Efficient calculation of personalized document rankings
Claudia Heß, Klaus Stein · 2007
Social networks allow users getting personalized recommendations for interesting resources like websites or scientific papers by using reviews of users they trust. Search engines rank documents by using the reference structure to compute a visibility for each document with reference structure-based functions like PageRank. Personalized document visibilities can be computed by integrating both ap-proaches. We present a framework for incorporat-ing the information from both networks, and rank-ing algorithms using this information for personal-ized recommendations. Because the computation of document visibilities is costly and therefore can-not be done in runtime, i.e., when a user searches a document repository, we pay special attention to develop algorithms providing an efficient calcula-tion of personalized visibilities at query time based on precalculated global visibilities. The presented ranking algorithms are evaluated by a simulation study. 1