Intent-Aware Diversification using Item-Based SubProfiles

In many approaches to recommendation diversification, a recommender scores items for relevance and then re-ranks them to balance relevance with diversity. In intent-aware diversification, diversity is formulated in terms of coverage of aspects, where aspects are either explicit such as movie genres or implicit such as the latent factors found during matrix factorization. Typically, the same set of aspects is used across all users. In this paper, we propose a form of personalized intent-aware diversification, which we call SPAD (SubProfile-Aware Diversification). The aspects we use in SPAD are subprofiles of the user’s profile. They are not defined in terms of explicit or implicit features. We compare SPAD to other forms of intent-aware diversification. We present empirical results in support of SPAD.