  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 recent ones distinguish the different importance of items for a
prediction. 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 for an item, resulting in sub-optimal recommendation. In this work,
we propose a general factor-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 factor-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
factor-level attention enhanced models consistently outperform their
counterparts, demonstrating the potential of differentiate user intents on the
factor-level for ICF recommendation models.
