Collaborative filtering with weighted opinion aspects

Yang, Chong; Yu, Xiaohui; Liu, Yang; Nie, Yanping; Wang, Yuanhong

doi:10.1016/j.neucom.2015.12.136

Cited by 54 publications

(38 citation statements)

References 21 publications

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“…However, each feature has different impacts on the process of consumer's purchase decision making [7]. Therefore, it is necessary to find a way to rank features automatically.…”

Section: Related Workmentioning

confidence: 99%

“…One of barriers possibly encountered is that the enormous quantity of user reviews results in an overload of information. Secondly, the consumer reviews that contain abundant user behavior information, for instance, user's opinions, attitudes [7] and preferences [8], are hardly useable. Furthermore, comments are also subjective, since the user's evaluation of the current product will be impacted by the previous purchasing experience.…”

Section: Introductionmentioning

confidence: 99%

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An Extended HITS Algorithm on Bipartite Network for Features Extraction of Online Customer Reviews

2018

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How to acquire useful information intelligently in the age of information explosion has become an important issue. In this context, sentiment analysis emerges with the growth of the need of information extraction. One of the most important tasks of sentiment analysis is feature extraction of entities in consumer reviews. This paper first constitutes a directed bipartite feature-sentiment relation network with a set of candidate features-sentiment pairs that is extracted by dependency syntax analysis from consumer reviews. Then, a novel method called MHITS which combines PMI with weighted HITS algorithm is proposed to rank these candidate product features to find out real product features. Empirical experiments indicate the effectiveness of our approach across different kinds and various data sizes of product. In addition, the effect of the proposed algorithm is not the same for the corpus with different proportions of the word pair that includes the "bad", "good", "poor", "pretty good", "not bad" these general collocation words.

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“…However, each feature has different impacts on the process of consumer's purchase decision making [7]. Therefore, it is necessary to find a way to rank features automatically.…”

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Extended HITS Algorithm on Bipartite Network for Features Extraction of Online Customer Reviews

2018

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“…Where, The similarity of each aspect in two cases is computed by a particular local similarity function. The local similarity values are aggregated by means of a sum of weighted aspects [15]. Local similarities are divided into two types namely symbolic and numeric.…”

Section: Retrieval and Similaritymentioning

confidence: 99%

Weighted Minkowski Similarity Method with CBR for Diagnosing Cardiovascular Disease

Faizal¹,

Hamdani²

2018

ijacsa

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This study implements Case-Based Reasoning (CBR) to make the early diagnosis of cardiovascular disease based on the calculation of the feature similarity of old cases. The features used to match old cases with new ones were age, gender, risk factors and symptoms. The diagnostic process was carried out by entering the case feature into the system, and then the system searched cases having similar features with the new case (retrieve). The level of similarity of each similar case was calculated using weighted Minkowski method. Cases with the highest level of similarity would be adopted as new case solutions. If the value of similarity was <0,8, the revision would be conducted by an expert. The tests result conducted by the expert showed that the system was able to perform the diagnosis correctly. The test results are performed on the sensitivity of 100% and specificity of 83,33%. Meanwhile, the accuracy of 95,83% and the error rate of 4,17% is so that this research is relevant enough to be implemented in the medical area.

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“…A user may show his/her overall preferences on an item through the overall ratings but at the same time, they may have satisfaction level variation in certain aspects. Existing works on the aspect-based review can be divided into the expressed aspects emphasis through the textual based review [18] and the numerical representation of the aspects [19], [20]. However, the textual based aspects emphasis cannot compute the representation of aspects weights interdependencies towards the overall rating.…”

Section: Introductionmentioning

confidence: 99%

Tensorized Neural Network for Multi-Aspect Rating-Based Recommendation

2019

IJRTE

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Generating personalized recommendations is one of the most crucial aspects in Recommender System research area. Most of the researches only focus on the accuracy of recommendation using collaborative filtering that relies on a single overall rating that represents the overall preferences. However, the user may have a different emphasis on different specific aspects that affect the users’ final rating decisions. Therefore, we present a neural network model that utilize multi-aspects ratings using Tensor Factorization to improve the accuracy of personalization, as well as optimizing the dynamic weights of the aspect. To measure the estimated weights for the aspects, we employ the Higher Order Singular Value Decomposition (HOSVD) technique called CANDECOMP/PARAFAC (CP) decomposition that allows for multi-faceted data processing. We then develop the Neural Network with backpropagation error to train the model with different parameter settings and limited computational time. We also use a non-linear activation function in each hidden layer in various settings. The experimental result measured using MAE shows that the proposed model has significantly outperformed the baseline approach in terms of the prediction accuracy. Based on the observation, the performance of rating prediction has been improved by employing the Tensorized Neural Network model and can overcome the problem of local optimum convergence for multi-aspect rating recommendation.

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Collaborative filtering with weighted opinion aspects

Cited by 54 publications

References 21 publications

An Extended HITS Algorithm on Bipartite Network for Features Extraction of Online Customer Reviews

An Extended HITS Algorithm on Bipartite Network for Features Extraction of Online Customer Reviews

Weighted Minkowski Similarity Method with CBR for Diagnosing Cardiovascular Disease

Tensorized Neural Network for Multi-Aspect Rating-Based Recommendation

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