ReRec: A Divide-and-Conquer Approach to Recommendation Based on Repeat Purchase Behaviors of Users in Community E-Commerce

Wu, Jun; Li, Yuanyuan; Shi, Li; Yang, Liping; Niu, Xiaxia; Zhang, Wen

doi:10.3390/math10020208

Cited by 7 publications

(5 citation statements)

References 24 publications

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“…The arity list, which defines the number of the subproblems into which the problem is decomposed at each level; for example, if it is equal to [2,3,3,5,5], then the first time the decomposing is done into two subproblems, next time, each of these subproblems is decomposed into 3 subproblems, and so forth; 2.…”

Section: Divide and Conquer Generalized Patternmentioning

confidence: 99%

“…Divide-and-conquer is a very important programming paradigm that has been adapted to become a parallel programming pattern, too. Informally, the classical form of it could be described as being a recursive method defined by the following steps [1,2]: The applicability of the divide-and-conquer is very diverse: from classical sorting algorithms such as quick-sort [3] and merge-sort [1], to complex applications such as model checking [4], recommendation systems [5], or even in testing the gamification effectiveness [6].…”

Section: Introductionmentioning

confidence: 99%

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On Generalizing Divide and Conquer Parallel Programming Pattern

Niculescu¹

2022

Mathematics

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(1) Background: Structuring is important in parallel programming in order to master its complexity, and this structuring could be achieved through programming patterns and skeletons. Divide-and-conquer computation is essentially defined by a recurrence relation that links the solution of a problem to the solutions of subproblems of the same type, but of smaller sizes. This pattern allows the specification of different types of computations, and so it is important to provide a general specification that comprises all its cases. We intend to prove that the divide-and-conquer pattern could be generalized such that to comprise many of the other parallel programming patterns, and in order to prove this, we provide a general formulation of it. (2) Methods: Starting from the proposed generalized specification of the divide-and-conquer pattern, the computation of the pattern is analyzed based on its stages: decomposition, base-case and composition. Examples are provided, and different execution models are analyzed. (3) Results: a general functional specification is provided for a divide-and-conquer pattern and based on it, and we prove that this general formulation could be specialized through parameters’ instantiating into other classical parallel programming patterns. Based on the specific stages of the divide-and-conquer, three classes of computations are emphasized. In this context, an equivalent efficient bottom-up computation is formally proved. Associated models of executions are emphasized and analyzed based on the three classes of divide-and-conquer computations. (4) Conclusion: A more general definition of the divide-and-conquer pattern is provided, and this includes an arity list for different decomposition degrees, a level of recursion, and also an alternative solution for the cases that are not trivial but allow other approaches (sequential or parallel) that could lead to better performance. Together with the associated analysis of patterns equivalence and optimized execution models, this provides a general formulation that is useful both at the semantic level and implementation level.

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Section: Divide and Conquer Generalized Patternmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

On Generalizing Divide and Conquer Parallel Programming Pattern

Niculescu¹

2022

Mathematics

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“…A recommender system strives to predict which items would stimulate user interest based on their behavior on a specific web site. In our research, we employ two types of approaches, namely collaborative filtering (CF) and content-based filtering (CBF), both of which are well-known methods for creating a recommender system [27][28][29].…”

Section: Recommendation Systems In E-commercementioning

confidence: 99%

Customer Profiling Using Internet of Things Based Recommendations

et al. 2022

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The digital revolution caused major changes in the world because not only are people increasingly connected, but companies are also turning more to the use of intelligent systems. The large amount of information about each product provided by the e-commerce websites may confuse the customers in their choices. The recommendations system and Internet of Things (IoT) are being used by an increasing number of e-commerce websites to help customers find products that fit their profile and to purchase what they had already chosen. This paper proposes a novel IoT based system that would serve as the foundation for creating a profile, which will store all the contextual data, personalize the content, and create a personal profile for each user. In addition, customer segmentation is used to determine which items the client wants. Next, statistical analysis is performed on the extracted data, where feelings, state of mind, and categorization play a critical role in forecasting what customers think about products, services, and so on. We will assess the accuracy of the forecasts to identify the most appropriate products based on the multi-source data thanks to the IoT, which assigns a digital footprint linking customers, processes, and things through identity-based information and recommendations, which is applied by using Raspberry Pi and other sensors such as the camera. Moreover, we perform experiments on the recommendation system to gauge the precision in predictions and recommendations.

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“…The CF method includes various techniques like clustering, Bayesian network, latent semantic model, deep learning, and the Markov decision process (MDP). However, the performance of the CF recommendation method tends to deteriorate with sparse scores, often leading to cold start problem in systems (Wu et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Personalized Recommendation Method of E-Commerce Products Based on In-Depth User Interest Portraits

Li,

Bao

2023

International Journal of Information Technology and Web Engineering

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In dynamic e-commerce environments, researchers strive to understand users' interests and behaviors to enhance personalized product recommendations. Traditional collaborative filtering (CF) algorithms have encountered computational challenges such as similarity errors and user rating habits. This research addresses these issues by emphasizing user profiling techniques. This article proposes an innovative user profile updating technique that explores the key components of user profile (basic information, behavior, and domain knowledge). An enhanced kernel fuzzy mean clustering algorithm constructs a dynamic user portrait based on domain knowledge mapping. This dynamic portrait is combined with e-commerce personalized recommendation to improve the accuracy of inferring user interests, thus facilitating accurate recommendation on the platform. The method proposed in this article greatly improves the overall performance and provides strong support for developing smarter and more personalized e-commerce product recommendation systems.

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ReRec: A Divide-and-Conquer Approach to Recommendation Based on Repeat Purchase Behaviors of Users in Community E-Commerce

Cited by 7 publications

References 24 publications

On Generalizing Divide and Conquer Parallel Programming Pattern

On Generalizing Divide and Conquer Parallel Programming Pattern

Customer Profiling Using Internet of Things Based Recommendations

Personalized Recommendation Method of E-Commerce Products Based on In-Depth User Interest Portraits

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