Listwise autocomplete ranking with position debias

A method, comprising:

• receiving a user input indicative of a portion of a search query;
• receiving a set of candidate queries based on the portion of the search query;
• inputting, to a listwise click-through rate (CTR) prediction model that is trained using a shallow tower running parallel to one or more transformer layers to remove positional bias based on position information of respective candidate queries in the set of candidate queries, respective feature vectors corresponding to candidate queries of the set of candidate queries;
• generating, by the listwise CTR prediction model, respective click probability prediction values corresponding to the candidate queries based on joint comparisons between the respective feature vectors, wherein the shallow tower is excluded from the listwise CTR prediction model to generate the respective click probability prediction values;
• generating respective ranking scores for the candidate queries based on a product of the respective click probability prediction values and respective reward per search values associated with the candidate queries;
• and
• outputting at least one candidate query of the set of candidate queries based on an ordered set of candidate queries, wherein the ordered set of candidate queries is ordered based on the respective ranking scores and independent of the positional bias.