A discriminative method for global query expansion and term reweighting using co-occurrence graphs

This article presents a new query expansion (QE) method aiming to tackle term mismatch in information retrieval (IR). Previous research showed that selecting good expansion terms which do not hurt retrieval effectiveness remains an open and challenging research question. Our method investigates how global statistics of term co-occurrence can be used effectively to enhance expansion term selection and reweighting. Indeed, we build a co-occurrence graph using a context window approach over the entire collection, thus adopting a global QE approach. Then, we employ a semantic similarity measure inspired by the Okapi BM25 model, which allows to evaluate the discriminative power of words and to select relevant expansion terms based on their similarity to the query as a whole. The proposed method includes a reweighting step where selected terms are assigned weights according to their relevance to the query. What’s more, our method does not require matrix factorisation or complex text mining processes. It only requires simple co-occurrence statistics about terms, which reduces complexity and insures scalability. Finally, it has two free parameters that may be tuned to adapt the model to the context of a given collection and control co-occurrence normalisation. Extensive experiments on four standard datasets of English (TREC Robust04 and Washington Post) and French (CLEF2000 and CLEF2003) show that our method improves both retrieval effectiveness and robustness in terms of various evaluation metrics and outperforms competitive state-of-the-art baselines with significantly better results. We also investigate the impact of varying the number of expansion terms on retrieval results.

Retrieval Effectiveness of Google on Reverse Image Search

This study investigates the reverse image search performance of Google, in terms of Average Precisions (APs) and Average Normalized Recalls (ANRs) at various cut-off points,on finding out similar images by using fresh Image Queries (IQs) from the five categories “Fashion,” “Computer,” “Home,” “Sports,” and “Toys.” The aim is to have an insight about retrieval effectiveness of Google on reverse image search and then motivate researchers and inform users. Five fresh IQs with different main concepts were created for each of the five categories. These 25 IQs were run on the search engine, and for each, the first 100 images retrieved were evaluated with binary relevance judgment. At the cut-off points 20, 40, 60, 80, and 100, both APs and ANRs were calculated for each category and for all 25 IQs. The AP range is from 41.60% (Toys—cut-off point 100) to 71% (Home—cut-off point 20). The ANR range is from 47.21% (Toys—cut-off point 20) to 71.31% (Computer—cut-off point 100). If the categories are ignored; when more images were evaluated, the performance of displaying relevant images in higher ranks increased, whereas the performance of retrieving relevant images decreased. It seems that the information retrieval effectiveness of Google on reverse image search needs to be improved.

Reinforcement Learning Based Query Routing Approach for P2P Systems

Peer-to-peer (P2P) systems have offered users an efficient way to share various resources and access diverse services over the Internet. In unstructured P2P systems, resource storage and indexation are fully distributed among participating peers. Therefore, locating peers sharing pertinent resources for a specific user query is a challenging issue. In fact, effective query routing requires smart decisions to select a certain number of peers with respect to their relevance for the query instead of choosing them at random. In this respect, we introduce here a new query-oriented approach, called the reinforcement learning-based query routing approach (RLQR). The main goal of RLQR is to reach high retrieval effectiveness as well as a lower search cost by reducing the number of exchanged messages and contacted peers. To achieve this, the RLQR relies on information gathered from previously sent queries to identify relevant peers for forthcoming queries. Indeed, we formulate the query routing issue as the reinforcement learning problem and introduce a fully distributed approach for addressing it. In addition, RLQR addresses the well-known cold-start issue during the training stage, which allows it to improve its retrieval effectiveness and search cost continuously, and, therefore, goes quickly through the cold-start phase. Performed simulations demonstrate that RLQR outperforms pioneering query routing approaches in terms of retrieval effectiveness and communications cost.

Retrieval effectiveness of image search engines

Purpose The purpose of this study is to explore the retrieval effectiveness of three image search engines (ISE) – Google Images, Yahoo Image Search and Picsearch in terms of their image retrieval capability. It is an effort to carry out a Cranfield experiment to know how efficient the commercial giants in the image search are and how efficient an image specific search engine is. Design/methodology/approach The keyword search feature of three ISEs – Google images, Yahoo Image Search and Picsearch – was exploited to make search with keyword captions of photos as query terms. Selected top ten images were used to act as a testbed for the study, as images were searched in accordance with features of the test bed. Features to be looked for included size (1200 × 800), format of images (JPEG/JPG) and the rank of the original image retrieved by ISEs under study. To gauge the overall retrieval effectiveness in terms of set standards, only first 50 result hits were checked. Retrieval efficiency of select ISEs were examined with respect to their precision and relative recall. Findings Yahoo Image Search outscores Google Images and Picsearch both in terms of precision and relative recall. Regarding other criteria – image size, image format and image rank in search results, Google Images is ahead of others. Research limitations/implications The study only takes into consideration basic image search feature, i.e. text-based search. Practical implications The study implies that image search engines should focus on relevant descriptions. The study evaluated text-based image retrieval facilities and thereby offers a choice to users to select best among the available ISEs for their use. Originality/value The study provides an insight into the effectiveness of the three ISEs. The study is one of the few studies to gauge retrieval effectiveness of ISEs. Study also produced key findings that are important for all ISE users and researchers and the Web image search industry. Findings of the study will also prove useful for search engine companies to improve their services.