Composite recommendatin and personazation in a shopping environment

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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.
Faculteit der Sociale Wetenschappen