This paper uncovers a new phenomenon in web search that we call domain bias — a user’s propensity to believe that a page is more relevant just because it comes from a particul...
Samuel Ieong, Nina Mishra, Eldar Sadikov, Li Zhang
Time-series data, which are a series of one-dimensional real numbers, have been studied in various database applications. In this paper, we extend the traditional similarity searc...
Seok-Lyong Lee, Seok-Ju Chun, Deok-Hwan Kim, Ju-Ho...
A token is hidden in one of several boxes and then the boxes are locked. The probability of placing the token in each of the boxes is known. A searcher is looking for the token by...
Amotz Bar-Noy, Panagiotis Cheilaris, Yi Feng 0002,...
We present a new algorithm for conformant probabilistic planning, which for a given horizon produces a plan that maximizes the probability of success under quantified uncertainty ...
Traditional machine-learned ranking algorithms for web search are trained in batch mode, which assume static relevance of documents for a given query. Although such a batch-learni...