Academic Journal
Enhancing Context-Aware Recommendation Using Trust-Based Contextual Attentive Autoencoder
| Τίτλος: | Enhancing Context-Aware Recommendation Using Trust-Based Contextual Attentive Autoencoder |
|---|---|
| Συγγραφείς: | S. Abinaya, A. Sherly Alphonse, S. Abirami, M. K. Kavithadevi |
| Πηγή: | Neural Processing Letters. 55:6843-6864 |
| Στοιχεία εκδότη: | Springer Science and Business Media LLC, 2022. |
| Έτος έκδοσης: | 2022 |
| Θεματικοί όροι: | FOS: Computer and information sciences, Artificial intelligence, Computer Networks and Communications, Economics, Collaborative filtering, Trust-Aware Recommender Systems, 02 engineering and technology, Preference, Data science, Context-Aware Recommender Systems, Context (archaeology), Engineering, Artificial Intelligence, Machine learning, Microeconomics, 0202 electrical engineering, electronic engineering, information engineering, Information retrieval, User Modeling, Recommender system, Biology, Content-Centric Networking for Information Delivery, Personalization, Human–computer interaction, Content-Based Recommendation, Paleontology, Deep learning, Autoencoder, Computer science, World Wide Web, Aerospace engineering, Recommender System Technologies, Collaborative Filtering, Computer Science, Physical Sciences, Cold start (automotive), Graph Neural Network Models and Applications, Information Systems |
| Περιγραφή: | 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. |
| Τύπος εγγράφου: | Article Other literature type |
| Γλώσσα: | English |
| ISSN: | 1573-773X 1370-4621 |
| DOI: | 10.1007/s11063-023-11163-x |
| DOI: | 10.21203/rs.3.rs-1813771/v1 |
| DOI: | 10.60692/fyx8r-tmx05 |
| DOI: | 10.60692/ec5h1-nww57 |
| DOI: | 10.60692/gt42g-va457 |
| DOI: | 10.60692/wd4x6-j0x50 |
| Rights: | Springer Nature TDM CC BY |
| Αριθμός Καταχώρησης: | edsair.doi.dedup.....aa245c5205d0f1ec8f11e68bac765c0e |
| Βάση Δεδομένων: | OpenAIRE |
| ISSN: | 1573773X 13704621 |
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| DOI: | 10.1007/s11063-023-11163-x |