Analyses of multiple evidence combination
Proceedings of the 20th 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
Beyond independent relevance: methods and evaluation metrics for subtopic retrieval
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
From Retrieval Status Values to Probabilities of Relevance for Advanced IR Applications
Information Retrieval
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
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
Using historical data to enhance rank aggregation
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Language models, probability of relevance and relevance likelihood
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Novelty and diversity in information retrieval evaluation
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
A new approach to evaluating novel recommendations
Proceedings of the 2008 ACM conference on Recommender systems
Proceedings of the Second ACM International Conference on Web Search and Data Mining
Portfolio theory of information retrieval
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Exploiting query reformulations for web search result diversification
Proceedings of the 19th international conference on World wide web
Performance of recommender algorithms on top-n recommendation tasks
Proceedings of the fourth ACM conference on Recommender systems
A comparative analysis of cascade measures for novelty and diversity
Proceedings of the fourth ACM international conference on Web search and data mining
An analysis of NP-completeness in novelty and diversity ranking
Information Retrieval
Modeling score distributions in information retrieval
Information Retrieval
Characterizing search intent diversity into click models
Proceedings of the 20th international conference on World wide web
Search result diversity for informational queries
Proceedings of the 20th international conference on World wide web
Intent-oriented diversity in recommender systems
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Rank and relevance in novelty and diversity metrics for recommender systems
Proceedings of the fifth ACM conference on Recommender systems
On the role of novelty for search result diversification
Information Retrieval
Search result diversification in resource selection for federated search
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Beyond relevance: on novelty and diversity in tag recommendation
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Exploiting the diversity of user preferences for recommendation
Proceedings of the 10th Conference on Open Research Areas in Information Retrieval
Increasing evaluation sensitivity to diversity
Information Retrieval
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The intent-oriented search diversification methods developed in the field so far tend to build on generative views of the retrieval system to be diversified. Core algorithm components in particular redundancy assessment are expressed in terms of the probability to observe documents, rather than the probability that the documents be relevant. This has been sometimes described as a view considering the selection of a single document in the underlying task model. In this paper we propose an alternative formulation of aspect-based diversification algorithms which explicitly includes a formal relevance model. We develop means for the effective computation of the new formulation, and we test the resulting algorithm empirically. We report experiments on search and recommendation tasks showing competitive or better performance than the original diversification algorithms. The relevance-based formulation has further interesting properties, such as unifying two well-known state of the art algorithms into a single version. The relevance-based approach opens alternative possibilities for further formal connections and developments as natural extensions of the framework. We illustrate this by modeling tolerance to redundancy as an explicit configurable parameter, which can be set to better suit the characteristics of the IR task, or the evaluation metrics, as we illustrate empirically.