A hybrid recommendation system with many-objective evolutionary algorithm

Cai, Xingjuan; Hu, Zhaoming; Zhao, Peng; Zhang, Wensheng; Chen, Jinjun

doi:10.1016/j.eswa.2020.113648

Cited by 111 publications

(75 citation statements)

References 35 publications

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“…In this paper, the hand gesture recognition model is established based on color information, depth information, and depth and color information fusion respectively. [44][45][46] Therefore, when collecting images, we need to pay attention to the collection of hand gesture data under different background and light conditions, so that the hand gesture recognition model has good robustness. In this paper, four kinds of self-occlusion gesture data are collected, and the color map and depth map of four kinds of gesture are defined as shown in Figure 6.…”

Section: Gesture Databasementioning

confidence: 99%

Occlusion gesture recognition based on improvedSSD

Liao

et al. 2020

Concurrency and Computation

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Gesture recognition has always been a research hotspot in the field of human-computer interaction. Its purpose is to realize the natural interaction with the machine by recognizing the semantics expressed by gesture. In the process of gesture recognition, the occlusion of gesture is an inevitable problem. In the process of gesture recognition, some or even all of the gesture features will be lost due to the occlusion of the gesture, resulting in the wrong recognition or even unrecognizability of the gesture. Therefore, it is of great significance to study gesture recognition under occlusion. The single shot multibox detector (SSD) algorithm is analyzed, and the front-end network is compared. Mobilenets is selected as the front-end network, and the Mobilenets-SSD network is improved. In tensorflow environment, based on the improved network model, the self-occlusion gesture and object occluding gesture are trained in color map, depth map, and color and depth fusion respectively. The recognition models of self-occlusion gestures and object-occlusion gestures in color map, depth map, and color and depth fusion are obtained. And compare and analyze the learning rate, loss function, and average accuracy of various models obtained for occlusion gesture recognition.

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Section: Gesture Databasementioning

confidence: 99%

Occlusion gesture recognition based on improvedSSD

Liao

et al. 2020

Concurrency and Computation

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“…Most real-world problems, such as recommender systems, involve multiple conflicting objective functions. It is not possible to obtain a single optimum solution to such problems [21]. Instead, we aim to obtain a set of tradeoff solutions where no solution is dominated by the others.…”

Section: Nimize Mmentioning

confidence: 99%

A Novel Many-Objective Recommendation Algorithm for Multistakeholders

Wang

Chen

2020

IEEE Access

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Most traditional recommender systems focus specifically on increasing consumer satisfaction by providing a list of relevant content to consumers. However, the perspectives of other multisided marketplace stakeholders are also equally important, i.e., the exposure for suppliers or providers and profit for the platform. The suppliers want their products to be presented to users, and the objective of the platform is to maximize their profit. Nevertheless, because consumers' preferences and the objectives of providers as well as the platform may conflict with each other, it degrades the utility of the recommendation methods by only considering users' views. Therefore, in this work, we use a many-objective optimization method to maintain a tradeoff among five objectives for three stakeholders and obtain multiple Pareto front solutions in a single run. We first combine customer lifetime value and user purchase preference to create a new similarity model (Sim_RFMP) to increase the recommendation accuracy of the recommendation list. Furthermore, we propose a many-objective model (NBHXMAOEA) for multistakeholder recommendation. In NBHXMAOEA, we present a novel N-block heuristic crossover operator (NBHX) that recombines blocks of chromosomes based on heuristics. Through extensive experiments, the results demonstrate that our proposed NBHXMAOEA achieves superior performance in terms of average accuracy, diversity, novelty, provider coverage, and platform profit to its competing methods. INDEX TERMS Many-objective, recommender systems, similarity model, stakeholders.

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“…See Holland (1992) for a general introduction into GAs. Some recent applications in engineering can be found in Cai et al (2020aCai et al ( , 2020b and Ma et al (2014aMa et al ( , 2014bMa et al ( , 2014cMa et al ( , 2016aMa et al ( , 2016b.…”

Section: The Frameworkmentioning

confidence: 99%