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Istituto di Scienza e Tecnologie dell'Informazione     
Bonchi F., Perego R., Silvestri F., Vahabi H., Venturini R. Recommendations for the long tail by Term-Query Graph. In: WWW'11 - 20th international conference companion on World Wide Web (Hyderabad, India, 28 March - 1 April 2011). Abstract, pp. 15 - 16. ACM, 2011.
 
 
Abstract
(English)
We define a new approach to the query recommendation problem. In particular, our main goal is to design a model enabling the generation of query suggestions also for rare and previously unseen queries. In other words we are targeting queries in the long tail. The model is based on a graph having two sets of nodes: Term nodes, and Query nodes. The graph induces a Markov chain on which a generic random walker starts from a subset of Term nodes, moves along Query nodes, and restarts (with a given probability) only from the same initial subset of Term nodes. Computing the stationary distribution of such a Markov chain is equivalent to extracting the so-called Center-piece Subgraph from the graph associated with the Markov chain itself. Given a query, we extract its terms and we set the restart subset to this term set. Therefore, we do not require a query to have been previously observed for the recommending model to be able to generate suggestions.
URL: http://dl.acm.org/citation.cfm?id=1963201&CFID=74367916&CFTOKEN=80133412
DOI: 10.1145/1963192.1963201
Subject Recommander system
H.3.3 Information Search and Retrieval


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