Collaborative Recommendations with Deep Feed-Forward Networks: An Approach to Service Personalization

Rizzo, Giovanni Luca Cascio; Marco, Marco De; Rosa, Pasquale De; Laura, Luigi

doi:10.1007/978-3-030-38724-2_5

Cited by 2 publications

(1 citation statement)

References 18 publications

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“…The first one is a new collaborative filtering framework based on a gauss core and an extension classification method (known as GCEDA) [26]. The second one is an advanced approach, which is based on Deep Feed-Forward Neural Networks (known as DFFN) [35]. This comparison experiment is carried out on ML-100k data set, and the results are shown in Table 5 and Figure 10.…”

Section: Compared With Other Methodsmentioning

confidence: 99%

Collaborative Filtering Recommendation Algorithm Based on TF-IDF and User Characteristics

Cai

Tang

et al. 2021

Applied Sciences

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The recommendation algorithm is a very important and challenging issue for a personal recommender system. The collaborative filtering recommendation algorithm is one of the most popular and effective recommendation algorithms. However, the traditional collaborative filtering recommendation algorithm does not fully consider the impact of popular items and user characteristics on the recommendation results. To solve these problems, an improved collaborative filtering algorithm is proposed, which is based on the Term Frequency-Inverse Document Frequency (TF-IDF) method and user characteristics. In the proposed algorithm, an improved TF-IDF method is used to calculate the user similarity on the basis of rating data first. Secondly, the multi-dimensional characteristics information of users is used to calculate the user similarity by a fuzzy membership method. Then, the above two user similarities are fused based on an adaptive weighted algorithm. Finally, some experiments are conducted on the movie public data set, and the experimental results show that the proposed method has better performance than that of the state of the art.

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Section: Compared With Other Methodsmentioning

confidence: 99%