Abstract:

The modeling of user-item interaction patterns is an important task for personalized recommendation. Many recommendation systems are based on the assumption that there is a linear relationship between users and items， and ignore the complexity and non-linearity of interaction between real and historical items， as a result， these systems cannot capture the complex decision-making process of users. Therefore， a more expressive top-N recommendation system’s item similarity factor model solution was combined with the multi-layer perceptron approach， to effectively model the higher-order relationships between items and capture more complex user decisions. The combination effect was verified on the three datasets of MovieLens， Foursquare and ratings_Digital_Music； and compared with the benchmark methods such as MLP （Multi-Layer Perception）， Factored Item Similarity Model （FISM）， DeepICF （Deep Item-based Collaborative Filtering） and ItemKNN （Item-based K-Nearest Neighbors）， the results demonstrate that the proposed method has significant improvement in recommendation performance.

Key words: deep neural network, personalized recommendation, higher-order relationship, non-linearity, user decision making

摘要：

用户-物品交互模式建模是个性化推荐的一项重要任务，许多推荐系统都基于用户与商品之间存在线性关系的假设，忽略了现实物品与历史物品之间交互的复杂性和非线性，导致这些系统不足以捕捉到用户的复杂决策过程。为此，将一个更有表现力的Top-N推荐系统的物品相似性因子模型解决方法与多层感知机方法相结合，以有效地建模物品之间的高阶关系，捕获更复杂的用户决策。分别在三个数据集MovieLens、Foursquare和ratings_Digital_Music上验证了结合后的效果，并与基准方法MLP、分解物品相似度模型（FISM）、DeepICF和ItemKNN进行对比，结果表明，所提出的方法在推荐性能上有明显的提高。

关键词: 深度神经网络, 个性化推荐, 高阶关系, 非线性, 用户决策