1. A retrieval method carried out by a conversational recommender system, implemented using a programmable data processing apparatus, the method comprising the steps of: executing a retrieval session comprising at least one retrieval cycle to execute a selection mechanism to retrieve recommended cases from a database in response to a user query and presenting the recommended cases to the user, using the programmable data processing apparatus; receiving user feedback in the retrieval cycles, using the programmable data processing apparatus; and dynamically choosing a selection mechanism during each of the retrieval cycles, using the programmable data processing apparatus; said step of dynamically choosing being made in each cycle on the basis of the user feedback for the cycle, and the dynamic choice being made between: a refinement selection mechanism using case retrieval based on prioritizing cases which are similar to the query, and a re-focusing selection mechanism using case retrieval based on prioritizing cases which are similar to the query and are dissimilar from recommended cases which have not been preferred by the user in the user's feedback; wherein in a current cycle which is a second or subsequent cycle of the session, the system carries a preference case by recommending again a case which was recommended in a previous cycle and was preferred in said previous cycle, and the system chooses: the refinement selection mechanism if a different preference is indicated in the current cycle; the re-focusing selection mechanism for implementing diversity if the same preference is again indicated in the current cycle; wherein the choice is made on the basis of, where a preference case is carried from a previous cycle, whether the user again prefers the same case in the current cycle; wherein the system requests the user to indirectly indicate a preferred case, by critiquing in which a range of values is indicated for a search feature; wherein the system dynamically generates a fresh feedback structure for user feedback in each cycle of a session; wherein the system reviews cases that remain in the search space for a session and identifies groups of features, and value ranges, and automatically presents the user with an option to indicate suitability of these features and value ranges for a compound critique feedback; wherein the system dynamically selects said features and value ranges by characterizing recurring features and ranges as association rules A→B, in which from the presence of features, and value ranges A it infers the presence of other features and value ranges B; wherein the system generates a plurality of candidate compound critiques, grades them, and presents to the user only compound critiques having higher grades; and wherein the candidate compound critiques are graded according to a criterion of applicability to the user and a criterion of ability to reduce down the search space.