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Found 18 papers, 12 papers with code
Diversity-Aware $k$-Maximum Inner Product Search Revisited
To bridge this gap, we revisit and refine the diversity-aware $k$MIPS (D$k$MIPS) problem by incorporating two well-known diversity objectives -- minimizing the average and maximum pairwise item similarities within the results -- into the original relevance objective.
WaZI: A Learned and Workload-aware Z-Index
Specifically, we first formulate a cost function to measure the performance of a Z-index on a dataset for a range-query workload.
Max-Min Diversification with Fairness Constraints: Exact and Approximation Algorithms
Diversity maximization aims to select a diverse and representative subset of items from a large dataset.
SAH: Shifting-aware Asymmetric Hashing for Reverse $k$-Maximum Inner Product Search
To speed up the Maximum Inner Product Search (MIPS) on item vectors, we design a shifting-invariant asymmetric transformation and develop a novel sublinear-time Shifting-Aware Asymmetric Locality Sensitive Hashing (SA-ALSH) scheme.
Graph Summarization via Node Grouping: A Spectral Algorithm
By initially allowing relaxed (fractional) solutions for integer maximization, we analytically expose the underlying connections to the spectral properties of the adjacency matrix.
Balancing Utility and Fairness in Submodular Maximization (Technical Report)
Submodular function maximization is a fundamental combinatorial optimization problem with plenty of applications -- including data summarization, influence maximization, and recommendation.
Streaming Algorithms for Diversity Maximization with Fairness Constraints
Given a set $X$ of $n$ elements, it asks to select a subset $S$ of $k \ll n$ elements with maximum \emph{diversity}, as quantified by the dissimilarities among the elements in $S$.
StoryBuddy: A Human-AI Collaborative Chatbot for Parent-Child Interactive Storytelling with Flexible Parental Involvement
Despite its benefits for children's skill development and parent-child bonding, many parents do not often engage in interactive storytelling by having story-related dialogues with their child due to limited availability or challenges in coming up with appropriate questions.
Rewiring What-to-Watch-Next Recommendations to Reduce Radicalization Pathways
Hence, we define the problem of reducing the prevalence of radicalization pathways by selecting a small number of edges to "rewire", so to minimize the maximum of segregation scores among all radicalized nodes, while maintaining the relevance of the recommendations.
Blindfolded Attackers Still Threatening: Strict Black-Box Adversarial Attacks on Graphs
To bridge the gap between theoretical graph attacks and real-world scenarios, in this work, we propose a novel and more realistic setting: strict black-box graph attack, in which the attacker has no knowledge about the victim model at all and is not allowed to send any queries.
Fair and Representative Subset Selection from Data Streams
We study the problem of extracting a small subset of representative items from a large data stream.
Efficient Sampling Algorithms for Approximate Temporal Motif Counting (Extended Version)
We first propose a generic edge sampling (ES) algorithm for estimating the number of instances of any temporal motif.
GRMR: Generalized Regret-Minimizing Representatives
Extracting a small subset of representative tuples from a large database is an important task in multi-criteria decision making.
Data Structures and Algorithms Databases
A Fully Dynamic Algorithm for k-Regret Minimizing Sets
Selecting a small set of representatives from a large database is important in many applications such as multi-criteria decision making, web search, and recommendation.
Databases Data Structures and Algorithms
Coresets for Minimum Enclosing Balls over Sliding Windows
This paper investigates the problem of maintaining a coreset to preserve the minimum enclosing ball (MEB) for a sliding window of points that are continuously updated in a data stream.
Efficient Representative Subset Selection over Sliding Windows
By keeping much fewer checkpoints, KW$^{+}$ achieves higher efficiency than KW while still guaranteeing a $\frac{1-\varepsilon'}{2+2d}$-approximate solution for SMDK.
Real-Time Influence Maximization on Dynamic Social Streams
Influence maximization (IM), which selects a set of $k$ users (called seeds) to maximize the influence spread over a social network, is a fundamental problem in a wide range of applications such as viral marketing and network monitoring.
Social and Information Networks Data Structures and Algorithms
