Author Description

Modeling consumer preference is an important research topic for the design of recommender systems. However, it is not a trivial task as consumer preferences are typically multi-faceted during their online purchase sessions, and they may have various types of intentions when interacting with recommendation agents, such as locating the most useful products or exploring novel content. Although the current generation of recommender systems has achieved great success in industrial applications, they primarily focus on only one dimension of consumer preference, such as identifying the most similar candidate products for the target consumer, while not addressing consumers' multi-faceted preferences of product variety, novelty, freshness, and so on. As a result, they would only reach suboptimal recommendation performance in business practice and might not fully meet consumers' satisfaction.In my dissertation, I will introduce and develop two research streams of novel recommender system methods, namely the unexpected recommender system and cross-domain recommender system, to model consumer desire of seeking novel content in recommendations. In particular, the former focuses on ”within-domain” consumer preference exploration, as it aims at providing novel and useful recommendations simultaneously from the same product domain to alleviate the consumer boredom problem. Meanwhile, the latter learns consumer preference information across multiple product domains and then produces better recommendations across these different domains to expand the consumers' horizons. Both methods would be effective and efficient in exploring consumer interests and improving consumer online experience, as I empirically demonstrate through an extensive set of simulation, offline, and online experiments on multiple industrial recommendation applications. Moreover, to further demonstrate the economic impact of my proposed approach and to provide managerial implications for practitioners, I also condu