Assuming that both users and items are independent and identically distributed, most existing methods model user–item pairs, while ignoring the relationship between items, leading to limited performance. To solve this problem, we propose a novel neural network, CoNet, which can effectively model the co-occurrence pattern for Collaborative Filtering (CF). We argue that items always occur in pairs, i.e. an item co-occurrence pattern. For example, movies ”Harry Potter 1” and ”Harry Potter 2” are always viewed by users who like magic style films. To learn the latent features, CoNet is simultaneously modeled on user–item and item–item interactions. Compared with methods that train on a single user–item pair, CoNet can encode highly descriptive features from the co-occurrence pattern.

To achieve a better performance, we design an attention network to learn the weight of a user’s preference for different items and subsequently aggregate the weighted embeddings to obtain the co-occurrence representations. Finally, we conducted extensive experiments using several data sets, which show that the proposed method is superior to other baseline approaches. Source code of CoNet is available from https://github.com/XiuzeZhou/conet.

假设用户和物品都是独立同分布的，大多数现有方法都对用户-物品对进行建模，而忽略了物品之间的关系，导致性能有限。为了解决这个问题，我们提出了一种新颖的神经网络 CoNet，它可以有效地为协同过滤 (CF) 的共现模式建模。我们认为项目总是成对出现，即项目共现模式。例如，电影《哈利波特 1》和《哈利波特 2》总是被喜欢魔术风格电影的用户观看。为了学习潜在特征，CoNet 同时对用户-项目和项目-项目交互进行建模。与在单个用户-项目对上训练的方法相比，CoNet 可以从共现模式中编码高度描述性的特征。

为了获得更好的性能，我们设计了一个注意力网络来学习用户对不同项目的偏好权重，然后聚合加权嵌入以获得共现表示。最后，我们使用多个数据集进行了广泛的实验，这表明所提出的方法优于其他基线方法。CoNet 的源代码可从 https://github.com/XiuzeZhou/conet 获得。