Learning for search result diversification

Zhu, Yun; Lan, Yanyan; Guo, Jiafeng; Cheng, Xueqi; Niu, Shuzi

doi:10.1145/2600428.2609634

Cited by 77 publications

(60 citation statements)

References 33 publications

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“…Diversity in search (both explicit and implicit) has seen a rich body of literature lately in [22,14,3,41,15,47,35,23]. Search result diversification aims to maximise the overall relevance of a document ranking to multiple query aspects, while minimising its redundancy with respect to these aspects.…”

Section: Search Results Diversificationmentioning

confidence: 99%

History by Diversity

Singh

Nejdl

Anand

2016

Proceedings of the 2016 ACM on Conference on Human Information Interaction and Retrieval

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Longitudinal corpora like newspaper archives are of immense value to historical research, and time as an important factor for historians strongly influences their search behaviour in these archives. While searching for articles published over time, a key preference is to retrieve documents which cover the important aspects from important points in time which is different from standard search behavior. To support this search strategy, we introduce the notion of a Historical Query Intent to explicitly model a historian's search task and define an aspect-time diversification problem over news archives.We present a novel algorithm, H D , that explicitly models the aspects and important time windows based on a historian's information seeking behavior. By incorporating temporal priors based on publication times and temporal expressions, we diversify both on the aspect and temporal dimensions. We test our methods by constructing a test collection based on The New York Times Collection with a workload of 30 queries of historical intent assessed manually. We find that H D outperforms all competitors in subtopic recall with a slight loss in precision. We also present results of a qualitative user study to determine wether this drop in precision is detrimental to user experience. Our results show that users still preferred H D 's ranking.

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Section: Search Results Diversificationmentioning

confidence: 99%

History by Diversity

Singh

Nejdl

Anand

2016

Proceedings of the 2016 ACM on Conference on Human Information Interaction and Retrieval

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“…Query result diversification has been extensively studied for recent years. Most previous work on query result diversification can be classified into the following two categories: implicit and explicit [31,39]. Our problem belongs to the former category.…”

Section: Related Workmentioning

confidence: 99%

Diversity-Aware Top-k Publish/Subscribe for Text Stream

Chen

Cong

2015

Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data

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Massive amount of text data are being generated by a huge number of web users at an unprecedented scale. These data cover a wide range of topics. Users are interested in receiving a few up-todate representative documents (e.g., tweets) that can provide them with a wide coverage of different aspects of their query topics. To address the problem, we consider the Diversity-Aware Topk Subscription (DAS) query. Given a DAS query, we continuously maintain an up-to-date result set that contains k most recently returned documents over a text stream for the query. The DAS query takes into account text relevance, document recency, and result diversity. We propose a novel solution to efficiently processing a large number of DAS queries over a stream of documents. We demonstrate the efficiency of our approach on realworld dataset and the experimental results show that our solution is able to achieve a reduction of the processing time by 60-75% compared with two baselines. We also study the effectiveness of the DAS query.

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“…He et al [23] introduced a flexible algorithm to combine multiple external resources. Zhu et al [24] provided a learning-to-rank approach to promote diversity. Yu and Ren [25] treated the diversity task as a multiple subtopic knapsack problem and re-ranked the documents like filling up multiple subtopic knapsacks.…”

Section: Related Workmentioning

confidence: 99%

Search Result Diversification Based on Query Facets

Dou

Wang

et al. 2015

J. Comput. Sci. Technol.

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In search engines, different users may search for different information by issuing the same query. To satisfy more users with limited search results, search result diversification re-ranks the results to cover as many user intents as possible. Most existing intent-aware diversification algorithms recognize user intents as subtopics, each of which is usually a word, a phrase, or a piece of description. In this paper, we leverage query facets to understand user intents in diversification, where each facet contains a group of words or phrases that explain an underlying intent of a query. We generate subtopics based on query facets and propose faceted diversification approaches. Experimental results on the public TREC 2009 dataset show that our faceted approaches outperform state-of-the-art diversification models.

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Learning for search result diversification

Cited by 77 publications

References 33 publications

History by Diversity

History by Diversity

Diversity-Aware Top-k Publish/Subscribe for Text Stream

Search Result Diversification Based on Query Facets

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