Evaluating platform-level interventions in search-based two-sided marketplaces is fundamentally challenged by systemic effects such as spillovers and network interference. While widely used for causal inference, the PSM (Propensity Score Matching) - DID (Difference-in-Differences) framework remains susceptible to selection bias and cross-unit interference from unaccounted spillovers. In this paper, we introduced Competitive Isolation PSM-DID, a novel causal framework that integrates propensity score matching with competitive isolation to enable platform-level effect measurement (e.g., order volume, GMV) instead of item-level metrics in search systems. Our approach provides theoretically guaranteed unbiased estimation under mutual exclusion conditions, with an open dataset released to support reproducible research on marketplace interference (github.com/xxxx). Extensive experiments demonstrate significant reductions in interference effects and estimation variance compared to baseline methods. Successful deployment in a large-scale marketplace confirms the framework's practical utility for platform-level causal inference.

Link prediction on knowledge graphs (KGs) has been extensively studied on binary relational KGs, wherein each fact is represented by a triple. A significant amount of important knowledge, however, is represented by hyper-relational facts where each fact is composed of a primal triple and a set of qualifiers comprising a key-value pair that allows for expressing more complicated semantics. Although some recent works have proposed to embed hyper-relational KGs, these methods fail to capture essential inference patterns of hyper-relational facts such as qualifier monotonicity, qualifier implication, and qualifier mutual exclusion, limiting their generalization capability. To unlock this, we present \emph{ShrinkE}, a geometric hyper-relational KG embedding method aiming to explicitly model these patterns. ShrinkE models the primal triple as a spatial-functional transformation from the head into a relation-specific box. Each qualifier ``shrinks'' the box to narrow down the possible answer set and, thus, realizes qualifier monotonicity. The spatial relationships between the qualifier boxes allow for modeling core inference patterns of qualifiers such as implication and mutual exclusion. Experimental results demonstrate ShrinkE's superiority on three benchmarks of hyper-relational KGs.

In this article, the reader and I will journey between two concurrent algorithms of the 1970s that are still studied today. The journey begins at the bakery algorithm9 and ends at an algorithm for implementing a distributed state machine.12 I hope we enjoy the voyage and perhaps even learn something. The bakery algorithm ensures processes execute a critical section of code one at a time. A process trying to execute that code chooses a number it believes to be higher than the numbers chosen by other such processes. The process with the lowest number goes first, with ties broken by process name. In the distributed state-machine algorithm, each process maintains a logical clock, with the clocks being synchronized by having a process include its clock value in the messages it sends. Commands to the state machine are ordered according to the value of a process's clock when it issues a command, with ties broken by process name. The similarity between the bakery algorithm's numbers and the state-machine algorithm's clocks has been noticed, but I know of no previous rigorous connection between them. Our trip makes this connection, going from the bakery algorithm to the state-machine algorithm through a sequence of algorithms, each (except the first) derived from the preceding one. The first algorithm on the journey is a straightforward generalization of the bakery algorithm, mainly by allowing a process to read other processes' numbers in an arbitrary order. We then deconstruct this algorithm by having each process maintain multiple copies of its number, one for each other process. Next is a distributed version of the deconstructed algorithm obtained by having each copy of a process i's number kept by the process that reads it, where i writes the value stored at another process by sending a message to that process.

The At-Most-One (AMO) constraint is a special case of cardinality constraint that requires at most one variable from a set of Boolean variables to be set to TRUE. AMO is important for modeling problems as Boolean satisfiability (SAT) from domains where decision variables represent spatial or temporal placements of some objects that cannot share the same spatial or temporal slot. The AMO constraint can be used for more efficient representation and problem solving in mutex networks consisting of pair-wise mutual exclusions forbidding pairs of Boolean variable to be simultaneously TRUE. An on-line method for automated detection of cliques for efficient representation of incremental mutex networks where new mutexes arrive using AMOs is presented. A comparison of SAT-based problem solving in mutex networks represented by AMO constraints using various encodings is shown.

Planning as satisfiability is a principal approach to planning with many eminent advantages. The existing planning as satisfiability techniques usually use encodings compiled from STRIPS. We introduce a novel SAT encoding scheme (SASE) based on the SAS+ formalism. The new scheme exploits the structural information in SAS+, resulting in an encoding that is both more compact and efficient for planning. We prove the correctness of the new encoding by establishing an isomorphism between the solution plans of SASE and that of STRIPS based encodings. We further analyze the transition variables newly introduced in SASE to explain why it accommodates modern SAT solving algorithms and improves performance. We give empirical statistical results to support our analysis. We also develop a number of techniques to further reduce the encoding size of SASE, and conduct experimental studies to show the strength of each individual technique. Finally, we report extensive experimental results to demonstrate significant improvements of SASE over the state-of-the-art STRIPS based encoding schemes in terms of both time and memory efficiency.

Mutual exclusions provide useful information for learn- ing classes of concepts. We designed MuSweeper as a MineSweeper-like game to collect mutual exclusions from web users. Using the mechanism of an exten- sive game with Imperfect information, our experiments showed MuSweeper to collect mutual exclusions with high precision and efficiency.

Information retrieval may suggest a document, and information extraction may tell us what it says, but which information sources do we trust and which assertions do we believe when different authors make conflicting claims? Trust algorithms known as fact-finders attempt to answer these questions, but consider only which source makes which claim, ignoring a wealth of background knowledge and contextual detail such as the uncertainty in the information extraction of claims from documents, attributes of the sources, the degree of similarity among claims, and the degree of certainty expressed by the sources. We introduce a new, generalized fact-finding framework able to incorporate this additional information into the fact-finding process. Experiments using several state-of-the-art fact-finding algorithms demonstrate that generalized fact-finders achieve significantly better performance than their original variants on both semi-synthetic and real-world problems.

Planning as satisfiability is a principal approach to planning with many eminent advantages. The existing planning as satisfiability techniques usually use encodings compiled from the STRIPS formalism. We introduce a novel SAT encoding scheme based on the SAS+ formalism. It exploits the structural information in the SAS+ formalism, resulting in more compact SAT instances and reducing the number of clauses by up to 50 fold. Our results show that this encoding scheme improves upon the STRIPS-based encoding, in terms of both time and memory efficiency.