Ranking (information retrieval)

About: Ranking (information retrieval) is a research topic. Over the lifetime, 21109 publications have been published within this topic receiving 435130 citations.

Critically Examining the "Neural Hype": Weak Baselines and the Additivity of Effectiveness Gains from Neural Ranking Models

Abstract: Is neural IR mostly hype? In a recent SIGIR Forum article, Lin expressed skepticism that neural ranking models were actually improving ad hoc retrieval effectiveness in limited data scenarios. He provided anecdotal evidence that authors of neural IR papers demonstrate "wins" by comparing against weak baselines. This paper provides a rigorous evaluation of those claims in two ways: First, we conducted a meta-analysis of papers that have reported experimental results on the TREC Robust04 test collection. We do not find evidence of an upward trend in effectiveness over time. In fact, the best reported results are from a decade ago and no recent neural approach comes close. Second, we applied five recent neural models to rerank the strong baselines that Lin used to make his arguments. A significant improvement was observed for one of the models, demonstrating additivity in gains. While there appears to be merit to neural IR approaches, at least some of the gains reported in the literature appear illusory.

Document representation and query expansion models for blog recommendation

Abstract: We explore several different document representation models and two query expansion models for the task of recommending blogs to a user in response to a query. Blog relevance ranking differs from traditional document ranking in ad-hocinformation retrieval in several ways: (1) the unit of output (the blog) is composed of a collection of documents (the blog posts) rather than a single document, (2) the query represents an ongoing and typically multifaceted interest in the topic rather than a passing ad-hoc information need and (3) due to the propensity of spam, splogs, and tangential comments, the blogosphere is particularly challenging to use as a source for high-quality query expansion terms. We address these differences at the document representation level, by comparing retrieval models that view either the blog or its constituent posts as the atomic units of retrieval, and at the query expansion level, by making novel use of the links and anchor text in Wikipedia1 to expand a user's initial query. We develop two complementary models of blog retrieval that perform at comparable levels of precision and recall. We also show consistent and significant improvement across all models using our Wikipedia expansion strategy.

Weakly-Supervised Acquisition of Open-Domain Classes and Class Attributes from Web Documents and Query Logs

Abstract: A new approach to large-scale information extraction exploits both Web documents and query logs to acquire thousands of opendomain classes of instances, along with relevant sets of open-domain class attributes at precision levels previously obtained only on small-scale, manually-assembled classes.

Beyond clicks: query reformulation as a predictor of search satisfaction

Abstract: To understand whether a user is satisfied with the current search results, implicit behavior is a useful data source, with clicks being the best-known implicit signal. However, it is possible for a non-clicking user to be satisfied and a clicking user to be dissatisfied. Here we study additional implicit signals based on the relationship between the user's current query and the next query, such as their textual similarity and the inter-query time. Using a large unlabeled dataset, a labeled dataset of queries and a labeled dataset of user tasks, we analyze the relationship between these signals. We identify an easily-implemented rule that indicates dissatisfaction: that a similar query issued within a time interval that is short enough (such as five minutes) implies dissatisfaction. By incorporating additional query-based features in the model, we show that a query-based model (with no click information) can indicate satisfaction more accurately than click-based models. The best model uses both query and click features. In addition, by comparing query sequences in successful tasks and unsuccessful tasks, we observe that search success is an incremental process for successful tasks with multiple queries.