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.
Highlights
The amount of data available on the internet is enormous and is constantly increasing
Recommender systems are a subclass of the information filtering system that seek to predict the 'rating' or 'preference' that a user would give to an item or social element they have not yet considered, using a model built from the characteristics of an item or the user's social environment (Ricci et al, 2011)
In contrast to single rating recommender system, the recommender system based on multi criteria ratings consists of n ratings and the rating function is of the form as proposed by (Adomavicius and Kwon, 2007): R : Users× Product→R0 × R1× R2 × × Rn
Summary
The amount of data available on the internet is enormous and is constantly increasing. The model based approach uses the past behavior of the user to calculate the recommendations. Collaborative filtering recommender systems can be classified based on the explicit and implicit ratings. In this study we propose an approach for incorporating ratings based on multi criteria in the recommender system.
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