ABSTRACT In many application domains of recommender systems, content-based (CB) information are available for users, objects or both. CB information plays an important role in the process of recommendation, especially in cold-start scenarios, where the volume of feedback data is low. However, CB information may come from several, possibly external, sources varying in reliability, coverage or relevance to the recommending task. Therefore, each content source or attribute possess a different level of informativeness, which should be taken into consideration during the process of recommendation. In this paper, we propose a Content-Aligned Bayesian Personalized Ranking Matrix Factorization method (CABPR), extending Bayesian Personalized Ranking Matrix Factorization (BPR) by incorporating multiple sources of content information into the BPR’s optimization procedure. The working principle of CABPR is to calculate user-to-user and object-to-object similarity matrices based on the content information and penalize differences in latent factors of closely related users’ or objects’. CABPR further estimates relevance of similarity matrices as a part of the optimization procedure. CABPR method is a significant extension of a previously published BPR_MCA method, featuring additional variants of optimization criterion and improved optimization procedure. Four variants of CABPR were evaluated on two publicly available datasets: MovieLens 1M dataset, extended by data from IMDB, DBTropes and ZIP code statistics and LOD-RecSys dataset extended by the information available from DBPedia. Experiments shown that CABPR significantly improves over standard BPR as well as BPR_MCA method w.r.t. several cold-start scenarios.

中文翻译：

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基于内容对齐的贝叶斯个性化排名的混合推荐

摘要在推荐系统的许多应用领域中，基于内容（CB）的信息可用于用户、对象或两者。CB信息在推荐过程中扮演着重要的角色，尤其是在冷启动场景中，反馈数据量很少。但是，CB 信息可能来自多个可能来自外部的来源，这些来源在可靠性、覆盖范围或与推荐任务的相关性方面各不相同。因此，每个内容源或属性都具有不同级别的信息量，在推荐过程中应予以考虑。在本文中，我们提出了一种内容对齐的贝叶斯个性化排名矩阵分解方法（CABPR），通过将多个内容信息源合并到 BPR 的优化过程中，扩展了贝叶斯个性化排名矩阵分解 (BPR)。CABPR 的工作原理是根据内容信息计算用户到用户和对象到对象的相似度矩阵，并惩罚密切相关的用户或对象的潜在因素的差异。CABPR 进一步估计相似矩阵的相关性，作为优化过程的一部分。CABPR 方法是先前发布的 BPR_MCA 方法的重要扩展，具有优化标准的附加变体和改进的优化程序。CABPR 的四种变体在两个公开可用的数据集上进行了评估：MovieLens 1M 数据集，由来自 IMDB 的数据扩展，DBTropes 和邮政编码统计数据以及由 DBPedia 提供的信息扩展的 LOD-RecSys 数据集。实验表明，在几个冷启动场景中，CABPR 显着优于标准 BPR 以及 BPR_MCA 方法。