Sebastian Feil
ABSTRACT
The cold start problem in recommender systems refers to the inability of making reliable recommendations if a critical mass of items has not yet been rated. To bypass this problem existing research focused on developing more reliable prediction models for situations in which only few items ratings exist. However, most of these approaches depend on adjusting the algorithm that determines a recommendation. We present a complimentary approach that does not require any adjustments to the recommendation algorithm. We draw on motivation theory and reward users for rating items. In particular, we instantiate different gamification patterns and examine their effect on the average user's number of provided report ratings. Our results confirm the positive effect of instantiating gamification patterns on the number of received report ratings.
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Digital Library
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- Lika, B., Kolomvatsos, K., Hadjiefthymiades, S. Facing the cold start problem in recommender, Expert Systems with Applications, 41 (2014), 2065-2073. Google ScholarDigital Library
- Morschheuser, B., Hamari, J., Koivisto, J. Gamification in crowdsourcing, Proc. HICSS (2016).Google Scholar
- Sahebi, S., Cohen, W.W. Community-based Recommendations: a Solution to the Cold Start Problem. Proc. RSWEB, ACM Press (2011).Google Scholar
- Vassileva, J. Motivating participation in social computing applications, User Modeling and User-Adapted Interaction, 22 (2012), 177-201. Google ScholarDigital Library
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