparsity of real query log data, and more specifically, the shortness of most query sessions.
Generating query substitutions
TLDR
A model for selecting between candidates is built, by using a number of features relating the query-candidate pair, and by fitting the model to human judgments of relevance of query suggestions, which improves the quality of the candidates generated.
Query Expansion by Mining User Logs
TLDR
This study proposes a new method for query expansion based on user interactions recorded in user logs that extracts correlations between query terms and document terms by analyzing user logs and can produce much better results than both the classical search method and the other query expansion methods.
Query suggestion using hitting time
TLDR
A novel query suggestion algorithm based on ranking queries with the hitting time on a large scale bipartite graph that can successfully boost long tail queries, accommodating personalized query suggestion, as well as finding related authors in research.
Query Recommendation Using Query Logs in Search Engines
TLDR
A method is proposed that, given a query submitted to a search engine, suggests a list of related queries that 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 query-flow graph: model and applications
TLDR
This paper introduces the query-flow graph, a graph representation of the interesting knowledge about latent querying behavior, and proposes a methodology that builds such a graph by mining time and textual information as well as aggregating queries from different users.
PQC: personalized query classification
TLDR
This paper proposes the Personalized Query Classification task and develops an algorithm based on user preference learning as a solution and proposes a collaborative ranking model to leverage similar users' information to tackle the sparseness problem in clickthrough logs.
...
1
2
3
4
...