  Item-based collaborative filtering (ICF) enjoys the advantages of high
recommendation accuracy and ease in online penalization and thus is favored by
the industrial recommender systems. ICF recommends items to a target user based
on their similarities to the previously interacted items of the user. Great
progresses have been achieved for ICF in recent years by applying advanced
machine learning techniques (e.g., deep neural networks) to learn the item
similarity from data. The early methods simply treat all the historical items
equally and recently proposed methods attempt to distinguish the different
importance of historical items when recommending a target item. Despite the
progress, we argue that those ICF models neglect the diverse intents of users
on adopting items (e.g., watching a movie because of the director, leading
actors, or the visual effects). As a result, they fail to estimate the item
similarity on a finer-grained level to predict the user's preference to an
item, resulting in sub-optimal recommendation. In this work, we propose a
general feature-level attention method for ICF models. The key of our method is
to distinguish the importance of different factors when computing the item
similarity for a prediction. To demonstrate the effectiveness of our method, we
design a light attention neural network to integrate both item-level and
feature-level attention for neural ICF models. It is model-agnostic and
easy-to-implement. We apply it to two baseline ICF models and evaluate its
effectiveness on six public datasets. Extensive experiments show the
feature-level attention enhanced models consistently outperform their
counterparts, demonstrating the potential of differentiating user intents on
the feature-level for ICF recommendation models.
