A computer-implemented method for facilitating the generation of recommendations of audiovisual content for a first user, the method comprising: receiving data from a plurality of second users, the received data indicating audiovisual content of interest to the second users and timeslots associated with the audiovisual content, wherein the received data indicates a particular audiovisual content item and at least one timeslot during which the particular audiovisual content item is viewed by the second users, and wherein the at least one timeslot includes a first timeslot when the particular audiovisual content item is most often viewed by the second users, wherein the particular audiovisual content item is available for viewing in at least the first timeslot and a second timeslot; processing said received data to automatically identify a plurality of relationships between interests of one or more of said second users in respective items of audiovisual content in said audiovisual content represented by said received data and generate a recommendation engine, wherein the processing comprises training a neural network (25; 50) to identify the plurality of relationships, and the recommendation engine is the trained neural network, and wherein said neural network comprises a plurality of input nodes each input node representing a respective audiovisual content item, at least one hidden layer node and a plurality of output nodes each output node representing a respective audiovisual content item, the recommendation engine being adapted to take as input indications of audiovisual content of interest to said first user such that audiovisual content items of interest to said first user are provided to at least a part of the input nodes of the neural network and to generate recommendations of further audiovisual content of interest to the first user based upon a subset of said plurality of relationships, and assigning, by the recommendation engine, a respective priority to each generated recommendation based upon said timeslots, wherein the recommendations generated by the recommendation engine include the particular audiovisual content item, and wherein assigning a respective priority comprises assigning a first priority to the particular audiovisual content item for the first timeslot and assigning a second priority to the particular audiovisual content item for the second timeslot, wherein the first priority is higher than the second priority; and wherein the method further comprises transmitting only part of said trained neural network to said first user, and wherein said part comprises only those input nodes corresponding to the audiovisual content items of interest to said first user, only those hidden layer nodes which can be activated by said input nodes of said part and only those output nodes which can be activated by said hidden layer nodes of said part. receiving data from a plurality of second users, the received data indicating audiovisual content of interest to the second users and timeslots associated with the audiovisual content, wherein the received data indicates a particular audiovisual content item and at least one timeslot during which the particular audiovisual content item is viewed by the second users, and wherein the at least one timeslot includes a first timeslot when the particular audiovisual content item is most often viewed by the second users, wherein the particular audiovisual content item is available for viewing in at least the first timeslot and a second timeslot; processing said received data to automatically identify a plurality of relationships between interests of one or more of said second users in respective items of audiovisual content in said audiovisual content represented by said received data and generate a recommendation engine, wherein the processing comprises training a neural network (25; 50) to identify the plurality of relationships, and the recommendation engine is the trained neural network, and wherein said neural network comprises a plurality of input nodes each input node representing a respective audiovisual content item, at least one hidden layer node and a plurality of output nodes each output node representing a respective audiovisual content item, the recommendation engine being adapted to take as input indications of audiovisual content of interest to said first user such that audiovisual content items of interest to said first user are provided to at least a part of the input nodes of the neural network and to generate recommendations of further audiovisual content of interest to the first user based upon a subset of said plurality of relationships, and assigning, by the recommendation engine, a respective priority to each generated recommendation based upon said timeslots, wherein the recommendations generated by the recommendation engine include the particular audiovisual content item, and wherein assigning a respective priority comprises assigning a first priority to the particular audiovisual content item for the first timeslot and assigning a second priority to the particular audiovisual content item for the second timeslot, wherein the first priority is higher than the second priority; and wherein the method further comprises transmitting only part of said trained neural network to said first user, and wherein said part comprises only those input nodes corresponding to the audiovisual content items of interest to said first user, only those hidden layer nodes which can be activated by said input nodes of said part and only those output nodes which can be activated by said hidden layer nodes of said part.