1. A method of information retrieval, comprising: determining, using a microprocessor, Q generative models (λ) in accordance with Probabilistic Latent Semantic Indexing (PLSI), said Q generative models being determined in an offline training; receiving a user query (q); choosing, using said microprocessor, N generative models out of the Q generative models, with N<Q; and determining, using said microprocessor, a content item (d) based on said query and a combination of the N generative models, wherein said step of choosing includes determining, 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, the quality estimation function depends on the query, such that the choosing of the N generative models depends on the query, wherein the step of choosing is performed at runtime when the query is received, and 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.
1. A method of information retrieval, comprising: determining, using a microprocessor, Q generative models (λ) in accordance with Probabilistic Latent Semantic Indexing (PLSI), said Q generative models being determined in an offline training; receiving a user query (q); choosing, using said microprocessor, N generative models out of the Q generative models, with N<Q; and determining, using said microprocessor, a content item (d) based on said query and a combination of the N generative models, wherein said step of choosing includes determining, 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, the quality estimation function depends on the query, such that the choosing of the N generative models depends on the query, wherein the step of choosing is performed at runtime when the query is received, and 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. 2. The method of claim 1 , wherein Q is larger than N by a factor between 2 to 6.