Group Recommendation Systems Based on External Social‐Trust Networks

Fang, Guang; Su, Lei; Jiang, Di; Wu, Liping

doi:10.1155/2018/6709607

Cited by 14 publications

(7 citation statements)

References 30 publications

(48 reference statements)

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“…Current research has pointed out that social relationships among group members significantly influence their decision-making behavior [44,58]. Users tend to rely more on recommendations from their friends or persons they trust than those generated by recommender systems [123].…”

Section: Social Relationshipmentioning

confidence: 99%

See 1 more Smart Citation

Humanized Recommender Systems: State-of-the-art and Research Issues

Tran

Felfernig

Tintarev

2021

ACM Trans. Interact. Intell. Syst.

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Psychological factors such as personality, emotions, social connections , and decision biases can significantly affect the outcome of a decision process. These factors are also prevalent in the existing literature related to the inclusion of psychological aspects in recommender system development. Personality and emotions of users have strong connections with their interests and decision-making behavior. Hence, integrating these factors into recommender systems can help to better predict users’ item preferences and increase the satisfaction with recommended items. In scenarios where decisions are made by groups (e.g., selecting a tourism destination to visit with friends), group composition and social connections among group members can affect the outcome of a group decision. Decision biases often occur in a recommendation process, since users usually apply heuristics when making a decision. These biases can result in low-quality decisions. In this article, we provide a rigorous review of existing research on the influence of the mentioned psychological factors on recommender systems. These factors are not only considered in single-user recommendation scenarios but, importantly, also in group recommendation ones, where groups of users are involved in a decision-making process. We include working examples to provide a deeper understanding of how to take into account these factors in recommendation processes. The provided examples go beyond single-user recommendation scenarios by also considering specific aspects of group recommendation settings.

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Section: Social Relationshipmentioning

confidence: 99%

“…Users tend to rely more on recommendations from their friends or persons they trust than those generated by recommender systems [123]. This behavior can be applied in situations where group members change their item preferences to reach a consensus [44]. For instance, let us assume that a user does not like an item i.…”

Section: Social Relationshipmentioning

confidence: 99%

Humanized Recommender Systems: State-of-the-art and Research Issues

Tran

Felfernig

Tintarev

2021

ACM Trans. Interact. Intell. Syst.

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“…However, user personalities were discarded. A method of group recommendation based on external social trust networks was designed in [17] with the consideration of personality factors. A preliminary evaluation of three use cases for social learning environments was proposed in [18].…”

Section: Related Workmentioning

confidence: 99%

Soft Cosine Gradient and Gaussian Mixture Joint Probability Recommender System for Online Social Networks

Devi¹,

Pattabiraman²

2020

IJIES

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Recommender systems are in recent past becoming more notable in the age of swift evolution of the information technology ensuring e-commerce users' suitable items. In recent years, various thread-based and trust based approaches have been proposed, which improve the precision and recall of recommendation to certain extent. However, these approaches are less accurate than expected when users' preferences evolve over time and when several aspects of the users are involved. To solve these problems, we propose a novel recommendation method called, Soft Cosine Gradient and Gaussian Joint Probability (SCG-GJP) for Online Social Networks (OSN). Specifically, the Local Soft Cosine Clique Clustering (LSCCC) is first used to identify good candidate items to be recommended. Second, using adjacency matrix factorization, probable past preference of individual user and overall preference of whole community is formed to reduce the problem of mean absolute error. Again, the obtained matrix is trained through the stochastic function gradient to rank the most suitable candidates. Finally, Gaussian Mixture Joint Probability Postfiltering model is proposed to provide useful recommendations. The experiments are executed with real-world dataset (DS) and compared with the conventional recommendation methods. Experimental results demonstrate that the SCG-GJP obtains higher accuracy, and lesser error as well as complexity.

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“…In order to identify topics and interests of users, topic models are often used for semantic analysis of posts [5] and analysis of subscriptions and friendship networks [6] of users to identify interests based on the topics of the user's communities and similar friends. To build a 360-degree user profile, the information obtained by analyzing his posts, subscriptions and social connections must be supplemented by analyzing media content.…”

Section: Introductionmentioning

confidence: 99%

Identifying User Interests and Habits Using Object Detection and Semantic Segmentation Models

Volokha

Peter

2021

Lecture Notes in Computer Science

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The article describes a software pipeline for identifying and classifying the interests of users in social networks using modern models and deep learning methods. The developed program is able to detect the presence of bad habits (smoking, alcohol), a sporting lifestyle, as well as determine the user's addiction to travel by an available set of photos. The software includes modules that implement deep learning algorithms for the object detection and semantic segmentation of images using the Cascade-R-CNN and DeepLabv3+ models, and the module for converting annotations of the images from COCO, ImageNet, OpenImagesV6 datasets and manually labeled images to the unified format. The models were trained on the created original datasets which include 90200 photos in total. The accuracy of the developed models is from 83.7% up to 86.6% mAP for object detection depending on a specific category of objects and 78.4% pixel accuracy for segmentation.

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Group Recommendation Systems Based on External Social‐Trust Networks

Cited by 14 publications

References 30 publications

Humanized Recommender Systems: State-of-the-art and Research Issues

Humanized Recommender Systems: State-of-the-art and Research Issues

Soft Cosine Gradient and Gaussian Mixture Joint Probability Recommender System for Online Social Networks

Identifying User Interests and Habits Using Object Detection and Semantic Segmentation Models

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