The Combination and Evaluation of Query Performance Prediction Methods

Using an Inverted Index Synopsis for Query Latency and Performance Prediction

Predicting the query latency by a search engine has important benefits, for instance, in allowing the search engine to adjust its configuration to address long-running queries without unnecessarily sacrificing its effectiveness. However, for the dynamic pruning techniques that underlie many commercial search engines, achieving accurate predictions of query latencies is difficult. We propose the use of index synopses—which are stochastic samples of the full index—for attaining accurate timing predictions. Indeed, we experiment using the TREC ClueWeb09 collection, and a large set of real user queries, and find that using small index synopses it is possible to very accurately estimate properties of the larger index, including sizes of posting list unions and intersections. Thereafter, we demonstrate that index synopses facilitate two key use cases: first, for query efficiency prediction, we show that predicting the query latencies on the full index and classifying long-running queries can be accurately achieved using index synopses; second, for query performance prediction, we show that the effectiveness of queries can be estimated more accurately using a synopsis index post-retrieval predictor than a pre-retrieval predictor. Overall, our experiments demonstrate the value of such a stochastic sample of a larger index at predicting the properties of the larger index.

Variable Selection in the Regularized Simultaneous Component Analysis Method for Multi-Source Data Integration

Interdisciplinary research often involves analyzing data obtained from different data sources with respect to the same subjects, objects, or experimental units. For example, global positioning systems (GPS) data have been coupled with travel diary data, resulting in a better understanding of traveling behavior. The GPS data and the travel diary data are very different in nature, and, to analyze the two types of data jointly, one often uses data integration techniques, such as the regularized simultaneous component analysis (regularized SCA) method. Regularized SCA is an extension of the (sparse) principle component analysis model to the cases where at least two data blocks are jointly analyzed, which - in order to reveal the joint and unique sources of variation - heavily relies on proper selection of the set of variables (i.e., component loadings) in the components. Regularized SCA requires a proper variable selection method to either identify the optimal values for tuning parameters or stably select variables. By means of two simulation studies with various noise and sparseness levels in simulated data, we compare six variable selection methods, which are cross-validation (CV) with the "one-standard-error" rule, repeated double CV (rdCV), BIC, Bolasso with CV, stability selection, and index of sparseness (IS) - a lesser known (compared to the first five methods) but computationally efficient method. Results show that IS is the best-performing variable selection method.

Oracle in Image Search

This article studies a novel problem in image search. Given a text query and the image ranking list returned by an image search system, we propose an approach to automatically predict the search performance. We demonstrate that, in order to estimate the mathematical expectations of Average Precision (AP) and Normalized Discounted Cumulative Gain (NDCG), we only need to predict the relevance probability of each image. We accomplish the task with a query-adaptive graph-based learning based on the images’ ranking order and visual content. We validate our approach with a large-scale dataset that contains the image search results of 1,165 queries from 4 popular image search engines. Empirical studies demonstrate that our approach is able to generate predictions that are highly correlated with the real search performance. Based on the proposed image search performance prediction scheme, we introduce three applications: image metasearch, multilingual image search, and Boolean image search. Comprehensive experiments are conducted to validate our approach.