Multi-criteria clustering-based recommendation using Mahalanobis distance

Multi-Criteria Recommender Systems (MCRS) have emerged as a significant advancement over traditional singlecriterion recommendation systems by incorporating multiple dimensions of user preferences. This paper presents a comprehensive comparative analysis of three prominent MCRS approaches: Weighted Sum, TOPSIS, and Fuzzy Logic-Based methods. Using the MovieLens 1M dataset extended with multi-criteria ratings (story, acting, direction), we evaluate these approaches across multiple performance metrics including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Precision, Recall, and F1-Score. Our experimental results demonstrate that while the Fuzzy Logic-Based approach achieves superior performance in handling uncertainty (MAE of 0.65, F1-Score of 0.80), the TOPSIS method provides the best balance between accuracy and computational efficiency. The Weighted Sum method, despite its simplicity, shows limitations in recommendation quality. The study concludes with recommendations for selecting MCRS methods based on specific application requirements and proposes future research directions focusing on hybrid models and deep learning integration.

Credibility score based multi-criteria recommender system

The movie recommender system has an important role in providing movie recommendations for users, but new users have difficulty choosing movies that are given by the recommender system because of the cold start problem. This study aims to overcome the cold start problem using a knowledge-based recommender system, i.e association rule mining using an apriori algorithm. The apriori algorithm aims to extract correlations between product itemsets, but the problem in the apriori algorithm is the large number of association rules that make the complex computation. To overcome this problem, we combine the apriori algorithm and k-means to produce more accurate recommendations, because the items are grouped before the recommendation process using the k-means algorithm. In this study, we use a dataset of movies and ratings from the Kaggle website. This study uses a minimum value of 0.5 confidence, and a minimum value of 4 lifts. To produce the best itemset in the form of antecedents and consequents of the Beauty and the Beast item with The Passion of Joan of Arc which has a value of 0.107981 support, 0.779661 confidence, 4.151695 lift

With the rapid growth of users, traditional collaborative filtering methods continue to struggle with handling data sparsity and cold start issues, leading to significantly reduced recommendation accuracy. To address these persistent challenges, this study proposes a movie recommendation system that integrates Graph Neural Networks (GNNs) with temporal contextual information. GNNs model user-movie interactions as graph structures, with users and movies represented as nodes and their interactions as edges. By incorporating temporal context, the model captures dynamic user preferences that evolve over time, allowing for more personalized and contextaware recommendations. The GNN model achieved an RMSE of 1.51, which further improved to 1.45 with the inclusion of temporal context, demonstrating the crucial role of contextual information in enhancing the system’s ability to predict user behavior. These findings highlight the substantial potential of integrating GNN with contextual data to significantly improve the overall performance of recommendation systems, especially in scenarios characterized by sparse data or limited user-item interactions.

Recommender systems (RSs) have been successfully applied to alleviate the problem of information overload and assist users' decision makings. Multi-criteria recommender systems is one of the RSs which utilizes users' multiple ratings on different aspects of the items (i.e., multi-criteria ratings) to predict user preferences. Traditional approaches usually predict ratings on each criterion individually and aggregate them together to estimate the user preferences. In this paper, we propose an approach named as Criteria Chains, where each combination of the criteria can be utilized in a way of contextual situations in order to better predict the multi-criteria ratings. Our experimental results based on the TripAdvisor and YahooMovies rating data sets demonstrate that our proposed approach is able to improve the performance of multi-criteria item recommendations.

Currently, deep reinforcement learning is widely used to transform the recommendation process into a sequential task. Despite this, the recommendation system continues to be plagued by data sparsity and cold start issues as a result of high online costs and a sparse user-item rating matrix. To address the aforementioned issues, this article proposes a social multi-agent reinforcement learning framework (MATR). Two agents collaborate within the framework to enhance the performance of conventional solutions. Specifically, one agent is responsible for capturing the dynamic preferences of users based on their interaction with the system. The other agent leverages a ubiquitous social network to minimise cold starts and data sparsity for some users. In addition, we designed a module for learning state representations from social networks and user rating matrices. To optimise the use of social networks in the state representation module, in addition to first-order social neighbour features, we used trust inference to discover high-level social neighbour features, and we integrated Feature aggregation modelling state representation with graph convolutional neural networks and attention mechanisms. Our method has been shown to be effective and advantageous using actual data sets.

Research in recommender systems has traditionally focused on improving the predictive accuracy of recommendations by developing new algorithms or by incorporating new sources of data. However, several studies have shown that accuracy does not always correlate with a better user experience, leading to recent research that puts emphasis on Human-Computer Interaction in order to investigate aspects of the interface and user characteristics that influence the user experience on recommender systems. Following this new research this paper presents SetFusion, a visual user-controllable interface for hybrid recommender system. Our approach enables users to manually fuse and control the importance of recommender strategies and to inspect the fusion results using an interactive Venn diagram visualization. We analyze the results of two field studies in the context of a conference talk recommendation system, performed to investigate the effect of user controllability in a hybrid recommender. Behavioral analysis and subjective evaluation indicate that the proposed controllable interface had a positive effect on the user experience.

Multi-behavioral recommender systems have emerged as a solution to address data sparsity and cold-start issues by incorporating auxiliary behaviors alongside target behaviors. However, existing models struggle to accurately capture varying user preferences across different behaviors and fail to account for diverse item preferences within behaviors. Various user preference factors (such as price or quality) entangled in the behavior may lead to sub-optimization problems. Furthermore, these models overlook the personalized nature of user behavioral preferences by employing uniform transformation networks for all users and items. To tackle these challenges, we propose the Disentangled Cascaded Graph Convolutional Network (Disen-CGCN), a novel multi-behavior recommendation model. Disen-CGCN employs disentangled representation techniques to effectively separate factors within user and item representations, ensuring their independence. In addition, it incorporates a multi-behavioral meta-network, enabling personalized feature transformation across user and item behaviors. Furthermore, an attention mechanism captures user preferences for different item factors within each behavior. By leveraging attention weights, we aggregate user and item embeddings separately for each behavior, computing preference scores that predict overall user preferences for items. Our evaluation of benchmark datasets demonstrates the superiority of Disen-CGCN over state-of-the-art models, showcasing an average performance improvement of 7.07% and 9.00% on respective datasets. These results highlight Disen-CGCN’s ability to effectively leverage multi-behavioral data, leading to more accurate recommendations.

A recommender system uses specific algorithms and techniques in order to suggest specific services, goods or other type of recommendations that users could be interested in. User’s preferences or ratings are used as inputs and top-N recommendations are produced by the system. The evaluation of the recommendations is usually based on accuracy metrics such as the Mean Absolute Error (MAE) and the Root Mean Squared Error (RMSE), while on the other hand Precision and Recall is used to measure the quality of the top-N recommendations. Recommender systems development has been mainly focused in the development of new recommendation algorithms. However, one of the major problems in modern offline recommendation system is the sparsity of the datasets and the selection of the suitable users Y that could produce the best recommendations for users X. In this paper, we propose an algorithm that uses Fuzzy sets and Fuzzy norms in order to evaluate the correlation between users in the data set so the system can select and use only the most relevant users. At the same time, we are extending our previous work about Reproduction of experiments in recommender systems by developing new explanations and variables for the proposed new algorithm. Our proposed approach has been experimentally evaluated using a real dataset and the results show that it is really efficient and it can increase both accuracy and quality of recommendations.

Recommender systems are becoming very useful for competitive businesses. It is very important for recommender systems to extract user preferences accurately by utilizing logs that record user behavior. Furthermore, user behavior should be analyzed from multiple aspects, storing the results as multicriteria rating scores. If the rating information is sparse, then systems are forced to compensate. One way to treat sparseness is to use a latent model that maps users and items to a small number of groups. To predict rating scores from such a model, we need to aggregate the data appropriately. This paper proposes a method for combining a latent model with a proposed regression technique. We evaluated the proposed method for the Yahoo! Movie data set and show empirically that the proposed combination improves the recommendation accuracy.

Recommender Systems are used to mitigate the information overload problem in different domains by providing personalized recommendations for particular users based on their implicit and explicit preferences. However, Item-based Collaborative Filtering (CF) techniques, as the most popular techniques of recommender systems, suffer from sparsity and new item limitations which result in producing inaccurate recommendations. The use of items’ semantic information besides the inclusion of multi-criteria ratings can successfully alleviate such problems and generate more accurate recommendations. This paper proposes an Item-based Multi-Criteria Collaborative Filtering algorithm that integrates the items’ semantic information and multi-criteria ratings of items to lessen known limitations of the item-based CF techniques. According to the experimental results, the proposed algorithm prove to be very effective in terms of dealing with both of the sparsity and new item problems and therefore produce more accurate recommendations when compared to standard item-based CF techniques.

Over the past several years, research in recommender systems has emphasized the importance of serendipity, but there is still no consensus on the definition of this concept and whether serendipitous items should be recommended is still not a well-addressed question. According to the most common definition, serendipity consists of three components: relevance, novelty and unexpectedness, where each component has multiple variations. In this paper, we looked at eight different definitions of serendipity and asked users how they perceived them in the context of movie recommendations. We surveyed 475 users of the movie recommender system, MovieLens regarding 2146 movies in total and compared those definitions of serendipity based on user responses. We found that most kinds of serendipity and all the variations of serendipity components broaden user preferences, but one variation of unexpectedness hurts user satisfaction. We found effective features for detecting serendipitous movies according to definitions that do not include this variation of unexpectedness. We also found that different variations of unexpectedness and different kinds of serendipity have different effects on preference broadening and user satisfaction. Among movies users rate in our system, up to 8.5% are serendipitous according to at least one definition of serendipity, while among recommendations that users receive and follow in our system, this ratio is up to 69%.

Predicting process behavior meets factorization machines

Research and development of recommender systems has been a vibrant field for over a decade, having produced proven methods for “preference-aware” computing. Recommenders use community opinion histories to help users identify interesting items from a considerably large search space (e.g., inventory from Amazon [7], movies from Netflix [9]). Personalization, recommendation, and the “human side of data-centric applications are even becoming important topics in the data management community [3]. A popular recommendation method used heavily in practice is collaborative filtering, consisting of two phases: (1) An offline model-building phase that uses community opinions of items (e.g., movie ratings, “Diggs” [6]) to build a model storing meaningful correlations between users and items. (2) An on-demand recommendation phase that uses the model to produce a set of recommended items when requested from a user or application. To be effective, recommender systems must evolve with their content. In current update-intensive systems (e.g., social networks, online news sites), the restriction that a model be generated offline is a significant drawback, as it hinders the system’s ability to evolve quickly. For instance, new users enter the system changing the collective opinions over items, or the system adds new items quickly (e.g., news posts, Facebook postings), which widens the recommendation pool. These updates affect the recommender model, that in turn affect the system’s recommendation quality in terms of providing accurate answers to recommender queries. In such systems, a completely real-time recommendation process is paramount. Unfortunately, most traditional state-of-the-art recommenders are “hand-built, implemented as custom software not built for a real-time recommendation process [1]. Further, for some

This chapter presents a brief and systematic overview of four major advanced recommender systems: group recommender systems, context-aware recommender systems, multi-criteria recommender systems, and cross-domain recommender systems. These advanced recommendations are characterized and compared in a unifying model as extensions of basic recommender systems. Future research topics and directions in the area of advanced personalized recommendations are discussed. Advanced recommender technologies will continue to advance.