10. A device comprising: a microprocessor adapted to calculate Q generative models (λ) in accordance with Probabilistic Latent Semantic Indexing (PLSI), said Q generative models being determined in an offline training; a storage adapted to store said Q generative models; an input interface adapted to receive a user query (q); and a selection unit adapted to select N generative models out of the Q generative models, with N<Q; wherein said microprocessor is further adapted to determine a content item (d) based on said query and a combination of the N generative models, wherein said selection unit is further adapted to determine, at runtime when a query has been received by the input interface, for each of the Q generative models, a value based on a quality estimation function, the N generative models are chosen depending on the value, the query comprises a plurality of N words w1, . . . , w−N, and the quality estimation function is given by ∑ i = 1 N ⁢ ⁢ p ⁡ ( w i | ( { w 1 , … ⁢ , w N } ⁢  ⁢ w i ) ) , w i representing the i-th word of the plurality of N words.
10. A device comprising: a microprocessor adapted to calculate Q generative models (λ) in accordance with Probabilistic Latent Semantic Indexing (PLSI), said Q generative models being determined in an offline training; a storage adapted to store said Q generative models; an input interface adapted to receive a user query (q); and a selection unit adapted to select N generative models out of the Q generative models, with N<Q; wherein said microprocessor is further adapted to determine a content item (d) based on said query and a combination of the N generative models, wherein said selection unit is further adapted to determine, at runtime when a query has been received by the input interface, for each of the Q generative models, a value based on a quality estimation function, the N generative models are chosen depending on the value, the query comprises a plurality of N words w1, . . . , w−N, and the quality estimation function is given by ∑ i = 1 N ⁢ ⁢ p ⁡ ( w i | ( { w 1 , … ⁢ , w N } ⁢  ⁢ w i ) ) , w i representing the i-th word of the plurality of N words. 11. The device of claim 10 , wherein said selection unit is further adapted to determine, at runtime when a query has been received by the input interface, for each of the Q generative models, a value based on a quality estimation function, wherein the N generative models are chosen depending on the value, wherein the quality estimation function depends on the query, wherein the query is split into a first part q1 and a second part q2 and the quality estimation function depends on a first probability p1 and a second probability p2, wherein 
 p 1 =p ( q 2 |q 1), and 
 p 2 =p ( q 1 |q 2).