Affiliations: Department of Computer Science, Atma Ram Sanatan Dharam College, University of Delhi, Delhi, India
Correspondence:
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Corresponding author. Parul Jain, Department of Computer Science, Atma Ram Sanatan Dharam College, University of Delhi, Delhi – 110021, India. E-mail: paruljainpj@rediffmail.com.
Abstract: Context Aware Recommender Systems exploit specific situation of users for recommendations, hence are more accurate and satisfactory. Neighborhood based collaborative filtering is the most successful approach in this area owing of its simplicity, intuitiveness, efficiency and domain independence. The key of this approach is to find similarity between users or items using user–item–context rating matrix. Typically, context aware datasets are highly sparse since there are not enough or no preferences under most contextual conditions. Traditional similarity measures such as Pearson correlation coefficient, Cosine and Mean squared difference suffer from co-rated item problem and do not consider contextual conditions of the users. Therefore, these measures are not effective for sparse datasets. Therefore, the aim of this paper is to propose a new similarity measure and its variants based on Bhattacharyya Coefficient which are suitable for sparse datasets weighted by contextual similarity. Subsequently, we have applied them in neighborhood based algorithms where each component is contextually weighted. The experiments are performed on two contextually rich datasets which are especially designed to do personalization research instead traditional well known datasets. The results for Individual and Group recommendations indicate that the proposed similarity measure based algorithms have significantly increased the accuracy of predictions over traditional Pearson correlation coefficient measure based algorithms.
Keywords: Bhattacharya coefficient, Neighborhood based collaborative filtering, Contextual similarity, Sparse datasets, Group recommendations
