EARS: Emotion-aware recommender system based on hybrid information fusion

Qian, Yongxian; Zhang, Yin; Ma, Xiao; Yu, Han; Peng, Limei

doi:10.1016/j.inffus.2018.06.004

Cited by 114 publications

(68 citation statements)

References 13 publications

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“…External attributes were modelled for effective software product recommendations. Quian et al [18] proposed ''EARS''(Emotion-aware recommender system based on hybrid information fusion) that uses social information, sentiment analysis and gaussian distribution based userbehaviour analysis for recommendation. Da'u and Salim [19] proposed sentiment-aware deep recommender system with neural attention network (SDRA) that uses aspect based sentiment analysis for improving recommendation accuracies.…”

Section: Related Workmentioning

confidence: 99%

“…Figure 16 makes it evident that recommendation precision of CISER is commendably better than conventional contentbased filtering (gain = 30%), item-based collaborative filtering (gain = 25%) and user-based collaborative filtering (gain = 24%). Figure 17 compares the proposed CISER model with previous research models, namely, CUP [20], DNN [14], TRRuSST [17] and EARS [18].CUP and DNN model user-preference trends, EARS uses emotional and socialinformation for recommendations, whereas TRRuSST quantifies product quality by manipulating external attributes modelled on user-reviews. It is clear that CISER improves recommendation efficacy with MAP@5 = 38% i.e.…”

Section: Comparison With Baseline Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Reviewer Credibility and Sentiment Analysis Based User Profile Modelling for Online Product Recommendation

Kumar

Al‐Turjman³

et al. 2020

IEEE Access

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Deciphering user purchase preferences, their likes and dislikes is a very tricky task even for humans, making its automation a very complex job. This research work augments heuristic-driven user interest profiling with reviewer credibility analysis and fine-grained feature sentiment analysis to devise a robust recommendation methodology. The proposed credibility, interest and sentiment enhanced recommendation (CISER) model has five modules namely candidate feature extraction, reviewer credibility analysis, user interest mining, candidate feature sentiment assignment and recommendation module. Review corpus is given as an input to the CISER model. Candidate feature extraction module uses context and sentiment confidence to extract features of importance. To make our model robust to fake and unworthy reviews and reviewers, reviewer credibility analysis proffers an approach of associating expertise, trust and influence scores with reviewers to weigh their opinion according to their credibility. The user interest mining module uses aesthetics of review writing as heuristics for interest-pattern mining. The candidate feature sentiment assignment module scores candidate features present in review based on their fastText sentiment polarity. Finally, the recommendation module uses credibility weighted sentiment scoring of user preferred features for purchase recommendations. The proposed recommendation methodology harnesses not only numeric ratings, but also sentiment expressions associated with features, customer preference profile and reviewer credibility for quantitative analysis of various alternative products. The mean average precision (MAP@1) for CISER is 93% and MAP@3 is 49%, which is better than current state-of-the-art systems.

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Section: Related Workmentioning

confidence: 99%

Section: Comparison With Baseline Modelsmentioning

confidence: 99%

Reviewer Credibility and Sentiment Analysis Based User Profile Modelling for Online Product Recommendation

Kumar

Al‐Turjman³

et al. 2020

IEEE Access

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“…Huang et al [25] constructed a hybrid multigroup coclustering recommendation framework to cluster users and items into multiple categories simultaneously, which fully utilized various data sources including ratings and user social networks. Qian et al [26] fused three types of representative heterogeneous information to comprehensively analyze user features, such as ratings, user social networks, and user review sentiments. Zheng et al [27] considered the evolving nature of user preferences over time and developed a time-sensitive and tag-aware recommendation framework.…”

Section: Additional Data Sources For Recommender Systemsmentioning

confidence: 99%

DeepFusion: Fusing User-Generated Content and Item Raw Content towards Personalized Product Recommendation

Gan

Zhang

2020

Complexity

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Personalized recommender systems, as effective approaches for alleviating information overload, have received substantial attention in the last decade. Learning effective latent factors plays the most important role in recommendation methods. Several recent works extracted latent factors from user-generated content such as ratings and reviews and suffered from the sparsity problem and the unbalanced distribution problem. To tackle these problems, we enrich the latent representations by incorporating user-generated content and item raw content. Deep neural networks have emerged as very appealing in learning effective representations in many applications. In this paper, we propose a novel deep neural architecture named DeepFusion to jointly learn user and item representations from numerical ratings, textual reviews, and item metadata. In this framework, we utilize multiple types of deep neural networks that are best suited for each type of heterogeneous inputs and introduce an extra layer to obtain the joint representations for users and items. Experiments conducted on the Amazon product data demonstrate that our approach outperforms multiple state-of-the-art baselines. We provide further insight into the design selections and hyperparameters of our recommendation method. In addition, we further explore the relative importance of various item metadata information on improving the rating prediction performance towards personalized product recommendation, which is extremely valuable for feature extraction in practice.

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“…The tourist attractions recommendation system has measured performance and reliability with four measurements [18] as follows:…”

Section: B Performance Evaluationmentioning

confidence: 99%

The Tourist Attractions Recommender System for Bangkok Thailand

Chujai¹,

Singthongchai²,

Yasaga³

et al. 2020

IJCTE

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The objective of this research is to design and develop a tool to evaluate tourists' satisfaction with the attractions recommendation system in Bangkok, Thailand. We have four main stages for the tourist attraction recommendation system. The first stage is to fill imputed missing values with association rules and multiple imputations. The second stage is constructing the tourist attractions recommendation model by ranking the tourist attractions with a ranking method and similarity measurements based on a personal recommender system with cosine algorithm. The third stage is to design and develop the personal recommender website. And the last stage is to evaluate the personal recommender system with four measurements: accuracy, precision, f-measure, and g-mean. The experiment results from a sampling of thirty people found that the tourist attraction recommendation system can: 1) make a positive recommendation 340 times, but 105 times will not meet the needs, and 2) make a negative recommendation 708 times, but 77 times will meet the needs. The results show that the tourist attractions recommendation system has satisfactory performance and reliability with high accuracy, precision, and f-measure, and g-mean values of 85.20%, 76.40%, 78.89%, and 84.26%, respectively. In addition, it was found that the users' satisfaction towards the system was at a high level with a value of 4.60. This means that the proposed tourist attractions recommendation system can be used to recommend personal preferences as well.

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EARS: Emotion-aware recommender system based on hybrid information fusion

Cited by 114 publications

References 13 publications

Reviewer Credibility and Sentiment Analysis Based User Profile Modelling for Online Product Recommendation

Reviewer Credibility and Sentiment Analysis Based User Profile Modelling for Online Product Recommendation

DeepFusion: Fusing User-Generated Content and Item Raw Content towards Personalized Product Recommendation

The Tourist Attractions Recommender System for Bangkok Thailand

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