  Item-based collaborative filtering (ICF) has been widely used in industrial
applications such as recommender system and online advertising. It models
users' preference on target items by the items they have interacted with.
Recent models use methods such as attention mechanism and deep neural network
to learn the user representation and scoring function more accurately. However,
despite their effectiveness, such models still overlook a problem that
performance of ICF methods heavily depends on the quality of item
representation especially the target item representation. In fact, due to the
long-tail distribution in the recommendation, most item embeddings can not
represent the semantics of items accurately and thus degrade the performance of
current ICF methods. In this paper, we propose an enhanced representation of
the target item which distills relevant information from the co-occurrence
items. We design sampling strategies to sample fix number of co-occurrence
items for the sake of noise reduction and computational cost. Considering the
different importance of sampled items to the target item, we apply attention
mechanism to selectively adopt the semantic information of the sampled items.
Our proposed Co-occurrence based Enhanced Representation model (CER) learns the
scoring function by a deep neural network with the attentive user
representation and fusion of raw representation and enhanced representation of
target item as input. With the enhanced representation, CER has stronger
representation power for the tail items compared to the state-of-the-art ICF
methods. Extensive experiments on two public benchmarks demonstrate the
effectiveness of CER.
