Time Series Based Optimized Recommendation of Promotional Rewards

We propose a recommendation system, based on a modified language model in the class of Mixture Transition Distribution (MTD) models, to recommend optimized and near-optimized time series of promotional rewards. The historical data of our application domain, loyalty program, are low count, non-Gaussian, sparse and bursty time series. These are significantly more challenging to model than Gaussian time series. Our contribution is the first time series-based recommendation system based on a language model and based on MTD models. This proposed recommendation system is essentially a hierarchical model for the reward-response time series pair. As timeliness of promotional rewards is important, we estimate probable churn off time by a simple technique using a related time series of visits of the loyalty program customers to the business location. We also propose a novel human in the loop technique to fabricate a future response time series using the estimated churn time. Using Viterbi algorithm, an optimized future reward series is estimated for recommendation, given the future response time series as input. During the evaluation process, the real response series was used as input instead. We also propose a constraint-based modification of Viterbi algorithm called Hopefulness procedure (HP). HP was proposed as a constraint based optimization method for achieving trade-offs between user retention and the likelihood of the future reward series. We also proposed three new evaluation metrics. To assess the degree of user retention that can likely be achieved using the future reward sequence, we used MAE (generally used for sequence matching) and recall, their two proposed modifications, and a proposed metric goodrec. We used three different types of correlated user engagement time series data recorded for an year for a set of users. The evaluation showed that the lower bound of the expected performance of HP algorithm is better than the Viterbi algorithm in generating a future reward series for user retention.