Empirical Study of User Preferences Based on Rating Data of Movies

Zhao, Yingsi; Shen, Bo

doi:10.1371/journal.pone.0146541

Cited by 9 publications

(2 citation statements)

References 52 publications

(57 reference statements)

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“…Ghauth and Abdullah [39] proposed a framework that recommends learning materials based on content similarity and user ratings. In predicting user preferences, Zhao and Shen [40] conducted an empirical study of movie ratings and proposed a preference model that eliminates impracticable predictions.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhancing Recommender Systems by Fusing Diverse Information Sources through Data Transformation and Feature Selection

2023

KSII TIIS

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Recommender systems aim to recommend items to users by taking into account their probable interests. This study focuses on creating a model that utilizes multiple sources of information about users and items by employing a multimodality approach. The study addresses the task of how to gather information from different sources (modalities) and transform them into a uniform format, resulting in a multi-modal feature description for users and items. This work also aims to transform and represent the features extracted from different modalities so that the information is in a compatible format for integration and contains important, useful information for the prediction model. To achieve this goal, we propose a novel multi-modal recommendation model, which involves extracting latent features of users and items from a utility matrix using matrix factorization techniques. Various transformation techniques are utilized to extract features from other sources of information such as user reviews, item descriptions, and item categories. We also proposed the use of Principal Component Analysis (PCA) and Feature Selection techniques to reduce the data dimension and extract important features as well as remove noisy features to increase the accuracy of the model. We conducted several different experimental models based on different subsets of modalities on the MovieLens and Amazon sub-category datasets. According to the experimental results, the proposed model significantly enhances the accuracy of recommendations when compared to SVD, which is acknowledged as one of the most effective models for recommender systems. Specifically, the proposed model reduces the RMSE by a range of 4.8% to 21.43% and increases the Precision by a range of 2.07% to 26.49% for the Amazon datasets. Similarly, for the MovieLens dataset, the proposed model reduces the RMSE by 45.61% and increases the Precision by 14.06%. Additionally, the experimental results on both datasets demonstrate that combining information from multiple modalities in the proposed model leads to superior outcomes compared to relying on a single type of information.

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Section: Literature Reviewmentioning

confidence: 99%

Enhancing Recommender Systems by Fusing Diverse Information Sources through Data Transformation and Feature Selection

2023

KSII TIIS

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“…They found that this preference information could be used for advertising, product recommendation, and various other personalized social media services. A simple statistical method was developed by Zhao and Shen (2016) to investigate the characteristics of user preferences on the MovieLens website. Their results show that users in small cliques always share similar opinions on movies to other clique members.…”

Section: Online User Surveys Via Social Mediamentioning

confidence: 99%

Using social media to explore regional cuisine preferences in China

Zhang

Yue

Zhou

et al. 2019

OIR

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Purpose Food plays an important role in every culture around the world. Recently, cuisine preference analysis has become a popular research topic. However, most of these studies are conducted through questionnaires and interviews, which are highly limited by the time, cost and scope of data collection, especially when facing large-scale survey studies. Some researchers have, therefore, attempted to mine cuisine preferences based on online recipes, while this approach cannot reveal food preference from people’s perspective. Today, people are sharing what they eat on social media platforms by posting reviews about the meal, reciting the names of appetizers or entrees, and photographing as well. Such large amount of user-generated contents (UGC) has potential to indicate people’s preferences over different cuisines. Accordingly, the purpose of this paper is to explore Chinese cuisine preferences among online users of social media. Design/methodology/approach Based on both UGC and online recipes, the authors first investigated the cuisine preference distribution in different regions. Then, dish preference similarity between regions was calculated and few geographic factors were identified, which might lead to such regional similarity appeared in our study. By applying hierarchical clustering, the authors clustered regions based on dish preference and ingredient usage separately. Findings Experimental results show that, among 20 types of traditional Chinese cuisines, Sichuan cuisine is most favored across all regions in China. Geographical proximity is the more closely related to differences of regional dish preference than climate proximity. Originality/value Different from traditional definitions of regions to which cuisine belong, the authors found new association between region and cuisine based on dish preference from social media and ingredient usage of dishes. Using social media may overcome problems with using traditional questionnaires, such as high costs and long cycle for questionnaire design and answering.

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