A hybrid content-based clustering architecture: minimising uncertainty in personalised multimedia content

Vassiliou, Charalampos; Stamoulis, Dimitris; Martakos, Drakoulis

doi:10.1504/ijista.2006.009911

Cited by 2 publications

(1 citation statement)

References 25 publications

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“…Various artificial intelligence algorithms (i.e., itemto-item, Bayesian networks, clustering, etc.) have been used for recommendations as well as for hybrid solutions, including Rule-Based and Horting filtering to increase the reduction of the uncertainty (Vassiliou et al, 2006). However, the added value of this research idea is that each individual user, expressing in fuzzy logic terms her/his perception of the weather condition, trains the recommender system in order to provide after some time a weather forecast that is more meaningful and actionable for her/him.…”

Section: "My Personal" Weather Forecastmentioning

confidence: 99%

“My personal forecast”: the digital transformation of the weather forecast communication using a fuzzy logic recommendation system

Stamoulis

Giannopoulos²

2022

Adv. Sci. Res.

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Abstract. Communicating the scientific data of the weather forecasts to the general public has always been a challenge. Using computer graphics' visual representations to convey the message to average people has certainly helped a lot to popularize the weather forecast consumption by the general public. However, these representations are not information rich since they are abstractions; moreover they are not very actionable on the receiver side to help one decide how s/he will “live” the forecast weather conditions and prepare appropriately. Therefore, there is a need to personalize the forecast based on past experience of the individuals and their personal needs. The forecast has to become more human- and needs-oriented and more focused to the particular requirements of each individual person. We, thus, propose a new co-creation process in which the audience is called to provide a daily feedback on how they lived the weather conditions personally on a daily basis, so that, “my personal forecast” can be produced making the forecast more actionable on the user side. Preliminary such attempts focused solely on the “feels like” temperature forecasts. To arrive at the “my personal forecast”, artificial intelligence based recommender systems need to be applied, using fuzzy logic as the appropriate method for the user to express the individually perceived weather conditions.

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Section: "My Personal" Weather Forecastmentioning

confidence: 99%