The Impact of Profile Coherence on Recommendation Performance for Shared Accounts on Smart TVs

摘要

Most recommendation algorithms assume that an account represents a single user, and capture a user's interest by what he/she has preferred. However, in some applications, e.g., video recommendation on smart TVs, an account is often shared by multiple users who tend to have disparate interests. It poses great challenges for delivering personalized recommendations. In this paper, we propose the concept of profile coherence to measure the coherence of an account's interests, which is computed as the average similarity between items in the account profile in our implementation. Furthermore, we evaluate the impact of profile coherence on the quality of recommendation lists for coherent and incoherent accounts generated by different variants of item-based collaborative filtering. Experiments conducted on a large-scale watch log on smart TVs conform that the profile coherence indeed impact the quality of recommendation lists in various aspects - accuracy, diversity and popularity.