Normalizing Item-Based Collaborative Filter Using Context-Aware Scaled Baseline Predictor

Item-based collaborative filter algorithms play an important role in modern commercial recommendation systems (RSs). To improve the recommendation performance, normalization is always used as a basic component for the predictor models. Among a lot of normalizing methods, subtracting the baseline predictor (BLP) is the most popular one. However, the BLP uses a statistical constant without considering the context. We found that slightly scaling the different components of the BLP separately could dramatically improve the performance. This paper proposed some normalization methods based on the scaled baseline predictors according to different context information. The experimental results show that using context-aware scaled baseline predictor for normalization indeed gets better recommendation performance, including RMSE, MAE, precision, recall, and nDCG

Revealing the real-world CO2 emission reduction of ridesplitting and its determinants based on machine learning

Ridesplitting, which is a form of pooled ridesourcing service, has great potential to alleviate the negative impacts of ridesourcing on the environment. However, most existing studies only explored its theoretical environmental benefits based on optimization models and simulations. To put into practice, this study aims to reveal the real-world emission reduction of ridesplitting and its determinants based on the observed data of ridesourcing in Chengdu, China. Integrating the trip data with the COPERT model, this study calculates the CO2 emissions of shared rides (ridesplitting) and their substituted single rides (regular ridesourcing) to estimate the CO2 emission reduction of each ridesplitting trip. The results show that not all ridesplitting trips reduce emissions from ridesourcing in the real world. The CO2 emission reduction rate of ridesplitting varies from trip to trip, averaging at 43.15g/km. Then, the interpretable machine learning models, gradient boosting machines, are applied to explore the relationship between the CO2 emission reduction rate of ridesplitting and its determinants. Based on the SHapley Additive exPlanations method, the overlap rate and detour rate of shared rides are identified to be the most important factors that determine the CO2 emission reduction rate of ridesplitting. Increasing the overlap rate, the number of shared rides, average speed, and ride distance ratio and decreasing the detour rate, actual trip distance, ride distance gap can increase the CO2 emission reduction rate of ridesplitting. In addition, nonlinear effects and interactions of several key factors are examined through the partial dependence plots. This study provides a scientific method for the government and ridesourcing companies to better assess and optimize the environmental benefits of ridesplitting.Comment: 33 pages, 12 figure

Multi-perspective neural architecture for recommendation system

Abstract(#br)Currently, there starts a research trend to leverage neural architecture for recommendation systems. Though several deep recommender models are proposed, most methods are too simple to characterize users’ complex preference. In this paper, for a fine-grained analysis, users’ ratings are explained from multiple perspectives, based on which, we propose our neural architectures. Specifically, our model employs several sequential stages to encode the user and item into hidden representations. In one stage, the user and item are represented from multiple perspectives and in each perspective, the representation of user and that of item put attentions to each other. Last, we metric the output representations from the final stage to approach the users’ ratings. Extensive experiments demonstrate that our method achieves substantial improvements against baselines

An Optimal Ride Sharing Recommendation Framework for Carpooling Services

Carpooling services allow drivers to share rides with other passengers. This helps in reducing the passengers’ fares and time, as well as traffic congestion and increases the income for drivers. In recent years, several carpooling based recommendation systems have been proposed. However, most of the existing systems do no effectively balance the conflicting objectives of drivers and passengers. We propose a Highest Aggregated Score Vehicular Recommendation (HASVR) framework that recommends a vehicle with highest aggregated score to the requesting passenger. The aggregated score is based on parameters, namely: (a) average time delay, (b) vehicle’s capacity, (c) fare reduction, (d) driving distance, and (e) profit increment. We propose a heuristic that balances the incentives of both drivers and passengers keeping in consideration their constraints and the real-time traffic conditions. We evaluated HASVR with a real-world dataset that contains GPS trace data of 61,136 taxicabs. Evaluation results confirm the effectiveness of HASVR compared to existing scheme in reducing the total mileage used to deliver all passengers, reducing the passengers’ fare, increasing the profit of drivers, and increasing the percentage of satisfied ride requests