Abstract

In today’s market-driven world whenever the choices have to be made while buying products, we rely on recommendations from people either through word of mouth, recommendation letters, previews or reviews in the newspapers or feedback provided by other customers and surveys made on different products, etc. We live in an age of information technology with a surfeit of information to be made use of effectively. This has inevitably, led to an information overload problem which in turn has created a clear demand for automated methods which will help users locate and retrieve information with respect to their personal preferences in the best and optimal manner; resulting in the development of the Recommender System. Most of the recommender systems are model-based and use Pearson Correlation or Cosine Similarity to find the users who share the same preferences and interests. In this study, we propose two approaches which integrate the concept of multi criteria ratings into the recommender system. The results show that our approach is better than the single traditional rating system.

Keywords:

Collaborative filtering, multi criteria ratings, Pearson correlation coefficient, recommender system, spearman rank correlation coefficient, weighted correlation,

References

Competing interests

The authors have no competing interests.

Open Access Policy

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Copyright

The authors have no competing interests.