Click Through Rate (CTR) is an important metric for ad systems, job portals, recommendation systems. CTR impacts publisher's revenue, advertiser's bid amounts in "pay for performance" business models. We learn regression models using features of the job, optional click history of job, features of "related" jobs. We show that our models predict CTR much better than predicting avg. CTR for all job listings, even in absence of the click history for the job listing.

Personalized page rank, related to random walks with restarts and conductance in resistive networks, is a frequent search paradigm for graph-structured databases. While efficient batch algorithms exist for static whole-graph page rank, interactive query-time personalized page rank has proved more challenging. Here we describe how to select and build indices for a popular class of page rank algorithms, so as to provide real-time personalized page rank and smoothly trade off between index size, preprocessing time, and query speed. We achieve this by developing a precise, yet efficiently estimated performance model for personalized page rank query execution. We use this model in conjunction with a query workload in a cost-benefit type index optimizer. On millions of queries from CiteSeer and its data graphs with 74-320 thousand nodes, our algorithm runs 50-400 x faster than whole-graph page rank, the gap growing with graph size. Index size is 10-20% of a text index. Ranking accuracy is above 94%.

In entity-relation (ER) graphs (V,E), nodes V represent typed entities and edges E represent typed relations. For dynamic personalized PageRank queries, nodes are ranked by their steady-state probabilities obtained using the standard random surfer model. In this work, we propose a framework to answer top-k graph conductance queries. Our top-k ranking technique leads to a 4X speedup, and overall, our system executes queries 200-1600X faster than whole-graph PageRank. Some queries might contain hard predicates i.e. predicates that must be satisfied by the answer nodes. E.g. we may seek authoritative papers on public key cryptography, but only those written during 1997. We extend our system to handle hard predicates. Our system achieves these substantial query speedups while consuming only 10-20% of the space taken by a regular text index.