Attentive Aspect Modeling for Review-aware Recommendation

In recent years, many studies extract aspects from user reviews and integrate them with ratings for improving the recommendation performance. The common aspects mentioned in a user's reviews and a product's reviews indicate indirect connections between the user and product. However, these aspect-based methods suffer from two problems. First, the common aspects are usually very sparse, which is caused by the sparsity of user-product interactions and the diversity of individual users' vocabularies. Second, a user's interests on aspects could be different with respect to different products, which are usually assumed to be static in existing methods. In this paper, we propose an Attentive Aspect-based Recommendation Model (AARM) to tackle these challenges. For the first problem, to enrich the aspect connections between user and product, besides common aspects, AARM also models the interactions between synonymous and similar aspects. For the second problem, a neural attention network which simultaneously considers user, product and aspect information is constructed to capture a user's attention towards aspects when examining different products. Extensive quantitative and qualitative experiments show that AARM can effectively alleviate the two aforementioned problems and significantly outperforms several state-of-the-art recommendation methods on top-N recommendation task.Comment: Camera-ready manuscript for TOI

An Ensemble Model-Based Recommendation Approach for Consumer Decision-Making System

A recommendation system can suggest items aligned with diverse user interests by leveraging multiple sources of information. While many recommendation systems heavily rely on the collaborative filtering (CF) approach—where user preference data is combined with others to predict additional items of potential interest—this study introduces a novel weighted recommendation system to enhance consumer decision-making using CF. The methodology includes the development of equations to calculate the weights for both the product and review, as well as to determine the similarity between consumer reviews. To ensemble the model, Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR) are employed in the methodology. The study considers Ensemble Classifiers (RF+SVM+LR) to implement the results, aiming for improved outcomes compared to prior research. The proposed model is trained and tested using an open-source dataset on Kaggle's website. Numerical analysis of the proposed model reveals superior performance, outperforming conventional methods in terms of accuracy (0.821), precision (0.802), recall (0.821), F-measure (0.833), error rate (0.100), and more