Implicit Feedback for Recommender Systems
Can implicit feedback substitute for explicit ratings in recommender systems? If so, we could avoid the difficulties associated with gathering explicit ratings from users. How, then, can we capture useful information unobtrusively, and how might we use that information to make recommendations ? In this paper we identify three types of implicit feedback and suggest two strategies for using implicit feedback to make recommendations. Introduction Recommender systems exploit ratings provided by an entire user population to reshape an information space for the benefit of one or more individuals (Oard, 1997b). In research systems, these ratings are often provided explicitly by each user using one or more ordinal or qualitative scales. The cognitive load effort to assign accurate ratings acts as disincentive, making it difficult to assemble large user populations and contributing to data sparsity within existing populations. Implicit feedback techniques seek to avoid this bottleneck by infe...