1. A system that facilitates providing query results to a user, comprising: a processor; system memory; an interface that receives data related to a query associated with a specific user; a user log component that comprises at least a repository of historic activities, behaviors, or combinations thereof specific to a user, wherein the logged historic user activities and/or behaviors are related to prospective interactions with objects related to the query results; a rank component that provides ranked query results adapted in relation to a specific user log, wherein the query results are at least in part prioritized utilizing a transition probability determination related to the specific user for user transitions between objects related to prospective query results based on historical user activity, user behavior, or combinations thereof, wherein the transition probability determination comprises utilizing a weighting technique that implements the following representation of a Markov model: M = ( M 11 M 12 M 13 M 21 M 22 M 23 M 31 M 32 M 33 ) , ⁢ where ⁢ ⁢ ∑ y ⁢ M iy = 1 Equation ⁢ ⁢ 1 and wherein the weighting technique uses the following to normalize aggregated counts related to the document to identify a probability: M ij = M ij ∑ y ⁢ M iy , where i is a first document and j is a disparate document, and M ij represents the probability of transitioning from document i to document j, and wherein the weighting technique uses the following to calculate a user rank of a document, wherein the user rank is recursively defined by the user rank of documents and user has followed to locate the document: 
 UserRank( X )=(1− d )+ d Σ X P PR ( P→X )(UserRank( P )), where d is a damping factor and P→X is going to X from P; and a results providing component configured to provide prioritized query results that incorporate transition probability related to a document.
1. A system that facilitates providing query results to a user, comprising: a processor; system memory; an interface that receives data related to a query associated with a specific user; a user log component that comprises at least a repository of historic activities, behaviors, or combinations thereof specific to a user, wherein the logged historic user activities and/or behaviors are related to prospective interactions with objects related to the query results; a rank component that provides ranked query results adapted in relation to a specific user log, wherein the query results are at least in part prioritized utilizing a transition probability determination related to the specific user for user transitions between objects related to prospective query results based on historical user activity, user behavior, or combinations thereof, wherein the transition probability determination comprises utilizing a weighting technique that implements the following representation of a Markov model: M = ( M 11 M 12 M 13 M 21 M 22 M 23 M 31 M 32 M 33 ) , ⁢ where ⁢ ⁢ ∑ y ⁢ M iy = 1 Equation ⁢ ⁢ 1 and wherein the weighting technique uses the following to normalize aggregated counts related to the document to identify a probability: M ij = M ij ∑ y ⁢ M iy , where i is a first document and j is a disparate document, and M ij represents the probability of transitioning from document i to document j, and wherein the weighting technique uses the following to calculate a user rank of a document, wherein the user rank is recursively defined by the user rank of documents and user has followed to locate the document: 
 UserRank( X )=(1− d )+ d Σ X P PR ( P→X )(UserRank( P )), where d is a damping factor and P→X is going to X from P; and a results providing component configured to provide prioritized query results that incorporate transition probability related to a document. 2. The system of claim 1 , the transition probability determination is related to the specific user for user transitions between documents related to prospective query results based on historical user activity, user behavior, or combinations thereof.