Abstract
The large amount of information available on the internet initiated various Recommender algorithms to act as an intermediate between number of choices and internet users. Collaborative filtering is one of the most traditional and intensively used recommendation approaches for many commercial services. Despite providing satisfying outcomes, it does have some issues that include source diversity, reliability, sparsity of data, scalability and cold start. Thus, there is a need for further improvement in the current generation of recommender system to achieve a more effective human decision support in a wide variety of applications and scenarios. Personalized Expert based collaborative filtering (PReC) approach is proposed to identify domain specific experts and the use of experts preference enhanced the performance of collaborative filtering recommender systems. A unified framework is proposed that integrates similar users rating data, experts rating and demographic data to reduce the number of pairwise computations from the search space to ensure scalability and enabled fine grained recommendations. The proposed method is evaluated using accuracy metrics MAE, RMSE on the data set collected from MovieLens datasets.
Highlights
With an overwhelming growth of information available over the internet in recent years, Recommender systems [1], [2] have proven to be a powerful tool whose aim is to guide users with personalized recommendations and alleviate the potential problem of information overload
We proposed personalized expert based collaborative filtering (PReC) to identify domain specific experts and use of demographic data with expert‟s preference in order to improve the performance of traditional collaborative filtering recommender systems
Expert user based Collaborative Filtering (EUCF) is proposed that integrates collaborative filtering features and clusters similar users and similar items thereby promoting experts based on the user profile and exploit their opinions addressing the reliability issue and enables fine grained recommendations
Summary
With an overwhelming growth of information available over the internet in recent years, Recommender systems [1], [2] have proven to be a powerful tool whose aim is to guide users with personalized recommendations and alleviate the potential problem of information overload. Despite the success of several Recommendation approaches such as Collaborative filtering [3], [4], Content based [5], [6] and Hybrid filtering [7], there have been several limitations increasing the need to provide effective and accurate recommendations. Collaborative filtering is one of the most traditional and intensively used recommendation approach for many commercial services like Movies recommendation, Music Recommendation, News Recommendation, Book Recommendation, etc., as it is content independent and easy to implement. In this approach, recommendations are generated based on user ratings and the similarity measures between users (User-based CF) and/or items (Item-based CF). There is a need for further improvement in the current generation of recommender system to achieve a more effective human decision support, in a wide variety of applications and scenarios
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