scispace - formally typeset
Search or ask a question
Topic

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.


Papers
More filters
Proceedings Article
06 Jan 2007
TL;DR: This paper presents ItemRank, a random-walk based scoring algorithm, which can be used to rank products according to expected user preferences, in order to recommend top-rank items to potentially interested users.
Abstract: Recommender systems are an emerging technology that helps consumers to find interesting products. A recommender system makes personalized product suggestions by extracting knowledge from the previous users interactions. In this paper, we present "ItemRank", a random-walk based scoring algorithm, which can be used to rank products according to expected user preferences, in order to recommend top-rank items to potentially interested users. We tested our algorithm on a standard database, the MovieLens data set, which contains data collected from a popular recommender system on movies, that has been widely exploited as a benchmark for evaluating recently proposed approaches to recommender system (e.g. [Fouss et al., 2005; Sarwar et al., 2002]). We compared ItemRank with other state-of-the-art ranking techniques (in particular the algorithms described in [Fouss et al., 2005]). Our experiments show that ItemRank performs better than the other algorithms we compared to and, at the same time, it is less complex than other proposed algorithms with respect to memory usage and computational cost too.

311 citations

Proceedings ArticleDOI
03 Nov 2003
TL;DR: It is shown how time can be incorporated into both query-likelihood models and relevance models, and shows that time-based models perform as well as or better than the best of the heuristic techniques.
Abstract: We explore the relationship between time and relevance using TREC ad-hoc queries. A type of query is identified that favors very recent documents. We propose a time-based language model approach to retrieval for these queries. We show how time can be incorporated into both query-likelihood models and relevance models. These models were used for experiments comparing time-based language models to heuristic techniques for incorporating document recency in the ranking. Our results show that time-based models perform as well as or better than the best of the heuristic techniques.

308 citations

Book
30 Jun 2015
TL;DR: This survey summarizes advances in modeling user click behavior on a web search engine result page and presents simple click models as well as more complex models aimed at improving search result ranking.
Abstract: With the rapid growth of web search in recent years the problem of modeling its users has started to attract more and more attention of the information retrieval community. This has several motivations. By building a model of user behavior we are essentially developing a better understanding of a user, which ultimately helps us to deliver a better search experience. A model of user behavior can also be used as a predictive device for non-observed items such as document relevance, which makes it useful for improving search result ranking. Finally, in many situations experimenting with real users is just infeasible and hence user simulations based on accurate models play an essential role in understanding the implications of algorithmic changes to search engine results or presentation changes to the search engine result page. In this survey we summarize advances in modeling user click behavior on a web search engine result page. We present simple click models as well as more complex models aimed at capturing non-trivial user behavior patterns on modern search engine result pages. We discuss how these models compare to each other, what challenges they have, and what ways there are to address these challenges. We also study the problem of evaluating click models and discuss the main applications of click models.

308 citations

Book
13 Oct 2010
TL;DR: The editors first offer a thorough introduction, including a systematic categorization according to learning task and learning technique, along with a unified notation, and the first half of the book is organized into parts on applications of preference learning in multiattribute domains, information retrieval, and recommender systems.
Abstract: The topic of preferences is a new branch of machine learning and data mining, and it has attracted considerable attention in artificial intelligence research in previous years. It involves learning from observations that reveal information about the preferences of an individual or a class of individuals. Representing and processing knowledge in terms of preferences is appealing as it allows one to specify desires in a declarative way, to combine qualitative and quantitative modes of reasoning, and to deal with inconsistencies and exceptions in a flexible manner. And, generalizing beyond training data, models thus learned may be used for preference prediction. This is the first book dedicated to this topic, and the treatment is comprehensive. The editors first offer a thorough introduction, including a systematic categorization according to learning task and learning technique, along with a unified notation. The first half of the book is organized into parts on label ranking, instance ranking, and object ranking; while the second half is organized into parts on applications of preference learning in multiattribute domains, information retrieval, and recommender systems. The book will be of interest to researchers and practitioners in artificial intelligence, in particular machine learning and data mining, and in fields such as multicriteria decision-making and operations research.

304 citations

Posted Content
TL;DR: This paper proves that the biased evaluation of candidate models within a predefined search space is due to inherent unfairness in the supernet training, and proposes two levels of constraints: expectation fairness and strict fairness.
Abstract: One of the most critical problems in weight-sharing neural architecture search is the evaluation of candidate models within a predefined search space. In practice, a one-shot supernet is trained to serve as an evaluator. A faithful ranking certainly leads to more accurate searching results. However, current methods are prone to making misjudgments. In this paper, we prove that their biased evaluation is due to inherent unfairness in the supernet training. In view of this, we propose two levels of constraints: expectation fairness and strict fairness. Particularly, strict fairness ensures equal optimization opportunities for all choice blocks throughout the training, which neither overestimates nor underestimates their capacity. We demonstrate that this is crucial for improving the confidence of models' ranking. Incorporating the one-shot supernet trained under the proposed fairness constraints with a multi-objective evolutionary search algorithm, we obtain various state-of-the-art models, e.g., FairNAS-A attains 77.5% top-1 validation accuracy on ImageNet. The models and their evaluation codes are made publicly available online this http URL .

304 citations


Network Information
Related Topics (5)
Web page
50.3K papers, 975.1K citations
83% related
Ontology (information science)
57K papers, 869.1K citations
82% related
Graph (abstract data type)
69.9K papers, 1.2M citations
82% related
Feature learning
15.5K papers, 684.7K citations
81% related
Supervised learning
20.8K papers, 710.5K citations
81% related
Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20241
20233,112
20226,541
20211,105
20201,082
20191,168