An Evaluation of Diversification Techniques

Thang, Duong Chi; Tâm, Nguyễn Thành; Hùng, Nguyễn Quốc; Aberer, Karl

doi:10.1007/978-3-319-22852-5_19

Cited by 9 publications

(3 citation statements)

References 27 publications

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“…But on the other hand, it took the longest time for computation. Thang et al (2015) evaluated 6 implicit methods Swap, Motley, MMR, MSD, GrassHopper, and Affinity Graph. They observed that MMR was the best performer.…”

Section: Results Diversificationmentioning

confidence: 99%

Fusion-based methods for result diversification in web search

Huang

et al. 2019

Information Fusion

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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.

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Section: Results Diversificationmentioning

confidence: 99%

Fusion-based methods for result diversification in web search

Huang

et al. 2019

Information Fusion

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“…We also carried out a qualitative study to demonstrate the interpretability of our framework. In future work, we plan to consider further sequential patterns in the user check-in data, such as the geography distance, as well as incorporate diversification mechanisms [61]. Also, we would want to address the cold start or less active users issues, which is a common challenge in LBSN recommendation and social network mining [2], [62], [63].…”

Section: Discussionmentioning

confidence: 99%

Learning Holistic Interactions in LBSNs with High-order, Dynamic, and Multi-role Contexts

Huynh

Tong

Nguyên

et al. 2022

IEEE Trans. Knowl. Data Eng.

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Location-based social networks (LBSNs) have emerged over the past few years. Their exponential network effects depend on the fact that each user can share her daily digital footprints with different communities, in different places, and at different times (for example in the form of check-in activities). Unlike other types of social networks, activities in an LBSN can potentially be performed by several users in a collaborative way. Existing studies of representation learning for LBSNs often consider them as regular graphs and ignore these high-order, dynamic, and multi-role contexts, since their holistic interactions are quite difficult to capture. In this paper, we propose a model in which these holistic interactions can be learned and transferred into node embeddings derived from a hypergraph representation and a persona decomposition process. More specifically, the model learns from friendship edges, check-in hyperedges, and node personas at the same time, and devises multiple presentations for each user that reflects their multiple roles in a social context. The embedding learning process also exploits useful patterns such as user colocation and sequential effects through a carefully designed point-of-interest splitting step. Extensive experiments on real and synthetic datasets show that our model outperforms alternative state-of-the-art embedding methods on friendship and location prediction tasks by an average margin of 45.7% and 29.46%, respectively. We also demonstrate the robustness of our model against adversarial conditions such as structural noise, attribute noise, and hyperparameter sensitivity.

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“…Diverse counterfactuals equip users with a range of actionable insights to potentially alter their outcomes favorably [130,134,135,182]. However, this also increases privacy risks as it may give away additional details that could be exploited for more potent attacks [5].…”

Section: Privacy Leaks In Counterfactual Explanationsmentioning

confidence: 99%

From the Editors and Nguyen Tat Thanh University

Nguyen¹,

Nguyên²

2019

Vietnam J. Comp. Sci.

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Let H = n 1 , n 2 , n 3 be a numerical semigroup. Let H be the interval completion of H, namely the semigroup generated by the interval n 1 , n 1 +1, . . . , n 3 . Let K be a field and K[H] the semigroup ring generated by H. Let I * H be the defining ideal of the tangent cone of K[H]. In this paper, we describe the defining equations of I * H . From that, we establish the Herzog-Stamate conjecture for monomial space curves stating that β i (I * H ) ≤ β i (I * H ) for all i, where β i (I * H ) and β i (I * H ) are the ith Betti numbers of I * H and I * H respectively.

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An Evaluation of Diversification Techniques

Cited by 9 publications

References 27 publications

Fusion-based methods for result diversification in web search

Fusion-based methods for result diversification in web search

Learning Holistic Interactions in LBSNs with High-order, Dynamic, and Multi-role Contexts

From the Editors and Nguyen Tat Thanh University

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