Enhancing accuracy of hybrid recommender systems through adapting the domain trends

Aksel F., Birtürk A.

Workshop on the Practical Use of Recommender Systems, Algorithms and Technologies 2010, PRSAT 2010 - Held at the 4th International ACM Conference on Recommender Systems, RecSys 2010, Barcelona, İspanya, 30 Eylül 2010, cilt.676, ss.11-18, (Tam Metin Bildiri) identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası: 676
  • Basıldığı Şehir: Barcelona
  • Basıldığı Ülke: İspanya
  • Sayfa Sayıları: ss.11-18
  • Anahtar Kelimeler: Adaptive recommender systems, Decision tree induction, Hybrid recommender framework, Hybrid recommender systems, Switching hybridization
  • TED Üniversitesi Adresli: Hayır

Özet

Hybrid recommender systems combine several algorithms based on their hybridization strategy. Prediction algorithm selection strategy directly influence the accuracy of the hybrid recommenders. Recent research has mostly focused on static hybridization schemes which are designed as fixed combinations of prediction algorithms and do not change at run-time. However, people's tastes and desires are temporary and gradually evolve. Moreover, each domain has unique characteristics, trends and unique user interests. In this paper, we propose an adaptive method for hybrid recommender systems, in which the combination of algorithms are learned and dynamically updated from the results of previous predictions. We describe our hybrid recommender system, called AdaRec, that uses domain attributes to understand the domain drifts and trends, and user feedback in order to change it's prediction strategy at run-time, and adapt the combination of content-based and collaborative algorithms to have better results. Experiment results with datasets show that our system outperforms naive hybrid recommender.