document: Recommendations for the long tail by term-query graph 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 .
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