Presentation från grundskolan, sameskolan och specialskolans kursplanekonfere...Skolverket
Den här presentationen används under kursplanekonferenserna som Skolverket anordnar under perioden 10 jan- 24 mars. Konferenserna behandlar reformerna inom grundskolan, sameskolan och specialskolan och riktar sig till de nyckelpersoner som blivit utvalda att ansvara för implementeringen på respektive skola.
Following the user’s interests in mobile context aware recommender systemsBouneffouf Djallel
The wide development of mobile applications provides a considerable amount of data of all types (images, texts, sounds, videos, etc.). In this sense, Mobile Context-aware Recommender Systems (MCRS) suggest the user suitable information depending on her/his situation and interests. Two key questions have to be considered 1) how to recommend the user information that follows his/her interests evolution? 2) how to model the user’s situation and its related interests? To the best of our knowledge, no existing work proposing a MCRS tries to answer both questions as we do. This paper describes an ongoing work on the implementation of a MCRS based on the hybrid-ε-greedy algorithm we propose, which combines the standard ε-greedy algorithm and both content-based filtering and case-based reasoning techniques.
Presentation från grundskolan, sameskolan och specialskolans kursplanekonfere...Skolverket
Den här presentationen används under kursplanekonferenserna som Skolverket anordnar under perioden 10 jan- 24 mars. Konferenserna behandlar reformerna inom grundskolan, sameskolan och specialskolan och riktar sig till de nyckelpersoner som blivit utvalda att ansvara för implementeringen på respektive skola.
Following the user’s interests in mobile context aware recommender systemsBouneffouf Djallel
The wide development of mobile applications provides a considerable amount of data of all types (images, texts, sounds, videos, etc.). In this sense, Mobile Context-aware Recommender Systems (MCRS) suggest the user suitable information depending on her/his situation and interests. Two key questions have to be considered 1) how to recommend the user information that follows his/her interests evolution? 2) how to model the user’s situation and its related interests? To the best of our knowledge, no existing work proposing a MCRS tries to answer both questions as we do. This paper describes an ongoing work on the implementation of a MCRS based on the hybrid-ε-greedy algorithm we propose, which combines the standard ε-greedy algorithm and both content-based filtering and case-based reasoning techniques.