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Publication
Adaptive recommendation in online environments
| Name: | Description: | Size: | Format: | |
|---|---|---|---|---|
| 4.84 MB | Adobe PDF |
Advisor(s)
Abstract(s)
Recommender systems form a class of Artificial Intelligence systems that aim to recommend relevant items to the users. Due to their utility, it has gained attention in several applications domains and is high demanded for research. In order to obtain successful models in the recommendation problem in non-prohibitive computational time, different heuristics, architectures and information filtering techniques are studied with different datasets. More recently, machine learning, especially through the use of deep learning, has driven growth and expanded the sequential recommender systems development. This research focuses on models for managing sequential recommendation supported by session-based recommendation. This paper presents the characterization in the specific theme and the state-of-the-art towards study object of the thesis: the adaptive recommendation to mitigate the information overload in online environments.
Description
Keywords
Recommender systems Information filtering Sequential recommendation Session-based recommendation DNN recommendation