Improving collaborative filtering’s rating prediction accuracy by introducing the experiencing period criterion
Sign Up to like & getrecommendations! Published in 2020 at "Neural Computing and Applications"
DOI: 10.1007/s00521-020-05460-y
Abstract: Collaborative filtering algorithms take into account users’ tastes and interests, expressed as ratings, in order to formulate personalized recommendations. These algorithms initially identify each user’s “near neighbors,” i.e., users having highly similar tastes and likings.… read more here.
Keywords: collaborative filtering; accuracy; period; rating prediction ... See more keywords
A novel rating prediction method based on user relationship and natural noise
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DOI: 10.1007/s11042-017-4481-8
Abstract: Rating prediction is a hot spot in the research of recommender systems. There are lots of methods in this field such as collaborative filtering. However, few of these approaches take users’ friendship relationships into consideration,… read more here.
Keywords: rating; novel rating; rating prediction; natural noise ... See more keywords
Integrating Stacked Sparse Auto-Encoder Into Matrix Factorization for Rating Prediction
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DOI: 10.1109/access.2021.3053291
Abstract: Currently, collaborative filtering technology has been widely used in personalized recommender systems. The problem of data sparsity is a severe challenge faced by traditional collaborative filtering methods based on matrix factorization techniques. A lot of… read more here.
Keywords: matrix; matrix factorization; rating prediction; rating ... See more keywords
Novel SDDM Rating Prediction Models for Recommendation Systems
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DOI: 10.1109/access.2021.3097207
Abstract: The accuracy of behavioral interactive features is a key factor for improving the performance of rating prediction. In order to deeply explore the potential rules of user behavior and enhance the accurate representation of interactive… read more here.
Keywords: rating prediction; sddm; prediction models; interactive features ... See more keywords
Dual Auto-Encoder Based Rating Prediction Recommendation Algorithm
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DOI: 10.1109/access.2022.3205610
Abstract: Collaborative filtering is the most widely used method in recommendation algorithms, but it still faces the serious problem of data sparsity. Traditional collaborative filtering uses matrix decomposition to learn the latent features of users and… read more here.
Keywords: recommendation; dual auto; auto; rating ... See more keywords
Enabling Kernel-Based Attribute-Aware Matrix Factorization for Rating Prediction
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DOI: 10.1109/tkde.2016.2641439
Abstract: In recommender systems, one key task is to predict the personalized rating of a user to a new item and then return the new items having the top predicted ratings to the user. Recommender systems… read more here.
Keywords: factorization; cold start; matrix factorization; rating prediction ... See more keywords
Joint Reason Generation and Rating Prediction for Explainable Recommendation
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Knowledge and Data Engineering"
DOI: 10.1109/tkde.2022.3146178
Abstract: Most recommendation systems focus on predicting rating or finding aspect information in reviews to understand user preferences and item properties. However, these methods ignore the effectiveness and persuasiveness of recommendation results. Consequently, explainable recommendation, namely… read more here.
Keywords: recommendation; explainable recommendation; reason generation; generation rating ... See more keywords