Blurring of the Boundary Between Interactive Search and Recommendation

Abstract

Search and recommendation engines are increasingly more intelligent. They have become more personalized and social as well as more interactive. No longer just offering ten blue links, search engines have increasingly been integrated with task and item recommenders directly, for example, to offer news, movie, music, and dining suggestions. Vice versa, recommendation systems have increasingly became more search-like by offering capabilities that enable users to tune and direct recommendation results instantly. As the two technologies evolve toward each other, there is increasingly a blurring of the boundary between these two approaches to interactive information seeking. On the search side, this is driven by the merging of question answering capabilities with search, led by systems like Google Now and Apple Siri that move search toward intelligent personal assistants. On the recommendation side, there has been a merging of techniques from not just keyword search but also faceted search, along with user-based and item-based collaborative filtering techniques and other more proactive recommenders. This blurring has resulted in both critical re-thinking about not just how to architect the systems by merging and sharing backend components common to both types of systems, but also how to structure the user interactions and experiences.