Collaborative Filtering Algorithm Based on Trust and Information Entropy

Kang, Anqi

doi:10.1109/iciibms.2018.8549962

Cited by 2 publications

(3 citation statements)

References 11 publications

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“…e purpose of this is to prevent false high information quality in this modal information. A large number of studies have found that the divergence of information plays a vital role in the accuracy of information [34]. Next, it is needed to obtain the recognition rate of single-modal information, such as the success recognition rate of speech information α 1 .…”

Section: Trust Degree Evaluation and Reverse Active Interactionmentioning

confidence: 99%

MES: A Helping Elderly Escort Interactive System Based on Reverse Active Integration of Multimodal Intentions

Lang

Feng

Yang

et al. 2022

Scientific Programming

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Lacking human-computer collaborative capability is one of the key problems commonly faced by the existing escort robots. To this end, a helping elderly escort interactive system based on reverse active integration of multimodal intentions (MES) is proposed in this paper in response to the elderly’s decline in language expression ability, memory, and other abilities. This system can understand the intention of the elderly based on scene perception and three-modal information: speech, gesture, and posture. In detail, the system can extract the interactive intention from the nondeterministic multimodal data input by the elderly and evaluate the trust degree of the extracted intention. The evaluation of intention trust degree is such a process that the system autonomously judges the feasibility of the elderly’s intention and corrects the wrong intention expressed by the elderly due to memory decline by reverse thinking of “find reasons based on the results”; when the intention cannot be extracted, the system will take the initiative to ask the elderly for enhanced information conducive to the intention extraction, so as to quickly and correctly extract the interactive intention of the elderly. This design aims at improving the quality of elderly care, making the interaction between the elderly and the robot more natural, improving the accuracy in intention extraction from fuzzy expression, as well as breaking the traditional “master-slave” human-computer interaction and improving the harmony of human-computer interaction. Further, the implementation principle of the system is detailed, and the system is evaluated in this paper. The evaluation experiment was conducted by a robot Pepper embedded with the system. Through experiments, it is verified that Pepper can quickly and accurately get the real intention of the elderly in the interaction with the elderly. In a challenging environment (such as the unclear expression of the elderly), it can still correctly extract the real intention of the elderly with an accuracy of 97% and can effectively avoid the wrong intention expressed by the elderly. This puts forward a valuable research path for the challenge in human-computer collaborative interaction.

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Section: Trust Degree Evaluation and Reverse Active Interactionmentioning

confidence: 99%

MES: A Helping Elderly Escort Interactive System Based on Reverse Active Integration of Multimodal Intentions

Lang

Feng

Yang

et al. 2022

Scientific Programming

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“…Zhang and Zhong [18] have proposed a method to design trust model by trust transitivity feature. In [19], the authors have combined the direct trust of users and the indirect trust to obtain the trust similarity by the Pearson similarity formula.…”

Section: Related Workmentioning

confidence: 99%

“…In detail, the agent observes the environment state s t to make a joint action ðx t , y t Þ, which obtains a reward and affects the state of next moment s t+1 . After that, the agent stores the transition tuple ðs t , ðx t , y t Þ, r t , s t+1 Þ in the replay memory D. The transition in D consists of the current state s t , the current action a t , the reward r t calculated by formula (19), and the state of the next decision epoch after the environment receives the action s t+1 . Furthermore, at each episode, minibatches are tokens from D. The network updates parameter θ and minimizes the mean square error as follows:…”

Section: Reward Designmentioning

confidence: 99%

Social‐Aware Caching Strategy Based on Joint Action Deep Reinforcement Learning

Jing

Song

et al. 2021

Wireless Communications and Mobile Computing

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Caching in device-to-device (D2D) networks is emerging a promising trend, which enables to reduce backhaul traffic. Moreover, social interaction among users influences the performance of overall system network. Therefore, it is crucial to consider social attributes in the D2D networks to develop a caching strategy to resolve the problem of unbalanced content distributed. In this paper, we consider two types of users according to their activeness, i.e., active users and inactive users. Inactive users assist active users cache contents during off-peak periods and provide the contents to the active users during peak periods to relieve the pressure of base station (BS). In addition, caching system model is divided into physical domain model and social domain model. In physical domain, the quality of communication links is judged by the delay between D2D users. In social domain, based on a real-world dataset, CiaoDVD, we calculate user similarity in three dimensions and obtain user trust by a trust topology to measure user relationships. Finally, in order to maximize the cache hit ratio, a joint action deep Q -networks (JADQN) framework is proposed to pair the active users with inactive users and distribute the contents to inactive users. Simulation results indicate that the proposed strategy improves the cache hit ratio by 42.9% and reduces the download delay by 48.8% compared with least frequency used (LFU) algorithm, which validates the effectiveness of our method.

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Collaborative Filtering Algorithm Based on Trust and Information Entropy

Cited by 2 publications

References 11 publications

MES: A Helping Elderly Escort Interactive System Based on Reverse Active Integration of Multimodal Intentions

MES: A Helping Elderly Escort Interactive System Based on Reverse Active Integration of Multimodal Intentions

Social‐Aware Caching Strategy Based on Joint Action Deep Reinforcement Learning

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