Recurrent Naive Bayes for Multi-Criteria Recommender Systems: A Novel Approach for Partial Preference Imputation

Recommender systems provide their users an efficient way to handle information overload problem by offering personalized suggestions. Traditional recommender systems are based on two-dimensional user-item preference matrix constructed depending on the users' overall evaluations over items. However, they have begun to present their preferences under various circumstances. Thus, traditional recommendation techniques fail to process multicriteria ratings during the recommendation process. Multicriteria recommender systems are an extension of traditional recommender systems that utilize multicriteria-based user preferences. Multicriteria recommender systems provide more personalized and accurate predictions compared to traditional recommender systems. However, the increased amount of data dimension causes sparsity to be a major problem of such systems. Especially, the similarity-based multicriteria recommender systems may fail to find similar neighbors to an active user due to the lack of corated items among users. Therefore, we propose a new similarity-based multicriteria collaborative filtering approach based on autoencoders. In order to handle sparsity, the proposed method extracts nonlinear, low-dimensional, dense features from raw and sparse users'/items' preferences. Our experimental outcomes show that the proposed work can amortize the negative impacts of sparsity over the accuracy comparing with the state-of-the-art multicriteria recommendation techniques.

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.

Recommender system is one of the emerging personalization tools in e-commerce domains for suggesting suitable items to users. Traditional collaborative filtering (CF) based recommender systems (RSs) suggest items to users based on the overall ratings to find out similar users. Multi-criteria ratings are used to capture user preferences efficiently in multi-criteria recommender systems (MCRSs), and incorporation of criteria ratings can lead to higher performance in MCRS. However, aggregation of these criteria ratings is a major concern in MCRS. In this paper, we propose a multi-criteria collaborative filtering-based RS by leveraging information derived from multi-criteria ratings through Genetic programming (GP). The proposed system consists of two parts: (1) weights of each user for every criterion are computed through our proposed modified sub-tree crossover in GP process (2) criteria weights are then incorporated in CF process to generate effective recommendations in our proposed system. The obtained results present significant improvements in prediction and recommendation qualities in comparison to heuristic approaches.

Recommender system is a well-known information system which assists decision making by producing recommendations tailored to user preferences. Multi-criteria recommender systems (MCRS) additionally take user preferences in multiple criteria into account, in order to better generate recommendations. The major challenge in MCRS is the process of aggregating user ratings in the multiple criteria. We claim that user preferences in these criteria can be considered as contexts, so that the overall taste on an item can be estimated by a process of context-aware predictions. In this paper, we exploit and summarize different methods which produce the recommendations by using criteria preferences as context information. We examine these methods based on three real-world data sets. Our experimental results demonstrate the effectiveness of these algorithms in the rating prediction task, in comparison with the state-of-the-art multi-criteria recommendation approaches.

In recent years, recommender systems have become a crucial tool, assisting users in discovering and engaging with valuable information and services. Multi-criteria recommender systems have demonstrated significant value in assisting users to identify the most relevant items by considering various aspects of user experiences. Deep learning (DL) models demonstrated outstanding performance across different domains: computer vision, natural language processing, image analysis, pattern recognition, and recommender systems. In this study, we introduce a deep learning model using VAE to improve multi-criteria recommendation systems. Specifically, we propose a variational autoencoder-based model for multi-criteria recommendation systems (VAE-MCRS). The VAE-MCRS model is sequentially trained across multiple criteria to uncover patterns that allow for better representation of user–item interactions. The VAE-MCRS model utilizes the latent features generated by the VAE in conjunction with user–item interactions to enhance recommendation accuracy and predict ratings for unrated items. Experiments carried out using the Yahoo! Movies multi-criteria dataset demonstrate that the proposed model surpasses other state-of-the-art recommendation algorithms, achieving a Mean Absolute Error (MAE) of 0.6038 and a Root Mean Squared Error (RMSE) of 0.7085, demonstrating its superior performance in providing more precise recommendations for multi-criteria recommendation tasks.

Recommender systems have been served to assist decision making by recommending a list of items to the end users. Multi-criteria recommender system (MCRS) is a type of recommender systems which enhance recommendation performance by taking user preferences on multiple criteria. Traditional algorithms for MCRS usually predict user ratings on these criteria, and finally estimate the overall rating by different aggregation functions. In this paper, we propose a new multi-criteria recommendation framework in which we can take advantage of Pareto ranking based estimated preferences on multiple criteria, and infer a ranking score for top-<i>N</i> recommendations. The proposed framework is general enough and all existing algorithms in MCRS can be reused to be integrated with our framework. We demonstrate the effectiveness of the proposed framework by evaluating top-<i>N</i> recommendations over four real-world data sets.

Recommender systems are powerful online tools that help to overcome problems of information overload. They make personalized recommendations to online users using various data mining and filtering techniques. However, most of the existing recommender systems use a single rating to represent the preference of user on an item. These techniques have several limitations as the preference of the user towards items may depend on several attributes of the items. Multi-criteria recommender systems extend the single rating recommendation techniques to incorporate multiple criteria ratings for improving recommendation accuracy. However, modeling the criteria ratings in multi-criteria recommender systems to determine the overall preferences of users has been considered as one of the major challenges in multi-criteria recommender systems. In other words, how to additionally take the multi-criteria rating information into account during the recommendation process is one of the problems of multi-criteria recommender systems. This article presents a methodological framework that trains artificial neural networks with particle swarm optimization algorithms and uses the neural networks for integrating the multi-criteria rating information and determining the preferences of users. The proposed neural network-based multi-criteria recommender system is integrated with k-nearest neighborhood collaborative filtering for predicting unknown criteria ratings. The proposed approach has been tested with a multi-criteria dataset for recommending movies to users. The empirical results of the study show that the proposed model has a higher prediction accuracy than the corresponding traditional recommendation technique and other multi-criteria recommender systems.

In today's internet era, recommender system (RS) addresses information overload problem, which is common in many information driven domains. RS helps users chose a set of appropriate options from a plethora of options. Traditional single rating recommender systems have been playing a vital role over the decades in various domains. However, it is limited in a sense of providing user's accurate preferences about an item or services to the recommendation engine. The single rating recommender systems receive a single rating about an item, due to which these systems are inadequate to understand the reasons behind users' choice of items. On the other hand, multi-criteria rating systems allow the users to share more information about user's interest/ disinterest through multiple criteria of an item. Therefore, the multi-criteria recommender engine gets more information from the users and provides relevant recommendations to the users. In this paper, we propose a novel technique to learn and rank users' preferences over different criteria. Dominant criteria of each item are also learnt and ranked in the proposed technique. The obtained ranks are exploited to predict the overall rating by adapting the traditional user-based and item-based collaborative filtering techniques. We conducted experiments on two real world datasets (TripAdvisor and Yahoo! Movies) and our approach outperforms the traditional single rating systems and existing approaches on multi-criteria recommender systems.

Recommender systems have already been engaging multiple criteria for the production of recommendations. Such systems, referred to as multicriteria recommenders, demonstrated early the potential of applying Multi-Criteria Decision Making (MCDM) methods to facilitate recommendation in numerous application domains. On the other hand, systematic implementation and testing of multicriteria recommender systems in the context of real-life applications still remains rather limited. Previous studies dealing with the evaluation of recommender systems have outlined the importance of carrying out careful testing and parameterization of a recommender system, before it is actually deployed in a real setting. In this paper, the experimental analysis of several design options for three proposed multiattribute utility collaborative filtering algorithms is presented for a particular application context (recommendation of e-markets to online customers), under conditions similar to the ones expected during actual operation. The results of this study indicate that the performance of recommendation algorithms depends on the characteristics of the application context, as these are reflected on the properties of evaluations' data set. Therefore, it is judged important to experimentally analyze various design choices for multicriteria recommender systems, before their actual deployment.

Recommender systems (RSs) are software tools that work as guides by suggesting products to users from a vast catalogue of products. Various approaches and techniques have been developed to provide effective recommendations to users. Classical collaborative filtering (CF) based RSs helps users by providing suggestions based on their overall assessment of items. However, providing suggestions based on their overall assessment is not an efficient way. So, multi-criteria recommender systems (MCRS) came into existence as an extended approach for suggesting products to users based on multiple features of products, and adding these multiple features can enhance the performance of the system. However, aggregation of these feature assessment i.e. feedback provided to multiple criteria is a key issue in MCRS. In this paper, we present a comparative analysis of genetic algorithm (GA) and genetic programming (GP) approaches to aggregate criteria ratings for predicting user preferences in MCRS. These two algorithms are bio-inspired and have great potential to solve optimization problems. In this research, GP and GA are used to solve the aggregation problem in MCRS by estimating weights for each criterion in a system. We compared the results of genetic programming and genetic algorithm approaches to show their effectiveness in multi-criteria rating systems.

Multi-criteria recommender systems have emerged as an important trend in studying recommendation systems. They outperform and have been proved to be more precise than single-criterion systems. On the other hand, deep learning (DL) techniques have shown promising results in various fields. Recently, several studies on single-criterion recommender systems explored DL and sentiment analysis. We propose in this article a novel sentiment DL based algorithm for multi-criteria recommendation using autoencoders and sentiment information. Two sentiment analysis models have been employed, LSTM and LSTM with Word2Vec. Experiments on the TripAdvisor multi-criteria dataset demonstrated the contribution of sentiment information and that our approach outperformed other exiting methods.

To achieve meaningful learning goals, both pedagogues and tutees need frequent supports on how to obtain relevant materials. Recommendation systems have been proved as important tools that assist learners in getting useful learning objects. Nowadays, various recommendation techniques are used to build a system that can find and suggests learning objects to learners. This paper proposed to use a multi-criteria recommendation technique and aggregation function approach for modeling user preferences on learning objects to improve the quality of recommendations given by the existing traditional recommendation systems. The proposed plan is to develop a neural network model and a hybrid of Genetic and Gradient descent algorithms to train the model using real datasets to learn the behavior of the inputs for accurate predictions of learners' preferences.

Recommender systems (RSs) are web-based tools that use various machine learning and filtering methods to propose useful items for users. Several techniques have been used to develop such a system for generating a list of useful recommendations. Traditionally, RSs use a single rating to represent preferences of a user on an item. A multi-criteria recommendation is a new technique that recommends items to users based on multiple attributes of the items. This technique has been used to solve many recommendation problems. Its predictive performance has been tested and proved to be more efficient than the traditional approach. However, this paper presents a model that is based on the architecture and main features of fuzzy sets and systems. Fuzzy logic (FL) is widely known for its application in different fields of study with its main advantage being that it does not need a lot of training data and its ability to combine human heuristics into the computer-assisted decision making process. FL is highly applicable in the domain of RS. The proposed study is to test and provide the predictive performance of the fuzzy-based multi-criteria technique and compare it with a single rating RS. Experimental results on real-world datasets from Yahoo! Movies proved that the proposed technique has remarkably improved the accuracy of the system

For a multi-criteria recommender system (MCRS), a complete set of criteria ratings is necessary to produce an accurate recommendation. Incomplete preferences, known as the "partial preferences problem," is one of the problems in MCRS. This issue affects the performance of MCRS due to an increase in data sparsity. Criteria rating prediction is one method for completing the preferences. Therefore, this study proposes a new method for preferences completion, that is a multi-attribute Bidirectional Encoder Representations from Transformers (BERT). The proposed method incorporates reviews and overall ratings to predict incomplete criteria ratings. Rule-based adjustment is also performed to enhance the performance of the proposed method in predicting the worst rating. This study shows that the proposed method outperforms the baseline method. The proposed method is also evaluated on MCRS using a user-based multi-criteria collaborative filtering approach. The result is that it has a positive impact on the recommendation system.

Adaptive genetic algorithm for user preference discovery in multi-criteria recommender systems