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Preference Modeling by Exploiting Latent Components of Ratings
Information Retrieval · Submitted 19 Oct 2017
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Meanwhile, it is verified by the experimental results on several benchmark datasets that the proposed method is superior to the state-of-art methods in terms of recommendation accuracy.
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The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.
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The abstract, sentence by sentence
Understanding user preference is essential to the optimization of recommender systems. As a feedback of user's taste, rating scores can directly reflect the preference of a given user to a given product. Uncovering the latent components of user ratings is thus of significant importance for learning user interests. In this paper, a new recommendation approach, called LCR, was proposed by investigating the latent components of user ratings. The basic idea is to decompose an existing rating into several components via a cost-sensitive learning strategy. Specifically, each rating is assigned to several latent factor models and each model is updated according to its predictive errors. Afterwards, these accumulated predictive errors of models are utilized to decompose a rating into several components, each of which is treated as an independent part to retrain the latent factor models. Finally, all latent factor models are combined linearly to estimate predictive ratings for users. In contrast to existing methods, LCR provides an intuitive preference modeling strategy via multiple component analysis at an individual perspective. Meanwhile, it is verified by the experimental results on several benchmark datasets that the proposed method is superior to the state-of-art methods in terms of recommendation accuracy.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Understanding user preference is essential to the optimization of recommender systems. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s1 | As a feedback of user's taste, rating scores can directly reflect the preference of a given user to a given product. | background 1.00 | 0.05 | 0.02 | asserted 0.99 |
| s2 | Uncovering the latent components of user ratings is thus of significant importance for learning user interests. | background 0.97 | 0.06 | 0.04 | asserted 1.00 |
| s3 | In this paper, a new recommendation approach, called LCR, was proposed by investigating the latent components of user ratings. | contribution 1.00 | 0.94 | 0.13 | asserted 1.00 |
| s4 | The basic idea is to decompose an existing rating into several components via a cost-sensitive learning strategy. | method 0.98 | 0.78 | 0.04 | asserted 0.99 |
| s5 | Specifically, each rating is assigned to several latent factor models and each model is updated according to its predictive errors. | method 1.00 | 0.73 | 0.03 | asserted 0.99 |
| s6 | Afterwards, these accumulated predictive errors of models are utilized to decompose a rating into several components, each of which is treated as an independent part to retrain the latent factor models. | method 1.00 | 0.85 | 0.04 | asserted 0.99 |
| s7 | Finally, all latent factor models are combined linearly to estimate predictive ratings for users. | method 1.00 | 0.81 | 0.08 | asserted 0.99 |
| s8 | In contrast to existing methods, LCR provides an intuitive preference modeling strategy via multiple component analysis at an individual perspective. | contribution 0.77 | 0.61 | 0.17 | asserted 0.99 |
| s9 | Meanwhile, it is verified by the experimental results on several benchmark datasets that the proposed method is superior to the state-of-art methods in terms of recommendation accuracy. | result 1.00 | 0.94 | 0.78 | asserted 1.00 |
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The paper
Original abstract
Understanding user preference is essential to the optimization of recommender systems. As a feedback of user's taste, rating scores can directly reflect the preference of a given user to a given product. Uncovering the latent components of user ratings is thus of significant importance for learning user interests. In this paper, a new recommendation approach, called LCR, was proposed by investigating the latent components of user ratings. The basic idea is to decompose an existing rating into several components via a cost-sensitive learning strategy. Specifically, each rating is assigned to several latent factor models and each model is updated according to its predictive errors. Afterwards, these accumulated predictive errors of models are utilized to decompose a rating into several components, each of which is treated as an independent part to retrain the latent factor models. Finally, all latent factor models are combined linearly to estimate predictive ratings for users. In contrast to existing methods, LCR provides an intuitive preference modeling strategy via multiple component analysis at an individual perspective. Meanwhile, it is verified by the experimental results on several benchmark datasets that the proposed method is superior to the state-of-art methods in terms of recommendation accuracy.
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