SoCRATe: A Framework for Compensating Users over Time with Limited Availability Recommendations

We present our preliminary ideas for developing SoCRATe, a framework and an online system dedicated to providing recommendations to users when items' availability is limited. SoCRATe is relevant to several real-world applications, among which movie and task recommendations. SoCRATe has several appealing features: it watches users as they consume recommendations and accounts for user feedback in refining recommendations in the next round, it implements loss compensation strategies to make up for sub-optimal recommendations, in terms of accuracy, when items have limited availability, and it decides when to re-generate recommendations on a need-based fashion. SoCRATe accommodates real users as well as simulated users to enable testing multiple recommendation choice models. To frame evaluation, SoCRATe introduces a new set of measures that capture recommendation accuracy over time as well as throughput and user satisfaction. All these features make SoCRATe unique and able to adapt recommendations to user preferences in a resource-limited setting.