A Collaborative Filtering Algorithm with Intragroup Divergence for POI Group Recommendation

Liu, Yanheng; Yin, Minghao; Zhou, Xu

doi:10.3390/app11125416

Cited by 11 publications

(4 citation statements)

References 24 publications

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“…(1) GR algorithm According to the fusion timing in the process of GR, most existing GR algorithms can be divided into model fusion and recommendation fusion [16]. The specific flow and differences between the two are presented in Figure 1.…”

Section: Relevant Theoretical Supportmentioning

confidence: 99%

The Application of Graph Neural Network Based on Multi-Head Attention Optimization in Information Recommendation Systems

2023

Preprint

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This study proposes a group information recommendation (GIR) model to accurately and comprehensively explore group preferences and enhances complex groups' recommendation effect. Firstly, the group recommendation (GR) algorithm, Multi-Head Attention (MHA) mechanism, and Graph Neural Network (GNN) are summarized. Secondly, the GIR model is constructed using the MHA and GNN models. On the one hand, the model considers the importance of differences in group objects' preferences in different item recommendations. On the other hand, the impact of the interaction between objects on group preference modeling is considered. The constructed GIR model includes an embedding layer, a propagation layer, a fusion layer, and a prediction layer. Finally, experiments are conducted to verify the effectiveness of the constructed model. The verification results reveal that: (1) compared with other recommendation models, the Multi-Head Attention-Graph Neural Network (MHA-GNN) model is improved by 1.05% in the Mean Reciprocal Rank (MRR) index. (2) The recommendation precision and MRR value of the MHA-GNN model are higher than those of other fused models. (3) Compared with other recommendation models, the MHA-GNN model has the highest recommendation precision and better recommendation effect. The above conclusions show that the introduced GNN technology can effectively improve the performance of the GR model. At the same time, the established GIR system has a more accurate recommendation effect, which can realize the efficient mining of group preferences. This study aims to achieve the efficient recommendation of information and save information retrieval time.

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Section: Relevant Theoretical Supportmentioning

confidence: 99%

The Application of Graph Neural Network Based on Multi-Head Attention Optimization in Information Recommendation Systems

2023

Preprint

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“…For the problem of user interest shift, more attention is often given to the point-of-interests ("POIs") with the latest ratings of users. Liu, Yin and Zhou [5] proposed a POI group recommendation method based on collaborative filtering and intra-group divergence, and constructed a user preference vector according to the user's preference for time and category.…”

Section: Related Workmentioning

confidence: 99%

An Improved Collaborative Filtering Recommendation Algorithm Based on Clustering Optimization and Interest Migration

Ren,

Liu

2023

Frontiers in Artificial Intelligence and Applications

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The collaborative filtering technique is widely used in recommendation systems across a variety of fields. However, it faces problems such as data sparsity and user interest drift, making it difficult to generate high-quality recommendations. In this paper, we use a Fuzzy C-Means clustering algorithm that combines item attributes to cluster items, add a time weight factor to the similarity calculation based on the Ebbinghaus forgetting function, and then find the nearest neighbor set by applying an improved similarity formula to generate recommendation results. The empirical results show that: (1) the Fuzzy C-Means clustering algorithm used in this paper that combines item attributes can effectively improve the recommendation results, and is able to find a more accurate nearest neighbor set compared to traditional methods; (2) The improved time weight function based on the Ebbinghaus forgetting function can adjust the weights of items at different times, which can effectively improve the problem of user interest drift.

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“…In this paper, the number of times users listen to songs is converted into the interval value of [0,5] as the user's score of songs, and a user song table is constructed. As shown in Table 2, the corresponding relationship between users and songs is obtained [6][7].…”

Section: Construction Of User-song Preference Matrixmentioning

confidence: 99%

Design of Music Heritage Database System Based on Collaborative Filtering Algorithm

Tian,

Liu

2023

Frontiers in Artificial Intelligence and Applications

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In order to understand the design of music heritage database system, a research on the design of music heritage database system based on collaborative filtering algorithm is put forward. In this paper, firstly, collaborative filtering recommendation algorithm is studied, and users are recommended through two models that realize the algorithm, namely user-based collaborative filtering and item-based collaborative filtering. Finally, the recommendation effects of the two models are compared. Secondly, based on collaborative filtering, the content-based music recommendation algorithm is integrated, and a music recommendation system is designed. When the traditional collaborative filtering algorithm is used to recommend to users, some popular songs will affect the similarity calculation between users and between songs, resulting in poor recommendation effect. In this paper, the influence of popular songs on similarity calculation is reduced by punishing popular songs, and the accuracy of recommendation is improved by improving similarity calculation. Finally, the experimental results on the crawled Netease cloud music data show that the accuracy and recall of the hybrid recommendation algorithm are better than those based on collaborative filtering and content.

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A Collaborative Filtering Algorithm with Intragroup Divergence for POI Group Recommendation

Cited by 11 publications

References 24 publications

The Application of Graph Neural Network Based on Multi-Head Attention Optimization in Information Recommendation Systems

The Application of Graph Neural Network Based on Multi-Head Attention Optimization in Information Recommendation Systems

An Improved Collaborative Filtering Recommendation Algorithm Based on Clustering Optimization and Interest Migration

Design of Music Heritage Database System Based on Collaborative Filtering Algorithm

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