Trust-Based Context-Aware Collaborative Filtering Using Denoising Autoencoder

Abinaya, S.; Devi, M. K. Kavitha

doi:10.1007/978-981-16-5640-8_4

Cited by 6 publications

(7 citation statements)

References 31 publications

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“…In TDAE [22] to enhance the recommendation accuracy by deploying deep neural networks, here denoising autoencoder-based rating prediction incorporates the trust data into the model. IS_AE [10] performs item splitting for gaining the knowledge of context into the autoencoder system with accurate recommendation but results in increased computation time.…”

Section: Related Workmentioning

confidence: 99%

“…Recommendation accuracy can be significantly increased by incorporating the trust values into the system [10]. Generally, the trust data that exist between users that is available explicitly in the dataset is sparse.…”

Section: Determining Implicit Trustmentioning

confidence: 99%

“…It takes the rating vector RUq ℝ n given by the user Uq on the entire item in the dataset together with the explicit trust vector ET  ℝ x and implicit trust vector IT  ℝ y as the three vectors of input to the model. The rating vector RUq is corrupted using the drop-out / mask-out method [10] to acquire the corrupted vector Uq R . The vector Uq R with ET and IT vectors is given into the TACDA model that produces the latent vector z q U h  ℝ l by the encoding function     .…”

Section: Trust-based Attentive Contextual Denoising Autoencodermentioning

confidence: 99%

“…Moreover, the associations among trusted values and the preference of the user are substantially nonlinear with the distinct distribution. To address this constraint and find more features from trust data, both the values of trust are fed into the TACDA models' all input and hidden layers, to enhance the top-n prediction accuracy [10] as shown in Eq (9).…”

Section: Trust-based Attentive Contextual Denoising Autoencodermentioning

confidence: 99%

“…However, the accuracy of recommendation provided by the autoencoder that incorporates context by attention framework can be enhanced by integrating the trust information [10] available in the social network that eliminates the sparsity and cold-start problem. Numerous investigations were recommended to add knowledge of trust into the model of recommendation [11,12], depending upon the fact that users' choices are habitually prompted primarily through their friends [13].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Enhancing Context-Aware Recommendation Using Trust-Based Contextual Attentive Autoencoder

Abinaya¹,

Alphonse²,

Abirami³

et al. 2023

Neural Process Lett

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Context-aware recommender systems (CARS) are intended primarily to consider the circumstances under which a user encounters an item to provide better-personalized recommendations. Users acquire point-of-interest, movies, products, and various online resources as suggestions. Classical collaborative filtering algorithms are shown to be satisfactory in a variety of recommendation activities processes, but cannot often capture complicated interactions between item and user, along with sparsity and cold start constraints. Hence it becomes a surge to apply a deep learning-based recommender model owing to its dynamic modeling potential and sustained success in other fields of application. In this work, a Trust-based Attentive Contextual Denoising Autoencoder(TACDA) for enhanced Top-N context-aware recommendation is proposed. Specifically, the TCADA model takes the sparse preference of the user that is integrated with trust data as input into the autoencoder to prevail over the cold start and sparsity obstacle and efficiently accumulates the context condition into the model via attention framework. Thereby, the attention technique is used to encode context features into a latent space of the user's trust data that is integrated with their preferences, which interconnects personalized context circumstances with active user's choice to deliver recommendations suited to that active user. Experiments conducted on Epinions and Caio dataset make obvious the efficiency of the TACDA model persistently outperforms the state-of-the-art methods.

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Section: Related Workmentioning

confidence: 99%

Section: Determining Implicit Trustmentioning

confidence: 99%

Section: Trust-based Attentive Contextual Denoising Autoencodermentioning

confidence: 99%

Section: Trust-based Attentive Contextual Denoising Autoencodermentioning

confidence: 99%