Minimal Interaction Content Discovery in Recommender Systems

摘要

Many prior works in recommender systems focus on improving the accuracy of item rating predictions. In comparison, the areas of recommendation interfaces and user-recommender interaction remain underex-plored. In this work, we look into the interaction of users with the recommendation list, aiming to devise a method that simplifies content discovery and minimizes the cost of reaching an item of interest. We quantify this cost by the number of user interactions (clicks and scrolls) with the recommendation list. To this end, we propose generalized linear search (GLS), an adaptive combination of the established linear and generalized search (GS) approaches. GLS leverages the advantages of these two approaches, and we prove formally that it performs at least as well as GS. We also conduct a thorough experimental evaluation of GLS and compare it to several baselines and heuristic approaches in both an offline and live evaluation. The results of the evaluation show that GLS consistently outperforms the baseline approaches and is also preferred by users. In summary, GLS offers an efficient and easy-to-use means for content discovery in recommender systems.