Optimal Recommendation in the Presense of Comparison Shopping
Recommender systems have been widely used by online stores to suggest items of interest to users. These systems often identify a subset of items from a much larger set that best matches the user's interest. A key concern with existing approaches is over specialization, which results in returning items that are too similar to each other. Unlike existing solutions that rely on diversity metrics to reduce similarity among recommended items, we propose to use choice probability to measure the overall quality of recommendation lists, which unifies the desire to achieve both relevancy and diversity in generating recommendations. We first define the recommendation problem in the discrete choice setting. We then develop an efficient greedy algorithm that solves the problem to optimum in O(KlogK) time, where K stands for the number of wanted items. Using data from a major travel website, we show that recommendation lists generated by our algorithm outperform existing strong baselines. Sensitivity analysis is also conducted to evaluate the robustness of our solution.