Standard procedures for estimating item parameters in Item Response Theory models make no use of auxiliary information about test items, such as their format or content, or the skills they require for solution. This paper describes a framework for exploiting this information, thereby enhancing the precision and stability of item parameter estimates and providing diagnostic information about items' operating characteristics. In the proposed model, final item parameter estimates represent a compromise between Linear Logistic Test Model estimates, where items with identical features would have identical estimates, and unrestricted maximum likelihood estimates. The principles were illustrated in a context for which a relatively simple approximation is available: empirical Bayes (EB) estimation of Rasch item difficulty parameters. Computation proceeded in three steps: (1) unrestricted maximum likelihood estimates of item parameters; (2) point estimates of the regression parameters; and (3) final estimates of item parameters. A numerical example applied EB estimation procedures to the responses from 150 sixth graders on the Fractions subtest of the California Achievement Test. Three models, varying in their assumptions of item exchangeability, were fitted to the data. Analysis showed that auxiliary information about item features contributed as much information about item parameters as the likelihood function did. (Author/LPG)