Efficient optimization of multiple recommendation quality factors according to individual user tendencies

Recommender systems are among the most visible applications of intelligent systems technology in practice and are used to help users find items of interest, for example on e-commerce sites, in a personalized way. While past research has focused mainly on accurately predicting the relevance of items that are unknown to the user, other quality criteria for recommendations have been investigated in recent years, including diversity, novelty, or serendipity. Considering these additional factors, however, often leads to the following two challenges. First, in many application domains, trade-offs like diversity vs. accuracy have to be balanced. Second, it is not always clear how much diversity or novelty is desirable in practice. In this work, we propose a novel parameterizable optimization scheme that re-ranks accuracy-optimized recommendation lists in order to cope with these challenges. Our method is both capable of considering multiple optimization goals at the same time and designed to consider individual user tendencies regarding the different quality factors, like diversity. In contrast to previous work, the method is not restricted to a specific underlying item ranking algorithm and its generic design allows the algorithm to be parameterized according to the requirements of the application domain. Experimental evaluations with different datasets show that balancing the quality factors with our method can be done with a marginal or no loss in ranking accuracy. Given that our method can be applied in various domains and within the narrow time constraints of online recommendation, our work opens new opportunities to design novel finer-grained personalization approaches in practical applications. (C) 2017 Elsevier Ltd. All rights reserved.