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2012
 Rodriguez-Carrion, Alicia;  Campo-Vázquez, Celeste;  García-Rubio, Carlos
Recommendations on the Move. In: Recommender Systems for the Social Web Book Chapter 
In: pp. 179-193, Springer International Publishing, Recommender Systems for the Social Web, 2012, ISBN: 978-3-642-25693-6.
Abstract | Links | BibTeX | Tags: current location, global position system, mobile phone, prediction algorithm, recommender system
@inbook{campo012,

title = {Recommendations on the Move. In: Recommender Systems for the Social Web},

author = {Alicia Rodriguez-Carrion and Celeste Campo-Vázquez and Carlos García-Rubio},

url = {https://doi.org/10.1007/978-3-642-25694-3},

doi = {https://doi.org/10.1007/978-3-642-25694-3_9},

isbn = {978-3-642-25693-6},

year  = {2012},

date = {2012-01-21},

urldate = {2012-01-21},

pages = {179-193},

publisher = {Springer International Publishing},

edition = {Recommender Systems for the Social Web},

abstract = {Recommender systems can take advantage of the user’s current location in order to improve the recommendations about places the user may be interested in. Taking a step further, these suggestions could be based not only on the user’s current location, but also on the places where the user is supposed to be in the near future, so the recommended locations would be on the path the user is going to follow. In order to do that we need some location prediction algorithms so that we can get those future locations. In this chapter we explain how to use the algorithms belonging to LZ family (LZ, LeZi Update and Active LeZi) as recommender engines, and we propose some ways of using these algorithms in places where the user has not been before or how to take advantage of the social knowledge about certain place so as to make these recommendations richer. Finally we show a prototype implementation of a recommender system for touristic places made up of these LZ predictors.},

keywords = {current location, global position system, mobile phone, prediction algorithm, recommender system},

pubstate = {published},

tppubtype = {inbook}

}

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Recommender systems can take advantage of the user’s current location in order to improve the recommendations about places the user may be interested in. Taking a step further, these suggestions could be based not only on the user’s current location, but also on the places where the user is supposed to be in the near future, so the recommended locations would be on the path the user is going to follow. In order to do that we need some location prediction algorithms so that we can get those future locations. In this chapter we explain how to use the algorithms belonging to LZ family (LZ, LeZi Update and Active LeZi) as recommender engines, and we propose some ways of using these algorithms in places where the user has not been before or how to take advantage of the social knowledge about certain place so as to make these recommendations richer. Finally we show a prototype implementation of a recommender system for touristic places made up of these LZ predictors.
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https://doi.org/10.1007/978-3-642-25694-3
doi:https://doi.org/10.1007/978-3-642-25694-3_9
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