Preferred Model Research Articles

Personalized Recommendation Systems Powered By Large Language Models: Integrating Semantic Understanding and User Preferences

This study proposes a novel personalized recommendation system leveraging Large Language Models (LLMs) to integrate semantic understanding with user preferences [1]. The system addresses critical challenges in traditional recommendation approaches by harnessing LLMs' advanced natural language processing capabilities. We introduce a framework combining a fine-tuned Roberta semantic analysis model with a multi-modal user preference extraction mechanism.The LLM component undergoes domain adaptation using Masked Language Modeling on a corpus of 112,000 user reviews from the MyAnimeList dataset, followed by task-specific fine-tuning using contrastive learning. User preferences are modeled through a weighted combination of explicit ratings, review sentiment, and implicit feedback, incorporating temporal dynamics through a time-decay function. Experimental results demonstrate significant improvements over state-of-the-art baselines, including Matrix Factorization, Neural Collaborative Filtering, BERT4Rec, and LightGCN. Our LLM-powered system achieves an 8.6%increase in NDCG@10 and a 10.5% improvement in Mean Reciprocal Rank compared to the best-performing baseline. Ablation studies reveal the synergistic effect of integrating LLM-based semantic understanding with user preference modeling. Case studies highlight the system's ability to recommend long-tail items and provide cross-genre suggestions, showcasing its capacity for nuanced content understanding. Scalability analysis indicates that while the LLM-based approach has higher initial computational costs, its performance scales comparably to other deep learning models for larger datasets. This research contributes to the field by demonstrating the effectiveness of LLMs in enhancing recommendation accuracy and diversity. Future work will explore advanced LLM architectures, multi-modal data integration, and techniques to improve computational efficiency and interpretability of recommendations.

The role of shared decision-making in enhancing patient experience: Insights from a cohort of curative head and neck cancer patients

IntroductionShared decision-making (SDM) is on the NHS policy agenda, and the preferred model for preference-sensitive decisions. This study establishes baseline patient-perceived SDM in a radical head and neck cohort, and explores patients’ views on SDM in a large, specialist trust. MethodsAn SDM questionnaire was distributed to all radical head and neck radiotherapy patients (N = 165), June–December 2023. This combined a well-validated instrument for measuring SDM from the patient perspective, SDM-Q-9, with additional questions exploring patient views. Thematic analysis was used to construct and interpret themes. Results65/165 (39%) questionnaires were returned. SDM-Q-9 mean standardised score was 78.6 (SD 26.3). There was a moderate ceiling effect (26%). Scores were not sensitive to sex (p = 0.64) or age (ρ = 0.1). Higher levels of SDM were perceived by participants who stated SDM was very important (51/65, 79%) than somewhat or not at all important (82.4 vs. 62.7; p = 0.02; Cohen d = 0.75). Individuals who discussed their personal priorities with the clinician (46/65, 70.8%), were more likely to be very satisfied with their involvement in SDM (89.1% vs. 52.9%). Thematic analysis generated three themes: Control, Desire for Transparency and Understanding, and Doctor as the Expert. ConclusionPatient-perceived SDM levels are high for head and neck patients. Participants who value SDM also perceive higher levels of SDM. Patient satisfaction increases when individuals discuss their personal priorities. The modest response rate and self-selection bias affect the generalisability of the results. Only radiotherapy patients were included; those who chose alternative treatment may perceive different levels of SDM. The moderate ceiling effect may limit the use of SDM-Q-9 to measure impact of future interventions to improve SDM. Implications for practiceSDM-Q-9 should be combined with an objective, observer measure of SDM.

A NICER View of the Nearest and Brightest Millisecond Pulsar: PSR J0437–4715

We report Bayesian inference of the mass, radius, and hot X-ray emitting region properties—using data from the Neutron Star Interior Composition ExploreR (NICER)—for the brightest rotation-powered millisecond X-ray pulsar, PSR J0437−4715. Our modeling is conditional on informative tight priors on mass, distance, and binary inclination obtained from radio pulsar timing using the Parkes Pulsar Timing Array (PPTA; Reardon et al.), and we use NICER background models to constrain the nonsource background, cross-checking with data from XMM-Newton. We assume two distinct hot emitting regions and various parameterized hot region geometries that are defined in terms of overlapping circles; while simplified, these capture many of the possibilities suggested by detailed modeling of return current heating. For the preferred model identified by our analysis, we infer a mass of M = 1.418 ± 0.037 M ⊙ (largely informed by the PPTA mass prior) and an equatorial radius of R=11.36−0.63+0.95 km, each reported as the posterior credible interval bounded by the 16% and 84% quantiles. This radius favors softer dense matter equations of state and is highly consistent with constraints derived from gravitational wave measurements of neutron star binary mergers. The hot regions are inferred to be nonantipodal and hence inconsistent with a pure centered dipole magnetic field.

Utilization of Liquid Organic Fertilizer from Cow and Chicken Manure, Indigofera on Hydroponic Lettuce, and Consumer Perception

Lettuce (Lactuca sativa L.) is a horticultural plant with high nutritional and economic value and good development prospects in Indonesia. However, the high costs of hydroponic fertilizers drive research to find more economical alternatives, such as substitution with Liquid Organic Fertilizer (LOF). This study aims to analyze consumer perceptions of hydroponic lettuce using LOF substitution and understand consumer responses and purchasing behavior toward these products to identify the attributes influencing purchasing decisions for hydroponic lettuce with LOF substitution. The study employs a survey method with purposive sampling, involving 35 regular customers and 15 supermarket shoppers from PT ABA to collect primary data through questionnaires regarding consumer interest in hydroponic lettuce with LOF substitution. Additionally, proximate analysis and nutrient content testing are conducted in the laboratory. Data analysis uses conjoint and discriminant analysis with SPSS Statistics 24 to evaluate consumer responses and the most preferred product model based on nutritional attributes, price, and weight. The study successfully analyzes consumer perceptions of hydroponic lettuce with LOF substitution and finds that consumers have a positive view of this product, mainly due to its better nutritional content and the plant's ability to absorb nutrients optimally. These factors significantly influence purchasing decisions, with the main attributes being price, weight, and nutritional content.

Student judges’ criteria for evaluating classmate pre-service chemistry teachers’ arguments developed in a remote simulated jury activity

ABSTRACT Although research has studied argumentation in science learning, it is necessary to understand the criteria teachers and students apply to evaluate arguments in the context of classroom production. Researchers have employed Toulmin's argument pattern as the preferred model to analyze arguments. However, the model is unfeasible for people to evaluate arguments while arguing or immediately after the discussion. This study explored the criteria two student judges applied to evaluate classmate pre-service chemistry teachers' arguments developed in a remote simulated jury. We applied a multi-level method for discourse analysis, affording key findings: (1) Student judges developed a symmetrical shared-built explanation, an unprecedented finding as discourse analysts consider asymmetry an inherent feature of explanations; (2) They applied diverse criteria to evaluate the arguments; (3) Evaluation activity was accomplished through interactive-explanation-related discursive procedures, such as informing about issues related to the evaluations; asking for confirmation and clarification; presenting arguments developed and silenced in the simulated jury. Implications include a set of exemplary criteria for teachers to evaluate students' arguments and instruct them on how to evaluate arguments. In conclusion, we discuss the relevance of the findings and the multi-level method for the science education research community, providing new ways to analyze instructional discourse.

The role of heterodimers formed by histamine H3 receptors and dopamine D1 receptors on the methamphetamine-induced conditioned place preference

RationaleThe rewarding effect of Methamphetamine (METH) is commonly believed to play an important role in METH use disorder. The altered expression of dopamine D1 receptor (D1R) has been suggested to be essential to the rewarding effect of METH. Notably, D1R could interact with histamine H3 receptors (H3R) by forming a H3R-D1R heteromer (H3R-D1R). ObjectivesThis study was designed to specifically investigate the involvement of H3R-D1R in the rewarding effect of METH. MethodsC57BL/6 mice were treated with intraperitoneal injections of a selective H3R antagonist (Thioperamide, THIO; 20 mg/kg), an H1R antagonist (Pyrilamine, PYRI; 10 mg/kg), or microinjections of cytomegalovirus (CMV)-transmembrane domain 5 (TM5) into the nucleus accumbens (NAc). The animal model of Conditioned Place Preference (CPP) was applied to determine the impact of H3R-D1R on the rewarding effect of METH. ResultsMETH resulted in a significant preference for the drug-associated chamber, in conjunction with increased H3R and decreased D1R expression in both NAc and the ventral tegmental area (VTA). THIO significantly attenuated the rewarding effect of METH, accompanied by decreased H3R and increased D1R expression. In contrast, pyrilamine failed to produce the similar effects. Moreover, the inhibitory effect of THIO on METH-induced CPP was reversed by SKF38393, a D1R agonist. Furthermore, SCH23390, a D1R antagonist, counteracted the ameliorative effect of SKF38393 on THIO. Co-immunoprecipitation (CO-IP) experiments further demonstrated the specific interaction between H3R and D1R in METH CPP mice. The rewarding effect of METH was also significantly blocked by the interruption of CMV-transmembrane domain 5 (TM5), but not CMV-transmembrane domain 7 (TM7) in NAc. ConclusionThese results suggest that modulating the activity of H3R-D1R complex holds promise for regulating METH use disorder and serves as a potential drug target for its treatment.

Modelling green knowledge production and environmental policies with semiparametric panel data regression models

AbstractThis paper introduces a novel semiparametric econometric framework for policy evaluation and estimates a green knowledge production function for a large, 30-year panel dataset of high-income countries. Due to the substantial uncertainty in the data-generating process and the potential presence of nonlinearities and latent common factors, the paper explores semiparametric panel specifications that go beyond interactive fixed effects fully parametric models. The findings suggest that (i) the semiparametric additive specification with individual time trends is the preferred model, (ii) threshold effects and nonlinearities are salient features of the data that parametric specifications fail to capture, and (iii) the impact of environmental policy is noteworthy and exhibits clear heterogeneity when modelled as a nonparametric function of specific knowledge inputs. The evidence reveals a significant nonlinear policy inducement effect stemming from R&D investments.

A novel model for real-time risk evaluation of vehicle–pedestrian interactions at intersections

Safety decisions for vehicles at an intersection rely on real-time, objective and continuous assessment of risks in vehicle–pedestrian interactions. Existing surrogate safety models, constrained by ideal assumptions of constant current speed and reliant on interaction points, often misjudge risks, and show inefficiency, inaccuracy and discontinuity. This work proposes a novel model for evaluation of those risks in vehicle–pedestrian interactions at intersections, which abstracts the pedestrian distribution density around a vehicle into a generalized model of driver-pedestrian interaction preferences. The introduction of two conceptions: ’driving risk index’ and ’driving risk gradient,’ facilitates the delineation of driving spaces for identifying safety–critical events. By means of the trajectory data from three intersections, model parameters are calibrated and a multidimensional vehicle–pedestrian interaction risk (VPIR) model is proposed to adapt the complex and dynamic characteristics of vehicle–pedestrian interactions at intersections. Commonly used surrogate safety models, such as Time to Collision (TTC), are selected as benchmark models. Results show that the proposed model overcomes the limitations of the existing interaction-point-based models, and offers a ideal assessment of driving risks at intersections. Finally, the model is illustrated with a case study that assesses the risks in vehicle–pedestrian interactions in varied scenarios and the case study indicates that the VPIR model works well in evaluating vehicle–pedestrian interaction risks. This work can facilitate humanoid learning in the autonomous driving domain, and achieve an ideal evaluation of vehicle–pedestrian interaction risks for safe and efficient vehicle navigation through an intersection.

Subject Of Civil Liability For Tourist Services Containing Package Tour

The article is devoted to the issue of determining the appropriate subject of civil liability for the provision of tourist services included in package tours. The article aims to propose a preferable model of the subject of liability based on the analysis of the main models of the subject of responsibility for tourism services existing in world practice, as well as the peculiarities of contractual relations between subjects of tourism activities and suppliers of tourism services that arise regarding the formation of package tours. In order to eliminate the existing contradictory practice regarding the subject of liability in the conditions of various modes of interaction between subjects of tourism activities and suppliers of tourism services, the article proposes to stipulate a clear model of liability for services included in package tours, which will henceforth allow forming contractual relations in the field of tourism on the basis of this model. In particular, taking into account the fact that the tour operator forms package tours and, within the framework of the latter, separately and/or together with the travel agent, organizes the travel of the consumer, it is suggested to determine mechanism of liability of the subjects of tourism activities for the provision of these services- as more effective and most consistent with the essence of the contractual relations of suppliers and subjects of tourism activities, except from those tourism services, for which the law provides for the liability of the service provider.

Observational Constraints on the Parameters of Hořava–Lifshitz Gravity

AbstractThis study investigates the accelerated cosmic expansion within the Hořava–Lifshitz (HL) Model. To constrain the cosmological parameters of this model, 17 Baryon Acoustic Oscillation points, 31 Cosmic Chronometer points, 40 Type Ia Supernovae points, 24 quasar Hubble diagram points, and 162 Gamma Ray Bursts points, along with the latest Hubble constant measurement (R22) are incorporated. is treated as a free parameter to extract and using late‐time datasets, aiming for optimal fitting values in each model. Treating as free improves precision, reduces bias, and enhances dataset compatibility. The obtained values of and are compared to the model, showing consistency with previous estimates from Planck and SDSS studies. The Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) favor the Hořava–Lifshitz model, with the model having the lowest AIC. Additionally, and analyses are conducted to assess model preference. Validation using the reduced statistic indicates satisfactory fits for the Hořava–Lifshitz model, while recognizing as the preferred model. Extensions of the analysis warrant further investigation.

Modeling multi-factor user preferences based on Transformer for next point of interest recommendation

With the rapid development of the mobile network and the gradual popularization of mobile devices, more and more users try to find attractive places to visit through WeChat, Twitter applications. In this trend, personalized next point of interest(POI) recommendation in the Location-based Social Network (LBSN) has become the focus of research and practice. Most existing studies capture user interest changes between different days (i.e. weekend and weekday), however, they ignore seasonal factors in time transition and category factors and thus fail to capture seasonal-level and category-level movement patterns in users’ mobile trajectories. Besides, they neglect the relevance between POIs from all users’ trajectory and fail to generate expressive POI embedding representation without constructing trajectory graph, which will reduce the accuracy of the next POI recommendation. To address the issues above, a next POI recommendation method for modeling Multi-factor User Preferences based on Transformer (MUPT) is developed, which consists of a global POI relationship modeling, a local multi-factor user preference modeling and a prediction module. It first learns the collaborative information of users with similar behavior to generate expressive POI embedding representation. Then it captures the personalized movement patterns of users at the POI, category and time levels based on Transformer mechanism in the local module. Especially the seasonal and other fine-grained information on the time series are learned in the time preference modeling part. The prediction module designed tracks the relationship between multi-level motion pattern representation of user check-in behavior and the next POI accessed by the user, and it finally obtains user’s preference probability for next POIs. An extensive experiment has been conducted on four datasets, and the experimental results analysis demonstrates that our proposed MUPT method is superior to other methods in terms of accuracy(ACC), mean reciprocal rank(MRR) and normalized discounted cumulative gain(NDCG).

Trends and cycles during the COVID-19 pandemic period

We perform several trend-cycle decompositions through the lens of two unobserved components models, herein estimated for Portugal and the euro area. Our procedure copes with the COVID-19’s consequences by explicitly considering potentially larger second moments during that period. This is achieved through a set of pandemic-specific shocks affecting only the 2020–21 period and embedded into estimation through a piecewise linear Kalman filter. Our methodology generates negligible historical revisions in key smoothed variables when the sample period is expanded until 2021:4, since pandemic shocks absorb a great deal of data volatility with minimal impacts on filtered data revisions or estimated parameters. Furthermore, non-pandemic shock volatility remains largely unaffected by the pandemic period. Innovations affecting the cycle in our preferred model are the key propellers of GDP developments during the COVID-19 pandemic period.

Response of tomato fruit to consecutive impact loading

Tomato fruits receive successive impacts during harvest and postharvest operations. This paper is a study of the response of tomato fruit to mono and multiple dynamic loadings and the ability to withstand consecutive impacts, which play the most critical role in downgrading and postharvest loss of fresh tomato fruits and designing harvest, and postharvest handling and processing equipment. The fruits were subjected to consecutive impacts by an instrumented pendulum one to three times successive impacts at the same location with the three different impact energy levels: 125, 250, and 500 mJ to establish a comprehensive model and refining by adding various groups of contributing factors to understand better and find out which parameters are more likely to cause and contribute in tomato mechanical damage. Twenty sub-models were evaluated using AIC and R2 values. The parameters of the preferred logistic model consist of response variables (peak contact force, contact time, and Elast), loading conditions (one, two, and three-times impact at the same location), and fruit quality parameter (total soluble solids). Also, another model was suggested for rapid assessment of bruise development.

Continuous distribution pattern and the origin of the high‐velocity layer in the Pearl River Mouth Basin in the northern South China Sea: Constraints by gravity modelling

The high‐velocity layer in the lower crust is widely distributed in the northern continental margin of the South China Sea. A detailed anatomy of the high‐velocity layer is crucial for understanding the continental rifting and crustal thinning. Based on three seismic reflection profiles across the Pearl River Mouth Basin (PRMB) and the global free‐air gravity anomaly data in this study, by the gravity modelling we construct the crustal structure along three seismic reflection profiles across the depression and uplift zones. The free‐air gravity anomaly data within the uplift and depression zones indicates the distinct zonation, and the high and low values of free‐air gravity anomalies in the basin show the northeastward trend. Based on the gravity modelling along the three seismic profiles, the crustal thickness is of 12–23 km beneath the basin and thickness of the crust is gradually thinning from the continental shelf to the continental slope. The high‐velocity layer extends eastward to the Dongsha Uplift and terminates westward beneath the Baiyun Sag. The average thickness of the high‐velocity layer is 4–6 km and the maximum thickness is about 8 km at the Dongsha Uplift. The thickness variation of the high‐velocity layer suggests the continuous distribution of mantle underplating and intense magma activity. According to the isolated distribution and the continuous distribution of the high‐velocity layer found by previous studies in the northern South China Sea, we use gravity modelling to test which models of the high‐velocity layer within the PRMB are reasonable. According to testing models and preferred models of the crustal structure by gravity modelling, we propose that the continuous distribution pattern of the high‐velocity layer is the best model to interpret the crustal thinning characteristics and the gravity anomaly responses of the high‐velocity layer in the basin. We propose that the high‐velocity layer in the Pearl River Mouth Basin was formed by the long‐term underplating of the high‐temperature melted mantle.

Lithospheric electrical structure and geodynamic model of the red river fault zone and its adjacent areas in southwest China: Constraints from 3-D magnetotelluric imaging

The Red River Fault (RRF) zone is situated on the southeastern boundary of the Tibetan Plateau. To further explore the geodynamic model of the region and the collision mechanism of the Eurasian continent, the electrical structure model of the lithosphere in the RRF was obtained through 3-D inversion of MT data. The preferred model indicated that fault zones were primarily distinguished by low resistivity anomalies, with some exhibiting high resistivity characteristics. It was hypothesized that the low resistivity anomalies may be attributed to the presence of saline fluids or metal sulfide within the fault zones, on the other hand, the high resistivity characteristics suggested inactivation due to fault cooling. Rapid changes in the electrical structure occurred on both sides of the fault zones, causing the upper and middle crust to be divided into distinct secondary tectonic units. Based on the effective viscosity calculation, we analyzed the crust-mantle rheology of the RRF area. We assumed that the RRF area was composed of three geodynamic models: the “crustal flow” model was distributed from the northern part of the Lancang River Fault (LCRF) zones to the southern part of the RRF zones. The “ductile continuum rheology” model was distributed from the north of the RRF to the western part of the Xiaojiang Fault (XJF) zones. The “mantle upwelling” model was distributed in the southeast part of the RRF.

Unlocking visual data to enhance the accuracy of AI-enabled mass valuation of urban houses: An Australian city case study

Despite earlier praises of hedonic price models (HPMs) in predicting the prices of residential properties, stakeholders in the property market have raised concerns regarding inaccuracy in automated valuation models (AVMs) using conventional HPMs. Furthermore, despite significant advancements in integrating artificial intelligence and image data into AVMs, research in the Australian context is absent. This paper investigates how image data capturing visual features of properties could enhance valuation accuracy using the data of actual sales of 34,399 properties from 2018 to 2020 across 128 urban suburbs in Brisbane, the third largest city in Australia. We develop a convolutional neural network model to extract visual features from large-scale data of 320,000 street-view and aerial-view images. We develop fusion models to integrate these additional visual features in HPMs. Using several experimental designs, our preferred fusion models generate a reduction of 28.35% in root-mean-square errors. Less predictive errors of AI-enabled AVMs through the use of visual data would enhance business confidence for AVMs’ end-users for investment, reporting, risk management, tax estimation, and urban planning purposes.

Is the output growth rate in NIPA a welfare measure?

Abstract This paper shows that, in a dynamic general equilibrium framework, the rate of output growth as measured by national income and product accounts reflects changes in welfare in the precise sense of equivalent variation. The main argument is straightforward. In a two-sector dynamic general equilibrium model of heterogeneous households, recursive preferences and quasi-concave technology, the Bellman equation provides a representation of household preferences over current consumption and investment. When applied to this representation of preferences, a Fisher–Shell true quantity index turns out to be equal to the Divisia index, closely approximated by the Fisher ideal chain index used in national income and product accounts.

Explicitly Exploiting Implicit User and Item Relations in Graph Convolutional Network (GCN) for Recommendation

Most existing collaborative filtering-based recommender systems rely solely on available user–item interactions for user and item representation learning. Their performance often suffers significantly when interactions are sparse, as limited user and item interactions are insufficient for learning robust representations. To address this issue, recent research has explored additional information between users and items by leveraging the user–item bipartite graph. However, these methods have not fully exploited high-order neighborhood information, primarily using sampled interactions to enrich training data rather than integrating this information directly into representation learning. In this paper, we propose a novel model, EIR-GCN (Embedding Integration with Relational Graph Convolutional Network), which directly incorporates various types of collaborative relations, such as user–user and item–item interactions, into the embedding function for user preference modeling. Specifically, our model employs advanced graph convolutional network (GCN) techniques to integrate user–item, user–user, and item–item relations for comprehensive representation learning. EIR-GCN initially selects the most influential second-order neighbors from the user–item bipartite graph to form user–user and item–item connections. With these enriched connections, a message-passing method is adopted to learn node representations by aggregating messages from directly linked nodes, including first-order item neighbors and selected second-order user neighbors. Extensive experiments on several public datasets demonstrate that EIR-GCN outperforms strong baselines, including recent GCN-based models and those exploiting high-order information. Our results show that EIR-GCN achieves state-of-the-art performance and effectively addresses the sparsity issue, highlighting its robustness and efficacy in recommendation tasks.

Cosmologically consistent analysis of gravitational waves from hidden sectors

Production of gravitational waves in the early Universe is discussed in a cosmologically consistent analysis within a first-order phase transition involving a hidden sector feebly coupled with the visible sector. Each sector resides in its own heat bath leading to a potential dependent on two temperatures and on two fields: one a standard model Higgs field and the other a scalar arising from a hidden sector U(1) gauge theory. A synchronous evolution of the hidden and visible sector temperatures is carried out from the reheat temperature down to the electroweak scale. The hydrodynamics of two-field phase transitions, one for the visible and the other for the hidden is discussed, which leads to separate tunneling temperatures and different sound speeds for the two sectors. Gravitational waves emerging from the two sectors are computed and their imprint on the measured gravitational wave power spectrum vs frequency is analyzed in terms of bubble nucleation signature, i.e., detonation, deflagration, and hybrid. It is shown that the two-field model predicts gravitational waves accessible at several proposed gravitational wave detectors: LISA, DECIGO, BBO, and Taiji, and their discovery would probe specific regions of the hidden sector parameter space and may also shed light on the nature of bubble nucleation in the early Universe. The analysis presented here indicates that the cosmologically preferred models are those where the tunneling in the visible sector precedes the tunneling in the hidden sector and the sound speed cs lies below its maximum, i.e., cs2<13. It is of interest to investigate if these features are universal and applicable to a wider class of cosmologically consistent models. Published by the American Physical Society 2024

Slip Rate for the Rose Canyon Fault through San Diego, California, Based on Analysis of GPS Data: Evidence for a Potential Rose Canyon–San Miguel-Vallecitos Fault Connection?

ABSTRACTThe Rose Canyon fault is the southern extension of the larger Newport–Inglewood–Rose Canyon fault system, which represents a major structural boundary in the Inner Continental Borderland (ICB) offshore of southern California. Ten to fifteen percent of total plate boundary motion in southern California is thought to be accommodated by the faults of the ICB, but the exact distribution of slip is uncertain. With an onshore segment, the Rose Canyon fault offers an opportunity to measure the slip rate using traditional geodetic methods. In this study, we use Global Positioning System (GPS) surface velocities from a combined campaign and continuous GPS network to constrain elastic models of the Rose Canyon fault. We then compare the observed surface velocities with proposed conceptual models of regional fault connections that facilitate the transfer of slip into the Rose Canyon fault to assess how well the observations are explained by the models. The results of elastic half-space models suggest that the Rose Canyon fault may be slipping toward the higher end of geologic estimates, with the preferred model indicating a slip rate of 2.4 ± 0.5 mm/yr. Although limited in terms of near-fault benchmarks, we find an improved model fit using an asymmetrical elastic half-space model and a higher slip rate, suggesting a potential rheological contrast across the Rose Canyon fault, similar to observations from the northern Newport–Inglewood fault segments. Observed GPS surface velocities, background seismicity, and gravity anomalies south of San Diego Bay point toward a more easterly trace for the Rose Canyon fault, suggesting a possible connection with the San Miguel–Vallecitos fault system. Such a connection could increase the potential rupture lengths of future earthquakes and have important consequences for regional seismic hazards.