The cold-start problem is a long-standing challenge in recommender systems. As a promising solution, content-based generative models usually project a cold-start item's content onto a warm-start item embedding to capture collaborative signals from item content so that collaborative filtering can be applied. However, since the training of the cold-start recommendation models is conducted on warm datasets, the existent methods face the issue that the collaborative embeddings of items will be blurred, which significantly degenerates the performance of cold-start item recommendation. To address this issue, we propose a novel model called Contrastive Collaborative Filtering for Cold-start item Recommendation (CCFCRec), which capitalizes on the co-occurrence collaborative signals in warm training data to alleviate the issue of blurry collaborative embeddings for cold-start item recommendation. In particular, we devise a contrastive collaborative filtering (CF) framework, consisting of a content CF module and a co-occurrence CF module to generate the content-based collaborative embedding and the co-occurrence collaborative embedding for a training item, respectively. During the joint training of the two CF modules, we apply a contrastive learning between the two collaborative embeddings, by which the knowledge about the co-occurrence signals can be indirectly transferred to the content CF module, so that the blurry collaborative embeddings can be rectified implicitly by the memorized co-occurrence collaborative signals during the applying phase. Together with the sound theoretical analysis, the extensive experiments conducted on real datasets demonstrate the superiority of the proposed model. The codes and datasets are available on https://github.com/zzhin/CCFCRec.

翻译：冷启动问题是推荐系统中一个长期存在的挑战。作为一种有前景的解决方案，基于内容的生成模型通常将冷启动物品的内容映射到热启动物品的嵌入向量上，以从物品内容中捕获协同信号，从而应用协同过滤。然而，由于冷启动推荐模型的训练是在热启动数据集上进行的，现有方法面临物品协同嵌入模糊的问题，这显著降低了冷启动物品推荐的性能。为解决这一问题，我们提出了一种名为“面向冷启动物品推荐的对比协同过滤”（CCFCRec）的新模型，该模型利用热启动训练数据中的共现协同信号，以缓解冷启动物品推荐中协同嵌入模糊的问题。具体而言，我们设计了一个对比协同过滤（CF）框架，该框架包含一个内容CF模块和一个共现CF模块，分别用于生成训练物品的基于内容的协同嵌入和共现协同嵌入。在两个CF模块的联合训练过程中，我们对这两个协同嵌入应用对比学习，从而将共现信号的知识间接迁移至内容CF模块，使得在应用阶段，模糊的协同嵌入能够通过记忆的共现协同信号得到隐式修正。结合严谨的理论分析，在真实数据集上进行的广泛实验证明了所提模型的优越性。代码与数据集可在 https://github.com/zzhin/CCFCRec 获取。