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Item-based filtering technique is a collaborative filtering algorithm for recommendations. Correlation-based similarity measures such as cosine similarity, Pearson correlation, and its variants have inherent limitations on sparse datasets because items may not have enough ratings for predictions. In addition, traditional similarity measures mainly focus on the orientations of the rating vectors, not magnitude, and as a result two rating vectors with different magnitudes but oriented in the same direction, can be exactly similar. Another aspect is that on a set of items, similar users’ may have different rating pattern. In addition, to calculate the similarity between items, ratings of all co-rated users are considered; however, a judicious approach is to consider the similarity between users as a weight to find the similar neighbors of a target item. To mitigate these issues, a modified Bhattacharyya coefficient is proposed in this paper …