Rating-based Recommender Research Articles

Empowering Recommendations with NLP: Exploiting Textual Reviews for Enhanced Rating-Based Systems

This research paper proposes a rating-based recommender system that leverages Natural Language Processing (NLP) techniques to enhance the accuracy and effectiveness of recommendations. Traditional recommender systems primarily rely on numerical ratings provided by users to make predictions. However, these ratings often lack detailed information about user preferences and suffer from sparsity and inconsistency issues. By incorporating NLP, we aim to extract valuable insights from textual reviews and improve the recommendation process. Our system utilizes sentiment analysis, topic modelling, and text embeddings to capture the implicit information in reviews and generate more personalized and context-aware recommendations. The experimental results demonstrate the superior performance of the proposed rating-based recommender system compared to conventional approaches.

An analytical study of folk musical instruments of the rural area of Tripura

A music library or music application's listening and search experiences can be greatly enhanced by music recommendation systems. There is simply too much music available in the market for a user to efficiently navigate tens of millions of songs. The demand for good music recommendations is so great that the field of MRS (Music Recommendation Systems) is growing quickly. Extraction of relevant information from user reviews of instrumental music was the primary motivation behind the development of the rating-based recommendation system. On the passage of times most of instruments are not being used by the new generation people as a result, these musical instruments are being wiped out from the world. The research discusses the main features of the performance of Folk Instrumental Music (FIM). In public concert Tripura conditions, it addresses different components of the FIM conducted. The research also examines the components historically recommended by FIM performers and the results provided by music in performances such as raga mood, tranquilly, scalability and astonishments.

Rating-Based Recommender System Based on Textual Reviews Using IoT Smart Devices

Recommender system (RS) is a unique type of information clarification system that anticipates the user's evaluation of items from a large pool based on the expectations of a single stakeholder. The proposed system is highly useful for getting expected meaning suggestions and guidance for choosing the proper product using artificial intelligence and IoT (Internet of Things) such as chatbot. The current proposed technique makes it easier for stakeholders to make context-based decisions that are optimal rather than reactive, such as which product to buy, news classification based on high filtering views, highly recommended wanted music to choose, and desired product to choose. Recommendation systems are a critical tool for obtaining verified information and making accurate decisions. As a result, operational efficiency would skyrocket, and the risk to the company that uses a recommender system would plummet. This proposed solution can be used in a variety of applications such as commercial hotels OYO and other hotels, hospitals (GYAN), public administrative applications banks HDFC, and ICICI to address potential questions on the spot using intelligence computing as a recommendation system. The existing RS is considering a few factors such as buying records, classification or clustering items, and user's geographic location. Collaborative filtering algorithms (CFAs) are much more common approaches for cooperating to mesh the respective documents they retrieved from the historical data. CFAs are distinguished in plenty of features that are uncommon from other algorithms. In this existing system classification, precision and efficiency and error rate are statistical measurements that need to be enhanced according to the current need to fit for global requirements. The proposed work deals with enhancing accuracy levels of text reviews with the recommender system while interacting by the numerous users for their domains. The authors implemented the recommender system using a user-based CF method and presented the significance of collaborative filtering on the movie domain with a recommender system. This whole experiment has been implanted using the RapidMiner Java-based tool. Results have been compared with existing algorithms to differentiate the efficiency of the current proposed approach.

Evaluating Annotated Dataset of Customer Reviews for Aspect Based Sentiment Analysis

Sentiment analysis of product reviews on e-commerce platforms aids in determining the preferences of customers. Aspect-based sentiment analysis (ABSA) assists in identifying the contributing aspects and their corresponding polarity, thereby allowing for a more detailed analysis of the customer’s inclination toward product aspects. This analysis helps in the transition from the traditional rating-based recommendation process to an improved aspect-based process. To automate ABSA, a labelled dataset is required to train a supervised machine learning model. As the availability of such dataset is limited due to the involvement of human efforts, an annotated dataset has been provided here for performing ABSA on customer reviews of mobile phones. The dataset comprising of product reviews of Apple-iPhone11 has been manually annotated with predefined aspect categories and aspect sentiments. The dataset’s accuracy has been validated using state-of-the-art machine learning techniques such as Naïve Bayes, Support Vector Machine, Logistic Regression, Random Forest, K-Nearest Neighbor and Multi Layer Perceptron, a sequential model built with Keras API. The MLP model built through Keras Sequential API for classifying review text into aspect categories produced the most accurate result with 67.45 percent accuracy. K- nearest neighbor performed the worst with only 49.92 percent accuracy. The Support Vector Machine had the highest accuracy for classifying review text into aspect sentiments with an accuracy of 79.46 percent. The model built with Keras API had the lowest 76.30 percent accuracy. The contribution is beneficial as a benchmark dataset for ABSA of mobile phone reviews.

Fairness metrics and bias mitigation strategies for rating predictions

Algorithm fairness is an established line of research in the machine learning domain with substantial work while the equivalent in the recommender system domain is relatively new. In this article, we consider rating-based recommender systems which model the recommendation process as a prediction problem. We consider different types of biases that can occur in this setting, discuss various fairness definitions, and also propose a novel bias mitigation strategy to address potential unfairness in a rating-based recommender system. Based on an analysis of fairness metrics used in machine learning and a discussion of their applicability in the recommender system domain, we map the proposed metrics from the two domains and identify commonly used concepts and definitions of fairness. Finally, to address unfairness and potential bias against certain groups in a recommender system, we develop a bias mitigation algorithm and conduct case studies on one synthetic and one empirical dataset to show its effectiveness. Our results show that unfairness can be significantly lowered through our approach and that bias mitigation is a fruitful area of research for recommender systems.

Deep collaborative filtering with social promoter score-based user-item interaction: a new perspective in recommendation

Most of the existing recommender systems understand the preference level of users based on user-item interaction ratings. Rating-based recommendation systems mostly ignore negative users/reviewers (who give poor ratings). There are two types of negative users. Some negative users give negative or poor ratings randomly, and some negative users give ratings according to the quality of items. Some negative users, who give ratings according to the quality of items, are known as reliable negative users, and they are crucial for a better recommendation. Similar characteristics are also applicable to positive users. From a poor reflection of a user to a specific item, the existing recommender systems presume that this item is not in the user’s preferred category. That may not always be correct. We should investigate whether the item is not in the user’s preferred category, whether the user is dissatisfied with the quality of a favorite item or whether the user gives ratings randomly/casually. To overcome this problem, we propose a Social Promoter Score (SPS)-based recommendation. We construct two user-item interaction matrices with users’ explicit SPS value and users’ view activities as implicit feedback. With these matrices as inputs, our attention layer-based deep neural model deepCF_SPS learns a common low-dimensional space to present the features of users and items and understands the way users rate items. Extensive experiments on online review datasets present that our method can be remarkably futuristic compared to some popular baselines. The empirical evidence from the experimental results shows that our model is the best in terms of scalability and runtime over the baselines.

Hypothetical tensor-based multi-criteria recommender system for new users with partial preferences

Multi-Criteria Recommender Systems (MCRSs) have been developed to improve the accuracy of single-criterion rating-based recommender systems that could not express and reflect users? fine-grained rating behaviors. In most MCRSs, new users are asked to express their preferences on multi-criteria of items, to address the cold-start problem. However, some of the users? preferences collected are usually not complete due to users? cognitive limitation and/or unfamiliarity on item domains, which is called ?partial preferences?. The fundamental challenge and then negatively affects to accurately recommend items according to users? preferences through MCRSs. In this paper, we propose a Hypothetical Tensor Model (HTM) to leverage auxiliary data complemented through three intuitive rules dealing with user?s unfamiliarity. First, we find four patterns of partial preferences that are caused by users? unfamiliarity. And then the rules are defined by considering relationships between multi-criteria. Lastly, complemented preferences are modeled by a tensor to maintain an inherent structure of and correlations between the multi-criteria. Experiments on a TripAdvisor dataset showed that HTM improves MSE performances from 40 to 47% by comparing with other baseline methods. In particular, effectivenesses of each rule regarding multi-criteria on HTM are clearly revealed.

Tensorized Neural Network for Multi-Aspect Rating-Based Recommendation

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.

User activity measurement in rating-based online-to-offline (O2O) service recommendation

The increasing popularity of O2O service make more and more people begin seeking and booking services online. After that, they experience the services in brick-and-mortar stores. This new business model has marketing potential and offer various opportunities to different industries. Consequently, various O2O services starting to appear, which results in difficult service selections for customers. Therefore, in this paper, we proposed a novel rating-based O2O service recommendation model considering user activity. In this method, the traditional similarity estimations are substituted by user activity which can better reflect the differentiations of customers’ behavioral characteristics. Therefore, recommendations are more accurate. The experimental results show that proposed method outperforms rating-based methods, including widely used collaborative filtering methods and state-of-the-art matrix methods. In addition, we find the optimal parameter values of our model, and explore the influence of Top-k on rating-based recommendation.

Leveraging Image Visual Features in Content-Based Recommender System

Content-based (CB) and collaborative filtering (CF) recommendation algorithms are widely used in modern e-commerce recommender systems (RSs) to improve user experience of personalized services. Item content features and user-item rating data are primarily used to train the recommendation model. However, sparse data would lead such systems unreliable. To solve the data sparsity problem, we consider that more latent information would be imported to catch users’ potential preferences. Therefore, hybrid features which include all kinds of item features are used to excavate users’ interests. In particular, we find that the image visual features can catch more potential preferences of users. In this paper, we leverage the combination of user-item rating data and item hybrid features to propose a novel CB recommendation model, which is suitable for rating-based recommender scenarios. The experimental results show that the proposed model has better recommendation performance in sparse data scenarios than conventional approaches. Besides, training offline and recommendation online make the model has higher efficiency on large datasets.

A trust-based recommendation method using network diffusion processes

A variety of rating-based recommendation methods have been extensively studied including the well-known collaborative filtering approaches and some network diffusion-based methods, however, social trust relations are not sufficiently considered when making recommendations. In this paper, we contribute to the literature by proposing a trust-based recommendation method, named CosRA+T, after integrating the information of trust relations into the resource-redistribution process. Specifically, a tunable parameter is used to scale the resources received by trusted users before the redistribution back to the objects. Interestingly, we find an optimal scaling parameter for the proposed CosRA+T method to achieve its best recommendation accuracy, and the optimal value seems to be universal under several evaluation metrics across different datasets. Moreover, results of extensive experiments on the two real-world rating datasets with trust relations, Epinions and FriendFeed, suggest that CosRA+T has a remarkable improvement in overall accuracy, diversity and novelty. Our work takes a step towards designing better recommendation algorithms by employing multiple resources of social network information.

NARRMDA: negative-aware and rating-based recommendation algorithm for miRNA-disease association prediction.

An increasing amount of evidence indicates that microRNAs (miRNAs) are closely related to many important biological processes and play a significant role in various human diseases. More and more researchers have begun to seek effective methods to predict potential miRNA-disease associations. However, reliable computational methods to predict potential disease-related miRNAs are lacking. In this study, we developed a new miRNA-disease association prediction model called Negative-Aware and rating-based Recommendation algorithm for miRNA-Disease Association prediction (NARRMDA) based on the known miRNA-disease associations in the HMDD database, miRNA functional similarity, disease semantic similarity and Gaussian interaction profile kernel similarity. NARRMDA combined a rating-based recommendation algorithm and a negative-aware algorithm to score and rank miRNAs without known associations with investigated diseases. Furthermore, we used leave-one-out cross validation to evaluate the accuracy of NARRMDA and compared NARRMDA with four previous classical prediction models (RLSMDA, HDMP, RWRMDA and MCMDA). As it turned out, NARRMDA and the other four prediction models achieved AUCs of 0.8053, 0.6953, 0.7702, 0.7891 and 0.7718, respectively, which proved that NARRMDA has superior performance of prediction accuracy. Furthermore, we verified the prediction results associated with colon neoplasms, esophageal neoplasms, lymphoma and breast neoplasms by two different validation schemas. In these case studies, 92%, 84%, 92%, and 100% of the top 50 potential miRNAs for these four diseases were confirmed by experimental discoveries, respectively. These results further show that NARRMDA has reliable performance of prediction ability.

Harnessing the power of social bookmarking for improving tag-based recommendations

Social bookmarking and tagging has emerged a new era in user collaboration. Collaborative Tagging allows users to annotate content of their liking, which via the appropriate algorithms can render useful for the provision of product recommendations. It is the case today for tag-based algorithms to work complementary to rating-based recommendation mechanisms to predict the user liking to various products. In this paper we propose an alternative algorithm for computing personalized recommendations of products, that uses exclusively the tags provided by the users. Our approach is based on the idea of using the semantic similarity of the user-provided tags for clustering them into groups of similar meaning. Afterwards, some measurable characteristics of users’ Annotation Competency are combined with other metrics, such as user similarity, for computing predictions. The evaluation on data used from a real-world collaborative tagging system, citeUlike, confirmed that our approach outperforms the baseline Vector Space model, as well as other state of the art algorithms, predicting the user liking more accurately.

Improving Ranking-based Recommendation by Social Information and Negative Similarity

Abstract Recommender system is able to suggest items that are likely to be preferred by the user. Traditional recommendation algorithms use the predicted rating scores to represent the degree of user preference, called rating-based recommendation methods. Recently, ranking-based algorithms have been proposed and widely used, which use ranking to present the user preference rather than rating scores. In this paper, we propose two novel methods to overcome the weaknesses in VSRank, a state-of-the-art ranking-based algorithm. Firstly, a novel similarity measure is proposed to make better use of negative similarity; secondly, social network information is integrated into the model to smooth ranking. Experimental results on a publicly available dataset demonstrate that the proposed methods outperform the existing widely used ranking-based algorithms and rating-based algorithms considerably.

Leveraging multi-criteria customer feedback for satisfaction analysis and improved recommendations

Travel websites and online booking platforms represent today’s major sources for customers when gathering information before a trip. In particular, community-provided customer reviews and ratings of various tourism services represent a valuable source of information for trip planning. With respect to customer ratings, many modern travel and tourism platforms—in contrast to several other e-commerce domains—allow customers to rate objects along multiple dimensions and thus to provide more fine-granular post-trip feedback on the booked accommodation or travel package. In this paper, we first show how this multi-criteria rating information can help to obtain a better understanding of factors driving customer satisfaction for different segments. For this purpose, we performed a Penalty-Reward contrast analysis on a data set from a major tourism platform, which reveals that customer segments significantly differ in the way the formation of overall satisfaction can be explained. Beyond the pure identification of segment-specific satisfaction factors, we furthermore show how this fine-granular rating information can be exploited to improve the accuracy of rating-based recommender systems. In particular, we propose to utilize user- and object-specific factor relevance weights which can be learned through linear regression. An empirical evaluation on datasets from different domains finally shows that our method helps us to predict the customer preferences more accurately and thus to develop better online recommendation services.