The probability ranking principle in IR
Readings in information retrieval
A language modeling approach to information retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
The use of MMR, diversity-based reranking for reordering documents and producing summaries
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Document language models, query models, and risk minimization for information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
A collaborative filtering algorithm and evaluation metric that accurately model the user experience
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
A study of mixture models for collaborative filtering
Information Retrieval
Less is more: probabilistic models for retrieving fewer relevant documents
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Probabilistic relevance ranking for collaborative filtering
Information Retrieval
A risk minimization framework for information retrieval
Information Processing and Management: an International Journal - Special issue: Formal methods for information retrieval
Optimizing multiple objectives in collaborative filtering
Proceedings of the fourth ACM conference on Recommender systems
Back to the roots: mean-variance analysis of relevance estimations
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
Beyond shot retrieval: searching for broadcast news items using language models of concepts
ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
Feature subspace selection for efficient video retrieval
MMM'10 Proceedings of the 16th international conference on Advances in Multimedia Modeling
Adaptive diversification of recommendation results via latent factor portfolio
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Expert Systems with Applications: An International Journal
Selecting effective expansion terms for diversity
Proceedings of the 10th Conference on Open Research Areas in Information Retrieval
Exploiting the diversity of user preferences for recommendation
Proceedings of the 10th Conference on Open Research Areas in Information Retrieval
Bias-variance analysis in estimating true query model for information retrieval
Information Processing and Management: an International Journal
The uncertain representation ranking framework for concept-based video retrieval
Information Retrieval
| Hi-index | 0.00 |
This paper concerns document ranking in information retrieval. In information retrieval systems, the widely accepted probability ranking principle (PRP) suggests that, for optimal retrieval, documents should be ranked in order of decreasing probability of relevance. In this paper, we present a new document ranking paradigm, arguing that a better, more general solution is to optimize top-n ranked documents as a whole, rather than ranking them independently. Inspired by the Modern Portfolio Theory in finance, we quantify a ranked list of documents on the basis of its expected overall relevance (mean) and its variance; the latter serves as a measure of risk, which was rarely studied for document ranking in the past. Through the analysis of the mean and variance, we show that an optimal rank order is the one that maximizes the overall relevance (mean) of the ranked list at a given risk level (variance). Based on this principle, we then derive an efficient document ranking algorithm. It extends the PRP by considering both the uncertainty of relevance predictions and correlations between retrieved documents. Furthermore, we quantify the benefits of diversification, and theoretically show that diversifying documents is an effective way to reduce the risk of document ranking. Experimental results on the collaborative filtering problem confirms the theoretical insights with improved recommendation performance, e.g., achieved over 300% performance gain over the PRP-based ranking on the user-based recommendation.