Enriching Public displays Ads Recommendations using an Individual

Martínez-Pabón, Francisco; Ospina-Quintero, Juan Camilo; Garzón-Marín, Víctor; Chantre-Astaiza, Ángela; Muñoz-Organero, Mario; Ramírez-González, Gustavo

doi:10.1145/2611009.2617196

Cited by 4 publications

(2 citation statements)

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“…The first interactions were useful to test the collaborative filtering and the aggregation techniques for the advertising domain. The results were published in [51,52]. At the end, we completed a dataset with 127 ads, 176 users, and 10.128 ratings for the tests.…”

Section: Dataset Considerationsmentioning

confidence: 99%

“…Some of them repeated the experiment because of their relationships with other members of the group. Regarding the group recommendations displayed on the Smart TV, we alternated two aggregation techniques between the groups to test the effect of each one; previous experiments showed a better performance for the chosen techniques [51]. We used the Least Misery aggregation technique for the odd groups (1, 3, 5, and 7); this technique makes a list of ratings with the minimum individual ratings, and the items are recommended based on the rating on that list.…”

Section: Online Testsmentioning

confidence: 99%

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Recommending Ads from Trustworthy Relationships in Pervasive Environments

Martínez-Pabón

Ospina-Quintero²,

Ramírez-González

et al. 2016

Mobile Information Systems

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The use of pervasive computing technologies for advertising purposes is an interesting emergent field for large, medium, and small companies. Although recommender systems have been a traditional solution to decrease users' cognitive effort to find good and personalized items, the classic collaborative filtering needs to include contextual information to be more effective. The inclusion of users' social context information in the recommendation algorithm, specifically trust in other users, may be a mechanism for obtaining ads' influence from other users in their closest social circle. However, there is no consensus about the variables to use during the trust inference process, and its integration into a classic collaborative filtering recommender system deserves a deeper research. On the other hand, the pervasive advertising domain demands a recommender system evaluation from a novelty/precision perspective. The improvement of the precision/novelty balance is not only a matter related to the recommendation algorithm itself but also a better recommendations' display strategy. In this paper, we propose a novel approach for a collaborative filtering recommender system based on trust, which was tested throughout a digital signage prototype using a multiscreen scheme for recommendations delivery to evaluate our proposal using a novelty/precision approach.

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Section: Dataset Considerationsmentioning

confidence: 99%

Section: Online Testsmentioning

confidence: 99%