  Recommending new items to existing users has remained a challenging problem
due to absence of user's past preferences for these items. The user
personalized non-collaborative methods based on item features can be used to
address this item cold-start problem. These methods rely on similarities
between the target item and user's previous preferred items. While computing
similarities based on item features, these methods overlook the interactions
among the features of the items and consider them independently. Modeling
interactions among features can be helpful as some features, when considered
together, provide a stronger signal on the relevance of an item when compared
to case where features are considered independently. To address this important
issue, in this work we introduce the Feature-based factorized Bilinear
Similarity Model (FBSM), which learns factorized bilinear similarity model for
TOP-n recommendation of new items, given the information about items preferred
by users in past as well as the features of these items. We carry out extensive
empirical evaluations on benchmark datasets, and we find that the proposed FBSM
approach improves upon traditional non-collaborative methods in terms of
recommendation performance. Moreover, the proposed approach also learns
insightful interactions among item features from data, which lead to deep
understanding on how these interactions contribute to personalized
recommendation.
