We study extensions of compressive sensing and low rank matrix recovery to the recovery of low rank tensors from incomplete linear information. While the reconstruction of low rank matrices via nuclear norm minimization is rather well-understand by now, almost no theory is available for the extension to higher order tensors due to various theoretical and computational difficulties arising for tensor decompositions. In fact, nuclear norm minimization for matrix recovery is a tractable convex relaxation approach, but the extension of the nuclear norm to tensors is in general NP-hard to compute. In this article, we introduce convex relaxations of the tensor nuclear norm which are computable in polynomial time via semidefinite programming. Our approach is based on theta bodies, a concept from computational algebraic geometry similar to the Lasserre relaxations. We introduce polynomial ideals which are generated by the second order minors corresponding to different matricizations of the tensor (where the tensor entries are treated as variables) such that the nuclear norm ball is the convex hull of the algebraic variety of the ideal. The $k$-th theta body for such an ideal generates a new norm which we call the $θ_k$-norm. We show that in the matrix case, these norms reduce to the nuclear norm. For tensors of order $d \geq 3$ however, we obtain new norms. The sequence of the corresponding unit-$θ_k$-norm balls converges asymptotically to the unit tensor nuclear norm ball. By providing the Gröbner basis for the ideals, we explicitly give semidefinite programs for the computation of the $θ_k$-norm and for the minimization of the $θ_k$-norm under an affine constraint. Numerical experiments for order-3 tensor recovery via $θ_1$-norm minimization suggest that our approach successfully reconstructs tensors of low rank from incomplete linear (random) measurements.