Quantifying Aspect Bias in Ordinal Ratings using a Bayesian Approach

User opinions expressed in the form of ratings can influence an individual's view of an item. However, the true quality of an item is often obfuscated by user biases, and it is not obvious from the observed ratings the importance different users place on different aspects of an item. We propose a probabilistic modeling of the observed aspect ratings to infer (i) each user's aspect bias and (ii) latent intrinsic quality of an item. We model multi-aspect ratings as ordered discrete data and encode the dependency between different aspects by using a latent Gaussian structure. We handle the Gaussian-Categorical non-conjugacy using a stick-breaking formulation coupled with P\'{o}lya-Gamma auxiliary variable augmentation for a simple, fully Bayesian inference. On two real world datasets, we demonstrate the predictive ability of our model and its effectiveness in learning explainable user biases to provide insights towards a more reliable product quality estimation.Comment: Accepted for publication in IJCAI 201

Rating Prediction based on Optimal Review Topics: A Proposed Latent Factors-Optimal Topics Hybrid Approach

Rating prediction is an inevitable problem which recommender systems (RS) need to address. Its goal is to accurately predict the rating a user will assign to a particular item. Predictions which utilize numerical ratings and review texts are biased and have low accuracy. Also, existing topic-based rating prediction approaches focus on finding the most preferred items through the identification of latent topics expressed in users’ review texts. Even though the latent topics seem to represent most user review texts, they do not necessarily capture each user’s preferences. The goal of this work is then to develop a more accurate model by considering product review texts analysis so as to gain additional preference knowledge. Hence, a hybrid algorithm that optimizes the latent topics is proposed.  Specifically, the proposed approach finds appropriate weights for the topics of each review text. Rating prediction is critical task for RS because slight performance enhancement of the prediction accuracy results into significant improvements in recommendations. Experimental evaluation over real-world datasets revealed performance improvements of the proposed approach compared to alternative models. The proposed model can be used by RS in various domain such as e-learning, movie and hotel rating

Review-based collaborative recommender system using deep learning methods

Recommender systems have been widely adopted to assist users in purchasing and increasing sales. Collaborative filtering techniques have been identified to be the most popular methods used for the recommendation system. One major drawback of these approaches is the data sparsity problem, which generally leads to low performances of the recommender systems. Recent development has shown that user review texts can be exploited to tackle the issue of data sparsity thereby improving the accuracy of the recommender systems. However, the problem with existing methods for the review-based recommender system is the use of handcrafted features which makes the system less accurate. Thus, to address the above issue, this study proposed collaborative recommender system models that utilize user textual reviews based on deep learning methods for improving predictive performances of recommender systems. To extract the product aspects to mine users‟ opinion, an aspect extraction method was first developed using a Multi-Channel Convolutional Neural Network. An aspect-based recommender system was then designed by integrating the opinions of users based on the product aspects into the collaborative filtering method for the recommendation process. To further improve the predictive performance, the fine-grained user-item interaction based on the aspect-based collaborative method was studied and a sentiment-aware recommender system was also designed using a deep learning method. Extensive series of experiments were conducted on real-world datasets from the Semeval-014, Amazon, and Yelp reviews to evaluate the performances of the proposed models from both the aspect extraction and rating prediction. Experimental results showed that the proposed aspect extraction model performed better than compared methods such as rule-based and the neural network-based approaches, with average gains of 5.2%, 12.0%, and 7.5% in terms of Precision, Recall, and F1 score, respectively. Meanwhile, the proposed aspect-based collaborative methods demonstrated better performances compared to benchmark approaches such as topic modelling techniques with an average improvement of 6.5% and 8.0% in terms of the Root Means Squared Error (RMSE) and Mean Absolute Error (MAE), respectively. Statistical T-test was conducted and the results showed that all the performance improvements were significant at P<0.05. This result indicates the effectiveness of utilizing the multi-channel convolutional neural network for better extraction accuracy. The findings also demonstrate the advantage of utilizing user textual reviews and the deep learning methods for improving the predictive accuracy in recommendation systems

Aspect Based Rating Prediction For Yelp Customer Review

With the wide spread of online review platforms, online reviews and star ratings have been widely used by customers to determine the quality of business. However, overall star rating is not once-for-all measurement in summarizing the review because review writers are likely to differ in how would they evaluate different aspects of the restaurants. In this paper, we attempted to predict the rating a reviewer will assign mainly based on the review text as well as other features derived from the Yelp review dataset. We created dictionaries to identify the combinations on sentence level and incorporated these new features. Our model achieved a fairly high level accuracy (84.61%). The contribution of aspect-related features was also discussed.Master of Science in Information Scienc