Ambiguous and multifaceted queries widely exist in academic and commercial search engines. Identifying the popular subtopics of queries is an important issue for search engines. In this paper, we propose a novel method to discover the popular subtopics for a given query. Our method first constructs a search behavior tripartite graph based on the search log data. Then, we utilize a subtractive i...

Retrieving relevant patient cohorts has the potential to accelerate clinical research. Recent evaluations have shown promising results, but also relevance measures that still need to be improved. To address the challenge of better modelling hospital visit relevance, we considered the impact of two forms of medical knowledge on the quality of patient cohorts. First, we automatically identified t...

We propose a hybrid model of differential privacy that considers a combination of regular and opt-in users who desire the differential privacy guarantees of the local privacy model and the trusted curator model, respectively. We demonstrate that within this model, it is possible to design a new type of blended algorithm for the task of privately computing the most popular records of a web searc...

Web users often issue a series of related queries, which form a search session, to fulfill complicated information needs. Query aggregation utilizes and combines multiple queries for session search. Prior research has demonstrated that query aggregation is an effective technique for session search that can generally achieve a 15% ∼ 20% nDCG@10 improvement on finding relevant documents for the l...

In this paper we propose a method that integrates the notion of understandability, as a factor of document relevance, into the evaluation of information retrieval systems for consumer health search. We consider the gain-discount evaluation framework (RBP, nDCG, ERR) and propose two understandability-based variants (uRBP) of rank biased precision, characterised by an estimation of understandabil...

In this paper, we study the problem of retrieving a ranked list of top-N items to a target user in recommender systems. We first develop a novel preference model by distinguishing different rating patterns of users, and then apply it to existing collaborative filtering (CF) algorithms. Our preference model, which is inspired by a voting method, is well-suited for representing qualitative user p...