Concept-based interactive query expansion
Proceedings of the 14th ACM international conference on Information and knowledge management
Generating query substitutions
Proceedings of the 15th international conference on World Wide Web
Center-piece subgraphs: problem definition and fast solutions
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Heads and tails: studies of web search with common and rare queries
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Extracting semantic relations from query logs
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Design trade-offs for search engine caching
ACM Transactions on the Web (TWEB)
Query suggestion using hitting time
Proceedings of the 17th ACM conference on Information and knowledge management
The query-flow graph: model and applications
Proceedings of the 17th ACM conference on Information and knowledge management
Online expansion of rare queries for sponsored search
Proceedings of the 18th international conference on World wide web
Optimal rare query suggestion with implicit user feedback
Proceedings of the 19th international conference on World wide web
VSEncoding: efficient coding and fast decoding of integer lists via dynamic programming
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Learning similarity function for rare queries
Proceedings of the fourth ACM international conference on Web search and data mining
Improving recommendation for long-tail queries via templates
Proceedings of the 20th international conference on World wide web
Query reformulation mining: models, patterns, and applications
Information Retrieval
Synthesizing high utility suggestions for rare web search queries
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Proceedings of the sixth ACM international conference on Web search and data mining
Task-aware query recommendation
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Graph-of-word and TW-IDF: new approach to ad hoc IR
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Penguins in sweaters, or serendipitous entity search on user-generated content
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Orthogonal query recommendation
Proceedings of the 7th ACM conference on Recommender systems
| Hi-index | 0.00 |
We present a recommendation method based on the well-known concept of center-piece subgraph, that allows for the time/space efficient generation of suggestions also for rare, i.e., long-tail queries. Our method is scalable with respect to both the size of datasets from which the model is computed and the heavy workloads that current web search engines have to deal with. Basically, we relate terms contained into queries with highly correlated queries in a query-flow graph. This enables a novel recommendation generation method able to produce recommendations for approximately 99% of the workload of a real-world search engine. The method is based on a graph having term nodes, query nodes, and two kinds of connections: term-query and query-query. The first connects a term to the queries in which it is contained, the second connects two query nodes if the likelihood that a user submits the second query after having issued the first one is sufficiently high. On such large graph we need to compute the center-piece subgraph induced by terms contained into queries. In order to reduce the cost of the above computation, we introduce a novel and efficient method based on an inverted index representation of the model. We experiment our solution on two real-world query logs and we show that its effectiveness is comparable (and in some case better) than state-of-the-art methods for head-queries. More importantly, the quality of the recommendations generated remains very high also for long-tail queries, where other methods fail even to produce any suggestion. Finally, we extensively investigate scalability and efficiency issues and we show the viability of our method in real world search engines.