Recommender System Using Transformer Model: A Systematic Literature Review

Pohan, Husni Iskandar; Warnars, Harco Leslie Hendric Spits; Soewito, Benfano; Gaol, Ford Lumban

doi:10.1109/icisit54091.2022.9873070

Cited by 2 publications

(1 citation statement)

References 19 publications

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“…Transformers are a deep learning model or architecture consisting of encoders and decoders that utilize attention retention [25] to generate optimal weighting of data [26]. Unlike recurrent neural networks (RNNs), they do not perform this sequentially, allowing them to parallel process much more efficiently [26]. Large language models are built upon this architecture.…”

Section: Transformers and Large Language Modelsmentioning

confidence: 99%

Real-Time Ideation Analyzer and Information Recommender

Blazevic,

Sina,

Secco

et al. 2024

Electronics

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The benefits of ideation for both industry and academia alike have been outlined by countless studies, leading to research into various approaches attempting to add new ideation methods or examine how the quality of the ideas and solutions created can be measured. Although AI-based approaches are being researched, there is no attempt to provide the ideation participants with information that inspire new ideas and solutions in real time. Our proposal presents a novel and intuitive approach that supports users in real time by providing them with relevant information as they conduct ideation. By analyzing their ideas within the respective ideation sessions, our approach recommends items of interest with high contextual similarity to the proposed ideas, allowing users to skim through, for example, publications and inspire new ideas quickly. The recommendations also evolve in real time. As more ideas are written during the ideation session, the recommendations become more precise. This real-time approach is instantiated with various ideation methods as a proof of concept, and various models are evaluated and compared to identify the best model for working with ideas.

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Section: Transformers and Large Language Modelsmentioning

confidence: 99%