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Item-based collaborative filtering (ICF) has been widely used in industrialapplications such as recommender system and online advertising. It modelsusers' preference on target items by the items they have interacted with.Recent models use methods such as attention mechanism and deep neural networkto learn the user representation and scoring function more accurately. However,despite their effectiveness, such models still overlook a problem thatperformance of ICF methods heavily depends on the quality of itemrepresentation especially the target item representation. In fact, due to thelong-tail distribution in the recommendation, most item embeddings can notrepresent the semantics of items accurately and thus degrade the performance ofcurrent ICF methods. In this paper, we propose an enhanced representation ofthe target item which distills relevant information from the co-occurrenceitems. We design sampling strategies to sample fix number of co-occurrenceitems for the sake of noise reduction and computational cost. Considering thedifferent importance of sampled items to the target item, we apply attentionmechanism to selectively adopt the semantic information of the sampled items.Our proposed Co-occurrence based Enhanced Representation model (CER) learns thescoring function by a deep neural network with the attentive userrepresentation and fusion of raw representation and enhanced representation oftarget item as input. With the enhanced representation, CER has strongerrepresentation power for the tail items compared to the state-of-the-art ICFmethods. Extensive experiments on two public benchmarks demonstrate theeffectiveness of CER.

Represent Items by Items: An Enhanced Representation of the Target Item for Recommendation