Collaborative filteringAttention modelDeep learningNeighborhood-based collaborative filtering is a method of high significance among recommender systems, with advantages of simplicity and justifiability. However, recently it is receiving less popularity due to its low prediction accuracy in contrast with model...
There are mainly two categories of traditional rec- ommendation algorithms: Content-Based (CB) and Collaborative Filtering (CF). CF methods make recommendations mainly accord- ing to the historical feedback information. They usually perform better when there is sufficient feedback information but ...
Lv Y, Zheng Y, Wei F, Wang C, Wang C (2020) AICF: Attention-based item collaborative filtering. Adv Eng Inform 44:101090 Article Google Scholar Pang G, Wang X, Hao F, Xie J, Wang X, Lin Y, Qin X (2019) ACNN-FM: A novel recommendation with attention-based convolutional neural...
Most existing collaborative filtering-based recommender systems rely solely on available user–item interactions for user and item representation learning... B Xiao,D Chen - Electronics (2079-9292) 被引量: 0发表: 2024年 Meta-Path-Based Deep Representation Learning for Personalized Point of Interest...
传统Item-Based协同过滤推荐算法改进04-1138.补充:基于项目的协同过滤推荐算法(Item-Based Collaborative Filtering Recommendation Algorithms)04-1139.Top-N推荐算法 Top-N recommendation Algorithms04-11 收起 写在前面 本文是一篇于2023年3月21日发表在2023 International Conference on Big Data, Environmental ...
The effectiveness of each module of the model architecture proposed in this paper is illustrated by the Table 4, which demonstrates that eliminating any module from the Attention-LSTM will result in an increase in the error values of each item. Table 4 The comparison of different ablation ...
Abstract In this paper, we propose an attention-based bipartite graph 3D model retrieval algorithm, where many-to-many matching method, the weighted bipartite graph matching, is employed for comparison between two 3D models. Considering the panoramic views can donate the spatial and structural informa...
The user-based collaborative filtering (UCF) model has been widely used in industry for recommender systems. UCF predicts a user's interest in an item based on rating information from similar user profiles. A neural network UCF model can learn effectively the high-order relations between users ...
The algorithm uses the sliding query method to obtain the user’s attention to the latent features of the item and solves the interaction problem among different dimensions of the user, and the latent features of the item. Keywords: artificial intelligence; recommender system; collaborative filtering...
2.1. Collaborative Filtering Based Methods Personalized POI recommendation has been extensively studied in the related research field. CF is one of the widely used technique [28]. some previous POI recommendation methods are user-based CF or item-based CF, which take advantage of check-ins of sim...