Search Result Diversification Research Articles

GDESA: Greedy Diversity Encoder with Self-attention for Search Results Diversification

Search result diversification aims to generate diversified search results so as to meet the various information needs of users. Most of those existing diversification methods greedily select the optimal documents one-by-one comparing with the selected document sequences. Due to the fact that the information utilities of the candidate documents are not independent, a model based on greedy document selection may not produce the global optimal ranking results. To address this issue, some work proposes to model global document interactions regardless of whether a document is selected, which is inconsistent with actual user behavior. In this article, we propose a new supervised diversification framework as an ensemble of global interaction and document selection. Based on a self-attention encoder-decoder structure and an RNN-based document selection component, the model can simultaneously leverage both the global interactions among all the documents and the interactions between the selected sequence and each unselected document. This framework is called Greedy Diversity Encoder with Self-Attention (GDESA). Experimental results show that GDESA outperforms previous methods that rely just on global interactions, and our further analysis demonstrates that using both global interactions and document selection is necessary and beneficial.

Optimize What You Evaluate With: Search Result Diversification Based on Metric Optimization

Most of the existing methods for search result diversification (SRD) appeal to the greedy strategy for generating diversified results, which is formulated as a sequential process of selecting documents one-by-one, and the locally optimal choice is made at each round. Unfortunately, this strategy suffers from the following shortcomings: (1) Such a one-by-one selection process is rather time-consuming for both training and inference. (2) It works well on the premise that the preceding choices are optimal or close to the optimal solution. (3) The mismatch between the objective function used in training and the final evaluation measure used in testing has not been taken into account. We propose a novel framework through direct metric optimization for SRD (referred to as MO4SRD) based on the score-and-sort strategy. Specifically, we represent the diversity score of each document that determines its rank position based on a probability distribution. These distributions over scores naturally give rise to expectations over rank positions. Armed with this advantage, we can get the differentiable variants of the widely used diversity metrics. Thanks to this, we are able to directly optimize the evaluation measure used in testing. Moreover, we have devised a novel probabilistic neural scoring function. It jointly scores candidate documents by taking into account both cross-document interaction and permutation equivariance, which makes it possible to generate a diversified ranking via a simple sorting. The experimental results on benchmark collections show that the proposed method achieves significantly improved performance over the state-of-the-art results.

Constructing a mobile visual search framework for Dunhuang murals based on fine-tuned CNN and ontology semantic distance

PurposeDunhuang murals are rich in cultural and artistic value. The purpose of this paper is to construct a novel mobile visual search (MVS) framework for Dunhuang murals, enabling users to efficiently search for similar, relevant and diversified images.Design/methodology/approachThe convolutional neural network (CNN) model is fine-tuned in the data set of Dunhuang murals. Image features are extracted through the fine-tuned CNN model, and the similarities between different candidate images and the query image are calculated by the dot product. Then, the candidate images are sorted by similarity, and semantic labels are extracted from the most similar image. Ontology semantic distance (OSD) is proposed to match relevant images using semantic labels. Furthermore, the improved DivScore is introduced to diversify search results.FindingsThe results illustrate that the fine-tuned ResNet152 is the best choice to search for similar images at the visual feature level, and OSD is the effective method to search for the relevant images at the semantic level. After re-ranking based on DivScore, the diversification of search results is improved.Originality/valueThis study collects and builds the Dunhuang mural data set and proposes an effective MVS framework for Dunhuang murals to protect and inherit Dunhuang cultural heritage. Similar, relevant and diversified Dunhuang murals are searched to meet different demands.

Assessing Viewpoint Diversity in Search Results Using Ranking Fairness Metrics

The way pages are ranked in search results influences whether the users of search engines are exposed to more homogeneous, or rather to more diverse viewpoints. However, this viewpoint diversity is not trivial to assess. In this paper, we use existing and novel ranking fairness metrics to evaluate viewpoint diversity in search result rankings. We conduct a controlled simulation study that shows how ranking fairness metrics can be used for viewpoint diversity, how their outcome should be interpreted, and which metric is most suitable depending on the situation. This paper lays out important groundwork for future research to measure and assess viewpoint diversity in real search result rankings.

Hypergraph-based image search reranking with elastic net regularized regression

Image search reranking is emerging as an effective technique to refine the text-based image search results using visual information. In this paper, we introduce a novel hypergraph-based image search reranking method that accounts for both relevance and diversity of search results. Namely, the text-based image search results are taken as vertices in a probabilistic regression-based hypergraph model and reranking is formulated as a hypergraph ranking problem with absorbing nodes. More specifically, to discover related samples and characterize the relationships among them, we bring the Elastic Net regularized regression model into the hypergraph construction. Exceeding the conventional hypergraph construction schemes, our scheme is able to describe the high-order relationships and the local manifold structure among visual samples while ensuring the datum-adaptiveness. Afterward, we apply a hypergraph-based ranking with absorbing nodes to ensure a diversified reranking. That is, during the reranking process, previously-ranked samples are transformed into absorbing nodes at each iteration, thereby redundant ones are prevented from receiving high ranking scores. Extensive experiments on real-world data from Flickr suggest our proposed reranking method achieves promising results compared to existing reranking methods.

OntoBestFit: A Best-Fit Occurrence Estimation strategy for RDF driven faceted semantic search

As Web 2.0 is transforming into a more systematized Semantic Web, there is an urgent need for semantic compliant techniques for web search. In this paper, the OntoBestFit has been proposed which is an RDF driven approach for minimizing ambiguity in search results and increasing the diversity of results, thereby solving both the context irrelevance and the serendipity problem. OntoBestFit focuses on harvesting RDF from standardized industry scale Semantic Wikis and transforming it into an intermediate dyadic structure, which makes it suitable for heterogeneous real-world domains. The approach focuses on deriving an RDF prioritization vector from a Term-Frequency Matrix and a Term co-occurrence Matrix formulated from the reduced dyadic RDF entities over a corpus of web pages to yield Query Indicator terms. The incorporation of dynamic query expansion by generating query facets from the Domain Ontologies and Query Indicator terms using the best-fit occurrence estimation algorithm has made this approach novel. The best-fit occurrence estimation algorithm is based on the adaptation of Simpson’s Diversity Index to compute the reduced and highly appropriate semantically similar domain ontologies based on domain richness computation, to increase the diversity in search results. Also, an Enriched Adaptive Pointwise Mutual Information measure has been proposed to compute the semantic similarity. OntoBestFit yields an average accuracy of 94.91% with a very low False Discovery Rate of 0.07, with a response time of 0.28 ms which is the best in class semantic search strategy in the era of Semantic Web.

A supervised term ranking model for diversity enhanced biomedical information retrieval

BackgroundThe number of biomedical research articles have increased exponentially with the advancement of biomedicine in recent years. These articles have thus brought a great difficulty in obtaining the needed information of researchers. Information retrieval technologies seek to tackle the problem. However, information needs cannot be completely satisfied by directly introducing the existing information retrieval techniques. Therefore, biomedical information retrieval not only focuses on the relevance of search results, but also aims to promote the completeness of the results, which is referred as the diversity-oriented retrieval.ResultsWe address the diversity-oriented biomedical retrieval task using a supervised term ranking model. The model is learned through a supervised query expansion process for term refinement. Based on the model, the most relevant and diversified terms are selected to enrich the original query. The expanded query is then fed into a second retrieval to improve the relevance and diversity of search results. To this end, we propose three diversity-oriented optimization strategies in our model, including the diversified term labeling strategy, the biomedical resource-based term features and a diversity-oriented group sampling learning method. Experimental results on TREC Genomics collections demonstrate the effectiveness of the proposed model in improving the relevance and the diversity of search results.ConclusionsThe proposed three strategies jointly contribute to the improvement of biomedical retrieval performance. Our model yields more relevant and diversified results than the state-of-the-art baseline models. Moreover, our method provides a general framework for improving biomedical retrieval performance, and can be used as the basis for future work.

Toward creating a fairer ranking in search engine results

With the increasing popularity and social influence of search engines in IR, various studies have raised concerns on the presence of bias in search engines and the social responsibilities of IR systems. As an essential component of search engine, ranking is a crucial mechanism in presenting the search results or recommending items in a fair fashion. In this article, we focus on the top-k diversity fairness ranking in terms of statistical parity fairness and disparate impact fairness. The former fairness definition provides a balanced overview of search results where the number of documents from different groups are equal; The latter enables a realistic overview where the proportion of documents from different groups reflect the overall proportion. Using 100 queries and top 100 results per query from Google as the data, we first demonstrate how topical diversity bias is present in the top web search results. Then, with our proposed entropy-based metrics for measuring the degree of bias, we reveal that the top search results are unbalanced and disproportionate to their overall diversity distribution. We explore several fairness ranking strategies to investigate the relationship between fairness, diversity, novelty and relevance. Our experimental results show that using a variant of fair ε-greedy strategy, we could bring more fairness and enhance diversity in search results without a cost of relevance. In fact, we can improve the relevance and diversity by introducing the diversity fairness. Additional experiments with TREC datasets containing 50 queries demonstrate the robustness of our proposed strategies and our findings on the impact of fairness. We present a series of correlation analysis on the amount of fairness and diversity, showing that statistical parity fairness highly correlates with diversity while disparate impact fairness does not. This provides clear and tangible implications for future works where one would want to balance fairness, diversity and relevance in search results.

The impact of result diversification on search behaviour and performance

Result diversification aims to provide searchers with a broader view of a given topic while attempting to maximise the chances of retrieving relevant material. Diversifying results also aims to reduce search bias by increasing the coverage over different aspects of the topic. As such, searchers should learn more about the given topic in general. Despite diversification algorithms being introduced over two decades ago, little research has explicitly examined their impact on search behaviour and performance in the context of Interactive Information Retrieval (IIR). In this paper, we explore the impact of diversification when searchers undertake complex search tasks that require learning about different aspects of a topic (aspectual retrieval). We hypothesise that by diversifying search results, searchers will be exposed to a greater number of aspects. In turn, this will maximise their coverage of the topic (and thus reduce possible search bias). As a consequence, diversification should lead to performance benefits, regardless of the task, but how does diversification affect search behaviours and search satisfaction? Based on Information Foraging Theory (IFT), we infer two hypotheses regarding search behaviours due to diversification, namely that (i) it will lead to searchers examining fewer documents per query, and (ii) it will also mean searchers will issue more queries overall. To this end, we performed a within-subjects user study using the TREC AQUAINT collection with 51 participants, examining the differences in search performance and behaviour when using (i) a non-diversified system (BM25) versus (ii) a diversified system (BM25 + xQuAD) when the search task is either (a) ad-hoc or (b) aspectual. Our results show a number of notable findings in terms of search behaviour: participants on the diversified system issued more queries and examined fewer documents per query when performing the aspectual search task. Furthermore, we showed that when using the diversified system, participants were: more successful in marking relevant documents, and obtained a greater awareness of the topics (i.e. identified relevant documents containing more novel aspects). These findings show that search behaviour is influenced by diversification and task complexity. They also motivate further research into complex search tasks such as aspectual retrieval—and how diversity can play an important role in improving the search experience, by providing greater coverage of a topic and mitigating potential bias in search results.

Extracting Multiple Visual Senses for Web Learning

Labeled image datasets have played a critical role in high-level image understanding. However, the process of manual labeling is both time consuming and labor intensive. To reduce the dependence on manually labeled data, there have been increasing research efforts on learning visual classifiers by directly exploiting web images. One issue that limits their performance is the problem of polysemy. Existing unsupervised approaches attempt to reduce the influence of visual polysemy by filtering out irrelevant images, but do not directly address polysemy. To this end, in this paper, we present a multimodal framework that solves the problem of polysemy by allowing sense-specific diversity in search results. Specifically, we first discover a list of possible semantic senses from untagged corpora to retrieve sense-specific images. Then, we merge visual similar semantic senses and prune noise by using the retrieved images. Finally, we train one visual classifier for each selected semantic sense and use the learned sense-specific classifiers to distinguish multiple visual senses. Extensive experiments on classifying images into sense-specific categories and reranking search results demonstrate the superiority of our proposed approach.

Search result diversification on attributed networks via nonnegative matrix factorization

Search result diversification is an effective way to tackle query ambiguity and enhance result novelty. In the context of large information networks, diversifying search result is also critical for further design of applications such as link prediction and citation recommendation. In previous work, this problem has mainly been tackled in a way of implicit query intent. To further enhance the performance on attributed networks, we propose a novel search result diversification approach via nonnegative matrix factorization. Our approach encodes latent query intents as well as nodes as representation vectors by a novel nonnegative matrix factorization model, and the diversity of the results accounts for the query relevance and the novelty w.r.t. these vectors. To learn the representation vectors of nodes, we derive the multiplicative updating rules to train the nonnegative matrix factorization model. We perform a comprehensive evaluation on our approach with various baselines. The results show the effectiveness of our proposed solution, and verify that attributes do help improve diversification performance.

Discovering and Distinguishing Multiple Visual Senses for Polysemous Words

To reduce the dependence on labeled data, there have been increasing research efforts on learning visual classifiers by exploiting web images. One issue that limits their performance is the problem of polysemy. To solve this problem, in this work, we present a novel framework that solves the problem of polysemy by allowing sense-specific diversity in search results. Specifically, we first discover a list of possible semantic senses to retrieve sense-specific images. Then we merge visual similar semantic senses and prune noises by using the retrieved images. Finally, we train a visual classifier for each selected semantic sense and use the learned sense-specific classifiers to distinguish multiple visual senses. Extensive experiments on classifying images into sense-specific categories and re-ranking search results demonstrate the superiority of our proposed approach.

Revisiting the cluster-based paradigm for implicit search result diversification

To cope with ambiguous and/or underspecified queries, search result diversification (SRD) is a key technique that has attracted a lot of attention. This paper focuses on implicit SRD, where the subtopics underlying a query are unknown. Many existing methods appeal to the greedy strategy for generating diversified results. A common practice is using a heuristic criterion for making the locally optimal choice at each round. As a result, it is difficult to know whether the failures are caused by the optimization criterion or the setting of parameters. Different from previous studies, we formulate implicit SRD as a process of selecting and ranking k exemplar documents through integer linear programming (ILP). The key idea is that: for a specific query, we expect to maximize the overall relevance of the k exemplar documents. Meanwhile, we wish to maximize the representativeness of the selected exemplar documents with respect to the non-selected documents. Intuitively, if the selected exemplar documents concisely represent the entire set of documents, the novelty and diversity will naturally arise. Moreover, we propose two approaches ILP4ID (Integer Linear Programming for Implicit SRD) and AP4ID (Affinity Propagation for Implicit SRD) for solving the proposed formulation of implicit SRD. In particular, ILP4ID appeals to the strategy of bound-and-branch and is able to obtain the optimal solution. AP4ID being an approximate method transforms the target problem as a maximum-a-posteriori inference problem, and the message passing algorithm is adopted to find the solution. Furthermore, we investigate the differences and connections between the proposed models and prior models by casting them as different variants of the cluster-based paradigm for implicit SRD. To validate the effectiveness and efficiency of the proposed approaches, we conduct a series of experiments on four benchmark TREC diversity collections. The experimental results demonstrate that: (1) The proposed methods, especially ILP4ID, can achieve substantially improved performance over the state-of-the-art unsupervised methods for implicit SRD. (2) The initial runs, the number of input documents, query types, the ways of computing document similarity, the pre-defined cluster number and the optimization algorithm significantly affect the performance of diversification models. Careful examinations of these factors are highly recommended in the development of implicit SRD methods. Based on the in-depth study of different types of methods for implicit SRD, we provide additional insight into the cluster-based paradigm for implicit SRD. In particular, how the methods relying on greedy strategies impact the performance of implicit SRD, and how a particular diversification model should be fine-tuned.

Fusion-based methods for result diversification in web search

Search result diversification of text documents is especially necessary when a user issues a faceted or ambiguous query to the search engine. A variety of approaches have been proposed to deal with this issue in recent years. In this article, we propose a group of fusion-based result diversification methods with the aim to improve performance that considers both relevance and diversity. They are linear combinations of scores that are obtained from different component search systems. The weight of each search system is determined by considering three factors: performance, dissimilarity, and complementarity. There are two major contributions. Firstly, we find that all the three factors of performance and complementarity and dissimilarity are useful for effective weighting of linear combination. Secondly, we present the logarithmic function-based model for converting ranking information into scores. Experiments are carried out with four groups of results submitted to the TREC web diversity task. Experimental results show that some of the fusion methods that use the aforementioned techniques perform more effectively than the state-of-the-art fusion methods for result diversification.

A novel local search for unicost set covering problem using hyperedge configuration checking and weight diversity

The unicost version of well-known set covering problem (SCP) is central to a wide variety of practical applications for which finding an optimal solution quickly is crucial. In this paper, we propose a new local searchbased algorithm for the unicost SCP which follows the general framework of the popular stochastic local search with a particular focus on the hyperedge selection strategy and weight diversity strategy. Specifically, a strategy as called hyperedge configuration checking strategy is proposed here to avoid the search trajectory which leads to local optima. Additionally, a new weight diversity strategy is proposed for the diversification of search results, by changing the weight of both covered and uncovered vertices in the current solution. The experimental results show that our algorithm significantly outperforms the state-of-the-art heuristic algorithm by one to two orders of magnitudes on the 85 classical instances. Also, our algorithm improves the current optimal solutions of 11 instances.

Efficient Structured Learning for Personalized Diversification

This paper is concerned with the problem of personalized diversification of search results, with the goal of enhancing the performance of both plain diversification and plain personalization algorithms. In previous work, the problem has mainly been tackled by means of unsupervised learning. To further enhance the performance, we propose a supervised learning strategy. Specifically, we set up a structured learning framework for conducting supervised personalized diversification, in which we add features extracted directly from tokens of documents and those utilized by unsupervised personalized diversification algorithms, and, importantly, those generated from our proposed user-interest latent Dirichlet topic model. We also define two constraints in our structured learning framework to ensure that search results are both diversified and consistent with a user's interest. To further boost the efficiency of training, we propose a fast training framework for our proposed method by adding additional multiple highly violated but also diversified constraints at every training iteration of the cutting-plane algorithm. We conduct experiments on an open dataset and find that our supervised learning strategy outperforms unsupervised personalized diversification methods as well as other plain personalization and plain diversification methods. Our fast training framework significantly saves training time while it maintains almost the same performance.

A Mean-Variance Analysis Based Approach for Search Result Diversification in Federated Search

Resource Selection is an important step in a federated search environment. The goal of this work was to improve the collection selection process by selecting collections in terms of relevance and diversity, to best answer a user's query. Sampled documents from the Central Sample Database are first ranked by Indri retrieval algorithm and later re-ranked by a Mean-Standard deviation method that reduces uncertainty and improves diversity of collection sources. A comparative evaluation with the R-based diversification metrics shows that the proposed method significantly outperforms the baseline diversification methods; ReDDE+MMR, ReDDE+MAP-IA and state-of-the-art resource selection methods (ReDDE and CORI) in all metrics.

Search Result Diversification Based on Query Facets

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.

Exploiting visual saliency for increasing diversity of image retrieval results

Diversification of search results allows for better and faster search, gaining knowledge about different perspectives and viewpoints on retrieved information sources. Recently various methods for diversification of image retrieval results have been proposed, mainly using textual information or techniques imported from the natural language processing domain. However, images contain much more information than their textual descriptions and the use of visual features deserves special attention in this context. Visual saliency provides information about parts of the image perceived as most important, which are instinctively targeted by humans when shooting a photo or looking at a picture. For this reason we propose to exploit such information to improve diversification of search results. To this purpose, we introduce a saliency-based method to re-rank the results of a query and we show that it can achieve significantly better performances as compared to the baseline approach. Experimental validation conducted on a number of queries applied to various datasets demonstrates the potential of the use of saliency information for the diversification of image retrieval results.

Explicit search result diversification using score and rank aggregation methods

Search result diversification is one of the key techniques to cope with the ambiguous and underspecified information needs of web users. In the last few years, strategies that are based on the explicit knowledge of query aspects emerged as highly effective ways of diversifying search results. Our contributions in this article are two‐fold. First, we extensively evaluate the performance of a state‐of‐the‐art explicit diversification strategy and pin‐point its potential weaknesses. We propose basic yet novel optimizations to remedy these weaknesses and boost the performance of this algorithm. As a second contribution, inspired by the success of the current diversification strategies that exploit the relevance of the candidate documents to individual query aspects, we cast the diversification problem into the problem of ranking aggregation. To this end, we propose to materialize the re‐rankings of the candidate documents for each query aspect and then merge these rankings by adapting the score(‐based) and rank(‐based) aggregation methods. Our extensive experimental evaluations show that certain ranking aggregation methods are superior to existing explicit diversification strategies in terms of diversification effectiveness. Furthermore, these ranking aggregation methods have lower computational complexity than the state‐of‐the‐art diversification strategies.