This paper focuses on the items/users representation in the domain of recommender systems. These systems compute similarities between items (and/or users) to recommend new items to users based on their previous preferences. It is often useful to consider the characteristics (a.k.a features or attributes) of the items and/or users. This represents items/users by vectors that can be very large, sparse and space-consuming. In this paper, we propose a new accurate method for representing items/users with low size data structures that relies on two concepts: (1) item/user representation is based on bloom filter vectors, and (2) the usage of these filters to compute bitwise AND similarities and bitwise XNOR similarities. This work is motivated by three ideas: (1) detailed vector representations are large and sparse, (2) comparing more features of items/users may achieve better accuracy for items similarities, and (3) similarities are not only in common existing aspects, but also in common missing aspects. We have experimented this approach on the publicly available MovieLens dataset. The results show a good performance in comparison with existing approaches such as standard vector representation and Singular Value Decomposition (SVD).