Composite recommendatin and personazation in a shopping environment
Keywords
Loading...
Authors
Issue Date
2017-03-29
Language
en
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
A mobile and web application are implemented to test composite recommendation
in a shopping environment. Routes along shops in the center of
Nijmegen are recommended and a comparison is made with recommendations
by a content-based recommender system. The composite recommender
system makes diverse sets of shops in a route, whereas the content-based
system recommends the top-n shops of a user. This research is thus a start
in finding what type of recommendations is preferred and whether the diversity
of the composite routes is important in a shopping environment. Because
we have no prior information about our participants, we encounter
the cold start problem. The appliance of persona-ization as a method to address
this problem is tested. Persona-ization is a method in search problems
to search on behalf of others. In this study, we apply it to the recommendation
problem where the first recommendation to each user is recommended
on behalf of a self chosen persona that fits the user best.
A list of shops in the center of Nijmegen is constructed with each shop
having one or more tags. These tags give information about the type of
shop and are used to compute ratings of users for each shop. The same tags
are used to describe the different personas. Preferred tags have a positive
weight and tags that a persona dislikes have a negative weight. A user
starts with the same weights as the self chosen persona.The user’s feedback
on what shops in the route are preferred is used to update the weights to
improve future recommendations.
An experiment is set up that consists of two parts. In total, 39 participants
completed the first part where the applicance of persona-ization was
tested. Of those 39 participants, 27 also completed the second part where
the performance of the composite recommender system is compared to the
performance of the content-based recommender system. Results show that
persona-ization significantly improves the recommendations and thus it is
concluded that it addresses the cold start problem effectively. The contentbased
recommender system outperforms the composite recommender system;
it is not only preferred significantly more often, the average percentage
of shops that is indicated as personalised is significantly higher for the
content-based recommended routes.
Description
Citation
Supervisor
Faculty
Faculteit der Sociale Wetenschappen