In this work, we propose a novel attention-based item collaborative filtering model(AICF) which adopts three different attention mechanisms to estimate the weights of historical items that users have interacted with. Compared with the state-of-the-art recommendation models, the AICF model with ...
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 ...
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 ...
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 information, in this work, we use panoramic views to represent each 3D model. Attention mechanism is...
[13,16,18]. In traditional methods, they only use historical interactions for recommendation. For example, MF [8] is a standard collaborative filtering method which only uses the user-item rating matrix with\(L_2\)regularization. LibFM [11] only models the latent feature interactions of users...
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...
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...
The experimental results show that the TAK-DepNR realizes average precision of 89% and 88% compared to the other models like Immersive Graph Neural Network (IGNN), Attentional Factorization Machine with Review- Based User-Item Interaction (AFMRUI), Item Collaborative Filteri...
Attention-Based Graph Convolution Collaborative Filtering The development of big data has brought changes to society and brought us challenges. How to extract useful information from complex data has become the focus of research in recent years. Personalized recommendation as an effective solut... X ...
Therefore, using the traditional method to predict the unknown ratings ignores this difference in importance, resulting in a false assumption that all users have the same attention to different characteristics of the same item. In this paper, we propose a collaborative filtering system based on ...