Document - Recommendations for the long tail by Term-Query Graph

2011

Contribution to conference Open Access

Recommendations for the long tail by Term-Query Graph

Bonchi F., Perego R., Silvestri F., Vahabi H., Venturini R.

Recommander system

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.

Source: 20th international conference companion on World Wide Web, WWW'11, pp. 15–16, Hyderabad, India, 28 March - 1 April 2011

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@inproceedings{oai:it.cnr:prodotti:206796,
	title = {Recommendations for the long tail by Term-Query Graph},
	author = {Bonchi F. and Perego R. and Silvestri F. and Vahabi H. and Venturini R.},
	doi = {10.1145/1963192.1963201},
	booktitle = {20th international conference companion on World Wide Web, WWW'11, pp. 15–16, Hyderabad, India, 28 March - 1 April 2011},
	year = {2011}
}