Cognitive Similarity-Based Collaborative Filtering Recommendation System

Nguyen, Luong Vuong; Hong, Minsung; Jung, Jason J.; Sohn, Bong-Soo

doi:10.3390/app10124183

Cited by 57 publications

(28 citation statements)

References 24 publications

(24 reference statements)

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“…The performance of an imputation algorithm should be evaluated by the final ratings predicted by the algorithm rather than its interim products. To maintain consistency with other studies, we applied the Mean Absolute Error (MAE) as a metric to compare the algorithms [31,32]. Equation ( 16) is an equation for calculating MAE, where r as andr as are the real and predicted ratings, respectively, by user u a on item t s , X is the test dataset, and |X| is the size of X.…”

Section: Methodsmentioning

confidence: 99%

A Technique of Recursive Reliability-Based Missing Data Imputation for Collaborative Filtering

et al. 2021

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Collaborative filtering (CF) is a recommendation technique that analyzes the behavior of various users and recommends the items preferred by users with similar preferences. However, CF methods suffer from poor recommendation accuracy when the user preference data used in the recommendation process is sparse. Data imputation can alleviate the data sparsity problem by substituting a virtual part of the missing user preferences. In this paper, we propose a k-recursive reliability-based imputation (k-RRI) that first selects data with high reliability and then recursively imputes data with additional selection while gradually lowering the reliability criterion. We also propose a new similarity measure that weights common interests and indifferences between users and items. The proposed method can overcome disregarding the importance of missing data and resolve the problem of poor data imputation of existing methods. The experimental results demonstrate that the proposed approach significantly improves recommendation accuracy compared to those resulting from the state-of-the-art methods while demanding less computational complexity.

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Section: Methodsmentioning

confidence: 99%

A Technique of Recursive Reliability-Based Missing Data Imputation for Collaborative Filtering

et al. 2021

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“…This research aims to increase the accuracy in the recommending process by using the ontology, combine with Singular Value Decomposition (SVD), the dimensionality reduction technique, to improve the scalability of recommending method. In [15], the cognitive similarity between users was considered to define the similar users that improve the performance of collaborative filtering in the movie recommendation system. This issue was also mentioned and exploited in [11] that proposed the crowdsourcing platform to collecting feedback from users who have experiences in the movies domain.…”

Section: Related Workmentioning

confidence: 99%

Tourism Recommender System Based on Cognitive Similarity Between Cross-Cultural Users

Nguyen

Jung

2021

Intelligent Environments 2021

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Nowadays, the speedy increasing information in tourism services since a massive amount of data is constructed by tourists experiences. The recommendation systems are widely applied to tourism services and focus on determining personalized user preferences to handle this extensive information. Exploiting the different cultural effects rarely consider in recent studies despite this factor influences recommendation based on user preferences. Furthermore, existing research only evaluates the relevance of cultural differences to their recommendation, rather than using the cross-cultural factors to recommendations systems. This paper proposes the collaborative filtering recommendation system based on similar tourist places where users from different cross-cultural can share their spatial experiences. To do that, we first collect user feedback about similar tourist places from many nationalities (consider as the cultures). We then exploit this feedback to define similar cross-cultural users (neighbors) based on a cognitive similarity. Finally, the system generates personalized recommendations based on user experiences and their neighbors. The initial dataset collected from TripAdvisor, consisting of four types such as hotels, restaurants, shopping malls, and attractions, is provided to the feedback collection function in our experiment. We were using the classical method, user-based Pearson correlation, as a baseline to demonstrate the performance of our proposed method. The result shows that the proposed system outperforms the baseline in terms of MAE and RMSE metrics.

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“…Bobadilla et al proposed a generic deep learning architecture to optimizing the collaborative filtering recommender system which is also an effective solution in the task of the POI recommendation [15]. Nguyen et al also proposed a generic recommender system using the cognitive similarity-based collaborative filtering technique which is capable of addressing the general task of POI recommendation [16]. Compared to the current POI recommendation approaches, our proposed model aims to address the sparsity of check-in records using the mechanism of adversarial learning and taking the geographical influence into account for improving the effectiveness of user and POI embeddings.…”

Section: Related Workmentioning

confidence: 99%

MTPR: A Multi-Task Learning Based POI Recommendation Considering Temporal Check-Ins and Geographical Locations

et al. 2020

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The rapid development of location-based social networks (LBSNs) produces the increasing number of check-in records and corresponding heterogeneous information which bring big challenges of points-of-interest (POIs) recommendation in our daily lives. The emergence of various recommender techniques bridges the gap between the numerous heterogeneous check-ins and the personalized POI recommendation. However, due to the differences between LBSNs and conventional recommendation tasks, besides the user feedback, the spatio-temporal information is also significant to precisely capture the user preferences. In this paper, we propose a multi-task learning model based POI recommender system which exploits a structure of generative adversarial networks (GAN) simultaneously considering temporal check-ins and geographical locations. The GAN-based model is capable of relieving the sparsity of check-in data in POI recommender systems. The temporal check-ins not only present the preference but also show the lifestyle of an individual while the geographical locations describe the active region of users which further filters POIs far from the feasible region. The multi-task learning strategy is capable of combining the information of temporal check-ins and geographical locations to improve the performance of personalized POI recommendation. We conduct the experiments on two real-world LBSNs datasets and the experimental results show the effectiveness of our proposed approach.

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Cognitive Similarity-Based Collaborative Filtering Recommendation System

Cited by 57 publications

References 24 publications

A Technique of Recursive Reliability-Based Missing Data Imputation for Collaborative Filtering

A Technique of Recursive Reliability-Based Missing Data Imputation for Collaborative Filtering

Tourism Recommender System Based on Cognitive Similarity Between Cross-Cultural Users

MTPR: A Multi-Task Learning Based POI Recommendation Considering Temporal Check-Ins and Geographical Locations

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