Search Result Diversification Research Articles

Div-BLAST: diversification of sequence search results.

Sequence similarity tools, such as BLAST, seek sequences most similar to a query from a database of sequences. They return results significantly similar to the query sequence and that are typically highly similar to each other. Most sequence analysis tasks in bioinformatics require an exploratory approach, where the initial results guide the user to new searches. However, diversity has not yet been considered an integral component of sequence search tools for this discipline. Some redundancy can be avoided by introducing non-redundancy during database construction, but it is not feasible to dynamically set a level of non-redundancy tailored to a query sequence. We introduce the problem of diverse search and browsing in sequence databases that produce non-redundant results optimized for any given query. We define diversity measures for sequences and propose methods to obtain diverse results extracted from current sequence similarity search tools. We also propose a new measure to evaluate the diversity of a set of sequences that is returned as a result of a sequence similarity query. We evaluate the effectiveness of the proposed methods in post-processing BLAST and PSI-BLAST results. We also assess the functional diversity of the returned results based on available Gene Ontology annotations. Additionally, we include a comparison with a current redundancy elimination tool, CD-HIT. Our experiments show that the proposed methods are able to achieve more diverse yet significant result sets compared to static non-redundancy approaches. In both sequence-based and functional diversity evaluation, the proposed diversification methods significantly outperform original BLAST results and other baselines. A web based tool implementing the proposed methods, Div-BLAST, can be accessed at cedar.cs.bilkent.edu.tr/Div-BLAST

DIVERSIFYING SEMANTIC ENTITY SEARCH: INDEPENDENT COMPONENT ANALYSIS APPROACH

Structured Web data are increasingly accessed using information retrieval methods and information retrieval increasingly relies on structured background knowledge. As users' searches are often directed towards finding information about entities rather than text documents, a key affordance of semantic search is the ability to retrieve relevant information about entities more precisely by utilizing the rich structured descriptions and background knowledge. Entity search also poses challenges for information retrieval methods. Entity descriptions are often short and conventional search term matching alone can be insufficient. As a consequence, the search engine should be able to increase the recall of the returned results and select a representative set of entities for a user; to diversify search results. This paper presents an approach to diversify entity search by using semantics present and inferred from the initial entity search results. Our approach utilizes ontologies as a source of background knowledge to improve recall of entity retrieval and independent component analysis to detect independent latent components shared by the entities. The search results are then diversified by selecting a representative set of entities based on their membership in the independent components. We demonstrate the performance of our approach through retrieval experiments conducted by using a real-world dataset composed from four entity databases. The results suggest that our approach can significantly improve effectiveness and diversity of entity search.

Adaptive diversification for tag-based social image retrieval

With the great popularity of social photos sharing websites, a tremendous volume of digital images is hosted together with their associated tags. Thus, extensive research efforts have been dedicated to tag-based social image search which enables users to formulate their queries using tags. However, tag queries are often ambiguous and typically short. Diversifying search results is a common solution in the absence of further knowledge about the user’s intention. Such approach aims to retrieve relevant images covering as much of the diverse meanings the query may have. However, not all queries are uniformly ambiguous and hence different diversification strategies might be suggested. In such a context, two new processes are jointly investigated at query pre-processing and post-processing levels. On the one hand, we propose a multi-view concept-based query expansion process, using a predefined list of semantic concepts, which aims to weight concepts from different views or contexts, aggregate the obtained weights and select the most representative ones using a dynamic threshold. On the other hand, we propose a new ranking process called “adaptive diverse relevance ranking” which automatically predicts an effective trade-off between relevance scores and diversity scores according to the query ambiguity level. Thorough experiments using 12 ambiguous queries over the NUS-WIDE dataset show the effectiveness of our approach versus classical uniform diversification approaches.

Introduction to the special issue on search intents and diversification

This special issue of the Information Retrieval journal is focused on the topic of query intents and diversification within search engines. Indeed, information retrieval users have diverse search needs, and the level of detail given in the user’s search query can differ widely depending on how clear the underlying information need itself is, the user’s search environment (e.g. input device), and how difficult it is for the user to express the need in the form of a query. For these reasons, many queries are ambiguous and/or underspecified. In light of this, research in accommodating different search intents has received a lot of attention lately. For example, major conferences like SIGIR, WWW and WSDM have begun to see papers on search result diversification, which aims to capture different user needs within one entry-point search result page. This problem was discussed intensively at the ECIR 2011 and WSDM 2012 Diversity in Document Retrieval Workshops. Also, in recent years, the TREC Web (2009–2012) and TREC Blog (2010) tracks have measured the diversity of participating systems in retrieving web pages and blog posts, respectively. Moreover, the recent INTENT tasks at NTCIR-9 and NTCIR-10 tackled not only search result diversification but also the task of mining intents given a query. The aim of this special issue is to highlight the advances and clarify the future goals in the ares of search intents and diversification. In particular, the articles included in this special issue address two areas within this vision, namely the identification of query

Mining subtopics from different aspects for diversifying search results

User queries to the Web tend to have more than one interpretation due to their ambiguity and other characteristics. How to diversify the ranking results to meet users' various potential information needs has attracted considerable attention recently. This paper is aimed at mining the subtopics of a query either indirectly from the returned results of retrieval systems or directly from the query itself to diversify the search results. For the indirect subtopic mining approach, clustering the retrieval results and summarizing the content of clusters is investigated. In addition, labeling topic categories and concept tags on each returned document is explored. For the direct subtopic mining approach, several external resources, such as Wikipedia, Open Directory Project, search query logs, and the related search services of search engines, are consulted. Furthermore, we propose a diversified retrieval model to rank documents with respect to the mined subtopics for balancing relevance and diversity. Experiments are conducted on the ClueWeb09 dataset with the topics of the TREC09 and TREC10 Web Track diversity tasks. Experimental results show that the proposed subtopic-based diversification algorithm significantly outperforms the state-of-the-art models in the TREC09 and TREC10 Web Track diversity tasks. The best performance our proposed algorithm achieves is ?-nDCG@5 0.307, IA-P@5 0.121, and ?#-nDCG@5 0.214 on the TREC09, as well as ?-nDCG@10 0.421, IA-P@10 0.201, and ?#-nDCG@10 0.311 on the TREC10. The results conclude that the subtopic mining technique with the up-to-date users' search query logs is the most effective way to generate the subtopics of a query, and the proposed subtopic-based diversification algorithm can select the documents covering various subtopics.

Diversifying Search Results through Pattern-Based Subtopic Modeling

Traditional information retrieval models do not necessarily provide users with optimal search experience because the top ranked documents may contain excessively redundant information. Therefore, satisfying search results should be not only relevant to the query but also diversified to cover different subtopics of the query. In this paper, the authors propose a novel pattern-based framework to diversify search results, where each pattern is a set of semantically related terms covering the same subtopic. They first apply a maximal frequent pattern mining algorithm to extract the patterns from retrieval results of the query. The authors then propose to model a subtopic with either a single pattern or a group of similar patterns. A profile-based clustering method is adapted to group similar patterns based on their context information. The search results are then diversified using the extracted subtopics. Experimental results show that the proposed pattern-based methods are effective to diversify the search results.

Leveraging Social Bookmarks from Partially Tagged Corpus for Improved Web Page Clustering

Automatic clustering of Web pages helps a number of information retrieval tasks, such as improving user interfaces, collection clustering, introducing diversity in search results, etc. Typically, Web page clustering algorithms use only features extracted from the page-text. However, the advent of social-bookmarking Web sites, such as StumbleUpon.com and Delicious.com, has led to a huge amount of user-generated content such as the social tag information that is associated with the Web pages. In this article, we present a subspace based feature extraction approach that leverages the social tag information to complement the page-contents of a Web page for extracting beter features, with the goal of improved clustering performance. In our approach, we consider page-text and tags as two separate views of the data, and learn a shared subspace that maximizes the correlation between the two views. Any clustering algorithm can then be applied in this subspace. We then present an extension that allows our approach to be applicable even if the Web page corpus is only partially tagged, that is, when the social tags are present for not all, but only for a small number of Web pages. We compare our subspace based approach with a number of baselines that use tag information in various other ways, and show that the subspace based approach leads to improved performance on the Web page clustering task. We also discuss some possible future work including an active learning extension that can help in choosing which Web pages to get tags for, if we only can get the social tags for only a small number of Web pages.

The 1st international workshop on diversity in document retrieval

When an ambiguous query is received, a sensible approach is for the information retrieval (IR) system to diversify the results retrieved for this query, in the hope that at least one of the interpretations of the query intent will satisfy the user. Diversity is an increasingly important topic, of interest to both academic researchers (such as participants in the TREC Web and Blog track diversity tasks), as well as to search engines professionals. In this workshop, we solicited submissions of both technical papers and position papers on three themes: approaches and models for diversity; the evaluation of diverse search results; and on applications of diverse search results. In the workshop, two invited talks, along with ten refereed papers were presented before breakout groups considered questions and issues with the three workshop themes.

Results selection diversity for web image retrieval

We describe a re-ranking method called dual rank to improve web image retrieval by clustering and reordering the images retrieved from an image search engine. General image retrieval exploits text and links the structure or little visual information to ‘understand’ the content of the web images, and usually lack the discriminative power to deliver visually diverse search results. The framework of dual rank is composed of inter-cluster rank and intra-cluster rank. To address the clustering problem, we first utilize a multipartite graph model to represent images and features, then formulate clustering as a constrained multi-objective optimization problem, which can be efficiently solved by semi-definite programming. We employ inter-cluster ranking function and intra-cluster ranking function to rank clusters and images, respectively. A representative image is selected from each cluster which together forms a diverse result set and as the optimal results for a query. We fuse different image features (text, colour, shape, texture, etc.) to improve the effect of clustering. Our method is evaluated against a standard search engine and significant improvements are reported in terms of Mean average precision, D@n and user experience.

Evaluating subtopic retrieval methods: Clustering versus diversification of search results

To address the inability of current ranking systems to support subtopic retrieval, two main post-processing techniques of search results have been investigated: clustering and diversification. In this paper we present a comparative study of their performance, using a set of complementary evaluation measures that can be applied to both partitions and ranked lists, and two specialized test collections focusing on broad and ambiguous queries, respectively. The main finding of our experiments is that diversification of top hits is more useful for quick coverage of distinct subtopics whereas clustering is better for full retrieval of single subtopics, with a better balance in performance achieved through generating multiple subsets of diverse search results. We also found that there is little scope for improvement over the search engine baseline unless we are interested in strict full-subtopic retrieval, and that search results clustering methods do not perform well on queries with low divergence subtopics, mainly due to the difficulty of generating discriminative cluster labels.

A weighted-graph-based approach for diversifying search results

Despite the explosive growth of internet usage, satisfying users’ experience in web search while understanding the dynamic nature of search engine ranking remains a challenge. The main reason is that user queries and web documents may belong to different categories given a taxonomy of information. In this study, we attempt to detangle the ambiguity in web search by using a systematic approach. A weighted graph model is utilised to represent the complicated documents network from web search results. In this model, document topics are explicitly defined from the weighted document graph with an unsupervised learning method. On weighing each topic, both the size of the topic and the intra-topic document importance distribution are considered. In order to achieve web search results diversity, the reranking method proposed in this work leverages on the relevance of the documents to the query and related topics. Evaluation results on synthetic data and realistic web pages show the efficacy of a proposed system.

Towards a Relevant and Diverse Search of Social Images

Recent years have witnessed the great success of social media websites. Tag-based image search is an important approach to accessing the image content on these websites. However, the existing ranking methods for tag-based image search frequently return results that are irrelevant or not diverse. This paper proposes a diverse relevance ranking scheme that is able to take relevance and diversity into account by exploring the content of images and their associated tags. First, it estimates the relevance scores of images with respect to the query term based on both the visual information of images and the semantic information of associated tags. Then, we estimate the semantic similarities of social images based on their tags. Based on the relevance scores and the similarities, the ranking list is generated by a greedy ordering algorithm which optimizes average diverse precision, a novel measure that is extended from the conventional average precision. Comprehensive experiments and user studies demonstrate the effectiveness of the approach. We also apply the scheme for web image search reranking, and it is shown that the diversity of search results can be enhanced while maintaining a comparable level of relevance.

Automatic Home Medical Product Recommendation

Web-based personal health records (PHRs) are being widely deployed. To improve PHR's capability and usability, we proposed the concept of intelligent PHR (iPHR). In this paper, we use automatic home medical product recommendation as a concrete application to demonstrate the benefits of introducing intelligence into PHRs. In this new application domain, we develop several techniques to address the emerging challenges. Our approach uses treatment knowledge and nursing knowledge, and extends the language modeling method to (1) construct a topic-selection input interface for recommending home medical products, (2) produce a global ranking of Web pages retrieved by multiple queries, and (3) provide diverse search results. We demonstrate the effectiveness of our techniques using USMLE medical exam cases.