Accounting for Taste: Using Profile Similarity to Improve Recommender Systems

摘要

Recommender systems have been developed to address the abundance of choice we face in taste domains (films, music, restaurants) when shopping or going out. However, consumers currently struggle to evaluate the appropriateness of recommendations offered. With collaborative filtering, recommendations are based on people's ratings of items. In this paper, we propose that the usefulness of recommender systems can be improved by including more information about recommenders. We conducted a laboratory online experiment with 100 participants simulating a movie recommender system to determine how familiarity of the recommender, profile similarity between decision-maker and recommender, and rating overlap with a particular recommender influence the choices of decision-makers in such a context. While familiarity in this experiment did not affect the participants' choices, profile similarity and rating overlap had a significant influence. These results help us understand the decision-making processes in an online context and form the basis for user-centered social recommender system design.