Parameter settings.

Personalized recommendation plays an important role in many online service fields. In the field of tourism recommendation, tourist attractions contain rich context and content information. These implicit features include not only text, but also images and videos. In order to make better use of these features, researchers usually introduce richer feature information or more efficient feature representation methods, but the unrestricted introduction of a large amount of feature information will undoubtedly reduce the performance of the recommendation system. We propose a novel heterogeneous multimodal representation learning method for tourism recommendation. The proposed model is based on two-tower architecture, in which the item tower handles multimodal latent features: Bidirectional Long Short-Term Memory (Bi-LSTM) is used to extract the text features of items, and an External Attention Transformer (EANet) is used to extract image features of items, and connect these feature vectors with item IDs to enrich the feature representation of items. In order to increase the expressiveness of the model, we introduce a deep fully connected stack layer to fuse multimodal feature vectors and capture the hidden relationship between them. The model is tested on the three different datasets, our model is better than the baseline models in NDCG and precision.

个性化推荐在诸多在线服务领域中扮演着关键角色。在旅游推荐领域，旅游景点蕴含丰富的上下文与内容信息，此类隐式特征不仅包含文本模态，还涵盖图像与视频模态。为更好地利用这些特征，研究者通常会引入更丰富的特征信息或更高效的特征表示方法，但无节制地引入大量特征信息无疑会降低推荐系统的整体性能。为此，我们提出一种面向旅游推荐的新型异构多模态表示学习方法。所提模型基于双塔架构，其中物品塔负责处理多模态隐特征：采用双向长短期记忆网络（Bidirectional Long Short-Term Memory, Bi-LSTM）提取物品的文本特征，借助外部注意力Transformer（External Attention Transformer, EANet）提取物品的图像特征，并将此类特征向量与物品ID进行拼接，以丰富物品的特征表示。为提升模型的表达能力，我们引入深度全连接堆叠层以融合多模态特征向量，并捕捉其间的潜在关联。我们在三个不同的数据集上对该模型开展了测试，结果显示，相较于基线模型，本模型在归一化折损累积增益（Normalized Discounted Cumulative Gain, NDCG）与精确率（Precision）指标上均表现更优。