Intent-aware search result diversification

Santos, Rodrygo L. T.; Macdonald, Craig; Ounis, Iadh

doi:10.1145/2009916.2009997

Cited by 73 publications

(53 citation statements)

References 37 publications

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“…In previous work diversification has mainly been applied for better topical variety in search results [9,25,26,27]. Equal preference is typically given to all aspects.…”

Section: Introductionmentioning

confidence: 99%

Sentiment diversification with different biases

Aktolga

Allan

2013

Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval

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Prior search result diversification work focuses on achieving topical variety in a ranked list, typically equally across all aspects. In this paper, we diversify with sentiments according to an explicit bias. We want to allow users to switch the result perspective to better grasp the polarity of opinionated content, such as during a literature review. For this, we first infer the prior sentiment bias inherent in a controversial topic -the 'Topic Sentiment'. Then, we utilize this information in 3 different ways to diversify results according to various sentiment biases: (1) Equal diversification to achieve a balanced and unbiased representation of all sentiments on the topic; (2) Diversification towards the Topic Sentiment, in which the actual sentiment bias in the topic is mirrored to emphasize the general perception of the topic; (3) Diversification against the Topic Sentiment, in which documents about the 'minority' or outlying sentiment(s) are boosted and those with the popular sentiment are demoted.Since sentiment classification is an essential tool for this task, we experiment by gradually degrading the accuracy of a perfect classifier down to 40%, and show which diversification approaches prove most stable in this setting. The results reveal that the proportionality-based methods and our SCSF model, considering sentiment strength and frequency in the diversified list, yield the highest gains. Further, in case the Topic Sentiment cannot be reliably estimated, we show how performance is affected by equal diversification when actually an emphasis either towards or against the Topic Sentiment is desired: in the former case, an average of 6.48% is lost across all evaluation measures, whereas in the latter case this is 16.23%, confirming that bias-specific sentiment diversification is crucial.

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“…In previous work diversification has mainly been applied for better topical variety in search results [9,25,26,27]. Equal preference is typically given to all aspects.…”

Section: Introductionmentioning

confidence: 99%

Sentiment diversification with different biases

Aktolga

Allan

2013

Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval

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“…We use existing measures MAP-IA@n [1,3], MRR-IA@n [1,3] and NDCG-IA@n [3,18] to evaluate different aspects of our ranking algorithms. We review these measures below.…”

Section: Dataset and Performance Measuresmentioning

confidence: 99%

“…This measure was used in different works (e.g., [3,18]) and it is an adaptation of the NDCG (normalized discounted cumulative gain [10]) for measuring the diversification of the search results produced by a search system. This adaptation takes the results retrieved for different categories (DCs) into consideration, making the measure capable of also measuring the effectiveness of diversification.…”

Section: Ndcg-ia@n (Intent Aware (Ia) Ndcg) [1 3]mentioning

confidence: 99%

“…They defined a diversification objective based on the estimated relevance of documents to multiple sub-queries, as well as on the relative importance of each sub-query to the original query. Later they [17,18] proposed a selective diversification approach. In particular, given an unseen query, their approach learns a trade-off between relevance and diversity, based on optimal trade-offs observed for similar training queries.…”

Section: Introductionmentioning

confidence: 99%

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mNIR: Diversifying Search Results Based on a Mixture of Novelty, Intention and Relevance

Hemayati

Dehkordi

Meng

2012

Web Information Systems Engineering - WISE 2012

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ABSTRACT. Current search engines do not explicitly take different meanings and usages of user queries into consideration when they rank the search results. As a result, they tend to retrieve results that cover the most popular meanings or usages of the query. Consequently, users who want results that cover a rare meaning or usage of query or results that cover all different meanings/usages may have to go through a large number of results in order to find the desired ones. Another problem with current search engines is that they do not adequately take users' intention into consideration. In this paper, we introduce a novel result ranking algorithm (mNIR) that explicitly takes result novelty, user intention-based distribution and result relevancy into consideration and mixes them to achieve better result ranking. We analyze how giving different emphasis to the above three aspects would impact the overall ranking of the results. Our approach builds on our previous method for identifying and ranking possible categories of any user query based on the meanings and usages of the terms and phrases within the query. These categories are also used to generate category queries for retrieving results matching different meanings/usages of the original user query. Our experimental results show that the proposed algorithm can outperform state-of-the-art diversification approaches.

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“…To accommodate such different user needs (or user intents) given a query, research in search result diversification has received much attention recently (e.g., Refs. [1], [10], [14], [15], [27]). TREC (Text Retrieval Conference) * 1 began a diversity task in the Web track in 2009 [6] * 2 , and NTCIR (NII Testbeds and Community for Information access Research) * 3 concluded its first INTENT task for mining intents and selectively diversifying search results in 2011 [29] * 4 .…”

Section: Introductionmentioning

confidence: 99%

Web Search Evaluation with Informational and Navigational Intents

Sakai

2013

Journal of Information Processing

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Given an ambiguous or underspecified web search query, search result diversification aims at accomodating different user intents within a single "entry-point" result page. However, some intents are informational, for which many relevant pages may help, while others are navigational, for which only one web page is required. We propose new evaluation metrics for search result diversification that considers this distinction, as well as the condordance test for comparing the intuitiveness of a given pair of metrics quantitatively. Our main experimental findings are: (a) In terms of discriminative power which reflects statistical reliability, the proposed metrics, DIN -nDCG and P+Q , are comparable to intent recall and D -nDCG, and possibly superior to α-nDCG; (b) In terms of the concordance test which quantifies the agreement of a diversity metric with a gold standard metric that represents a basic desirable property, DIN -nDCG is superior to other diversity metrics in its ability to reward both diversity and relevance at the same time. Moreover, both D -nDCG and DIN -nDCG significantly outperform α-nDCG in their ability to reward diversity, to reward relevance, and to reward both at the same time. In addition, we demonstrate that the randomised Tukey's Honestly Significant Differences test that takes the entire set of available runs into account is substantially more conservative than the paired bootstrap test that only considers one run pair at a time, and therefore recommend the former approach for significance testing when a set of runs is available for evaluation.

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Intent-aware search result diversification

Cited by 73 publications

References 37 publications

Sentiment diversification with different biases

Sentiment diversification with different biases

mNIR: Diversifying Search Results Based on a Mixture of Novelty, Intention and Relevance

Web Search Evaluation with Informational and Navigational Intents

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