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. © 2011 Authors.

Recommendations for the long tail by term-query graph / Bonchi, F.; Perego, R.; Silvestri, F.; Vahabi, H.; Venturini, R.. - (2011), pp. 15-16. (Intervento presentato al convegno 20th International Conference Companion on World Wide Web, WWW 2011 tenutosi a Hyderabad, ind) [10.1145/1963192.1963201].

Recommendations for the long tail by term-query graph

Bonchi F.;Silvestri F.;
2011

Abstract

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. © 2011 Authors.
2011
20th International Conference Companion on World Wide Web, WWW 2011
query recommender systems; web search effectiveness
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Recommendations for the long tail by term-query graph / Bonchi, F.; Perego, R.; Silvestri, F.; Vahabi, H.; Venturini, R.. - (2011), pp. 15-16. (Intervento presentato al convegno 20th International Conference Companion on World Wide Web, WWW 2011 tenutosi a Hyderabad, ind) [10.1145/1963192.1963201].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1571304
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