Topic model-based recommender systems and their applications to cold-start problems
Topic model-based recommender systems and their applications to cold-start problems
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DOI：
10.1016/j.eswa.2022.117129
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发表时间：
2022-04
期刊：
影响因子：
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通讯作者：
Mimu Kawai;Hiroyuki Sato;Takayuki Shiohama
中科院分区：
文献类型：
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作者：
Mimu Kawai;Hiroyuki Sato;Takayuki Shiohama
推荐系统提供符合用户偏好的信息和项目。本研究提出混合推荐模型，使用基于内容的过滤和潜在的Dirichlet分配（LDA）为基础的模型。所提出的模型是LDA的扩展，其中单词对应于用户特征和项目特征，并被发现适合于处理冷启动问题，因为它通过其潜在维度为新用户和项目提供预测评级。这些模型具有同时分析项目主题、项目特征主题和用户特征主题的优点。使用MovieLens 1M数据集进行的实验表明，所提出的模型提供了与基线推荐模型相似的预测性能，并且在用户和项目主题的可解释性方面上级基线模型。
Recommender systems provide information and items that match a user’s preference. This study proposes hybrid recommender models that use content-based filtering and latent Dirichlet allocation (LDA)-based models. The proposed models are extensions of the LDA where the words correspond to user characteristics and item features and are found to be suitable for handling cold-start problems, as it provides predicted ratings for new users and items via its latent dimension. These models have the advantage of analyzing item topics, item feature topics, and user characteristic topics simultaneously. Experiments conducted with the MovieLens 1M dataset illustrate that the proposed models provide similar prediction performances as baseline recommender models and are superior to the baseline models regarding the interpretability of the user and item topics.