Analyzing and Mitigating Repetitions in Trip Recommendation
It addresses repetition issues in trip recommendation, which is incremental as it builds on existing models to improve specific outcomes.
The paper tackles the problem of undesired repetitive outcomes in trip recommendation by introducing AR-Trip, a method that reduces repetitions through a cycle-aware predictor and perturbations, achieving enhanced precision on four public datasets.
Trip recommendation has emerged as a highly sought-after service over the past decade. Although current studies significantly understand human intention consistency, they struggle with undesired repetitive outcomes that need resolution. We make two pivotal discoveries using statistical analyses and experimental designs: (1) The occurrence of repetitions is intricately linked to the models and decoding strategies. (2) During training and decoding, adding perturbations to logits can reduce repetition. Motivated by these observations, we introduce AR-Trip (Anti Repetition for Trip Recommendation), which incorporates a cycle-aware predictor comprising three mechanisms to avoid duplicate Points-of-Interest (POIs) and demonstrates their effectiveness in alleviating repetition. Experiments on four public datasets illustrate that AR-Trip successfully mitigates repetition issues while enhancing precision.