Improving Recommender Systems Research Articles

A Heterogeneous Information Networks with an Attention Mechanism for Improving Recommender Systems

A Heterogeneous Information Networks with an Attention Mechanism for Improving Recommender Systems

Enhancing movie recommendations through comparative analysis of UCB algorithm variants

In the digital realm, recommendation systems are pivotal in shaping user experiences on online platforms, tailoring content based on user feedback. A notable algorithm in this domain is the multi-armed bandit algorithm, with the Upper Confidence Bound (UCB) emerging as a classic and effective variant. This paper delves into an array of Upper Confidence Bound algorithm variations, encompassing UCB1, Asymptotically Optimal UCB, UCB-V, and UCB1Tuned. The research harnesses the MovieLens dataset to assess the performance of these algorithms, employing cumulative regret as the primary metric. For l in UCB1 and c in UCB-V, both oversized and undersized parameters will result in negative outcomes. And UCB1Tuned outperforms the other three algorithms in this experiment, since it considers variance and adjusts parameters dynamically. The study demonstrates that setting a appropriate UCB index is crucial for enhancing the performance of the UCB algorithm in recommendation system. It holds significance for both improve recommendation system algorithms and enhance user experience.

SARDINE: Simulator for Automated Recommendation in Dynamic and Interactive Environments

Simulators can provide valuable insights for researchers and practitioners who wish to improve recommender systems, because they allow one to easily tweak the experimental setup in which recommender systems operate, and as a result lower the cost of identifying general trends and uncovering novel findings about the candidate methods. A key requirement to enable this accelerated improvement cycle is that the simulator is able to span the various sources of complexity that can be found in the real recommendation environment that it simulates. With the emergence of interactive and data-driven methods—e.g., reinforcement learning or online and counterfactual learning-to-rank—that aim to achieve user-related goals beyond the traditional accuracy-centric objectives, adequate simulators are needed. In particular, such simulators must model the various mechanisms that render the recommendation environment dynamic and interactive, e.g., the effect of recommendations on the user or the effect of biased data on subsequent iterations of the recommender system. We therefore propose SARDINE, a flexible and interpretable recommendation simulator that can help accelerate research in interactive and data-driven recommender systems. We demonstrate its usefulness by studying existing methods within nine diverse environments derived from SARDINE, and even uncover novel insights about them.

Movie recommendation and classification system using block chain

Recommender Systems are mainly used in various e-commerce applications, especially online stores threatening users’ privacy. The privacy issues can be overcome by using security solutions, which include blockchain technology for privacy applications. The fusion of the Internet of Things and blockchain technology has fully improved modern distributed systems. The combination guarantees the safety and scalability of the recommender system. We aim to create an authorized secure exchange device using blockchain-enabled multiparty computation by adding smart contracts to the core blockchain protocol. The recommendation structure and Blockchain technology make online shopping more convenient and private. We propose a blockchain-related recommender system using the “movielens” data. The case study includes a smart contract model that recommends movies to buyers. Initially, we tested the model on a small “movielens dataset” and extended it to a 3M movielens dataset. We developed a classifier model for movielens and proposed a Dual light graph convolutional network for movielens data classification. Our results, including ablation analysis, show that blockchain strategies and Dual light graph convolutional networks can effectively improve recommender systems’ privacy. Furthermore, the suggested blockchain technique can be stretched by similar procedures.

Dual-view multi-modal contrastive learning for graph-based recommender systems

Personalized recommender systems play a crucial role in various online content-sharing platforms (e.g., TikTok). The learning of representations for multi-modal content is pivotal in current graph-based recommender systems. Existing works aim to enhance recommendation accuracy by leveraging multi-modal features (e.g., image, sound, text) as side information for items. However, this approach falls short in fully discerning users' fine-grained preferences across different modalities. To tackle this limitation, this paper introduces the Dual-view Multi-Modal contrastive learning Recommendation model (DMM-Rec). DMM-Rec employs self-supervised learning to guide the learning of user and item representations within the multi-modal context. Specifically, to capture users' preferences for different modalities, we propose specific-modal contrastive learning. Simultaneously, to capture users' cross-modal preferences, cross-modal contrastive learning is introduced to uncover interdependencies in users' preferences across modalities. The contrastive learning tasks not only adaptively explore potential relations between modalities but also address the data sparsity challenge in recommender systems. Extensive experiments conducted on three datasets and compared against ten baselines demonstrate that DMM-Rec outperforms the strongest baseline by an average of 6.81%. These results underscore the effectiveness of considering multi-modal content in improving recommender systems.

What do my users want? Leveraging users insights to improve recommender systems in eWOM communities.

What do my users want? Leveraging users insights to improve recommender systems in eWOM communities.

Improving Recommender Systems by a Further Factorization of the Factor Matrices

Improving Recommender Systems by a Further Factorization of the Factor Matrices

Deep variational models for collaborative filtering-based recommender systems

Deep learning provides accurate collaborative filtering models to improve recommender system results. Deep matrix factorization and their related collaborative neural networks are the state of the art in the field; nevertheless, both models lack the necessary stochasticity to create the robust, continuous, and structured latent spaces that variational autoencoders exhibit. On the other hand, data augmentation through variational autoencoder does not provide accurate results in the collaborative filtering field due to the high sparsity of recommender systems. Our proposed models apply the variational concept to inject stochasticity in the latent space of the deep architecture, introducing the variational technique in the neural collaborative filtering field. This method does not depend on the particular model used to generate the latent representation. In this way, this approach can be applied as a plugin to any current and future specific models. The proposed models have been tested using four representative open datasets, three different quality measures, and state-of-the-art baselines. The results show the superiority of the proposed approach in scenarios where the variational enrichment exceeds the injected noise effect. Additionally, a framework is provided to enable the reproducibility of the conducted experiments.

Improving collaborative filtering using lexicon-based sentiment analysis

<span>Since data is available increasingly on the Internet, efforts are needed to develop and improve recommender systems to produce a list of possible favorite items. In this paper, we expand our work to enhance the accuracy of Arabic collaborative filtering by applying sentiment analysis to user reviews, we also addressed major problems of the current work by applying effective techniques to handle the scalability and sparsity problems. The proposed approach consists of two phases: the sentiment analysis and the recommendation phase. The sentiment analysis phase estimates sentiment scores using a special lexicon for the Arabic dataset. The item-based and singular value decomposition-based collaborative filtering are used in the second phase. Overall, our proposed approach improves the experiments’ results by reducing average of mean absolute and root mean squared errors using a large Arabic dataset consisting of 63,000 book reviews.</span>

An Effective Algorithm to Improve Recommender Systems using Evolutionary Computation Algorithms and Neural Network

The growing Internet access and easy access to it have resulted in a significant increase in e-content, which, along with many benefits, has caused problems for users. Internet users simply cannot find the content they need from this massive amount of data. Users are faced with a lot of suggestions for choosing goods, buying items, selecting music and videos, and more. Advantage systems can be used to overcome these problems. Today, with the spread of people’s use of cyberspace, such as web sites and social networks, and increasing the need for conscious and clever selection of people, recommender systems has been extensively investigated. Although the neural network can identify the connections between the inputs and outputs of a dataset, but in order to achieve the proper performance of the neural network, a proper structure should be considered. We will use the mantle algorithm to determine this structure. The mantle algorithm is a form of traditional genetic algorithm that uses local search to reduce the time to achieve optimal response. Genetic algorithms are created to search across the search space, while the local search, the neighborhood of the neighborhood, finds every response found by the genetic algorithm to find better answers. This algorithm seeks to find the optimal values for the parameters of the neural network method, so optimal solutions of the memetic algorithm is considered to be used to set parameters for the neural network method. The results of this study show the desirable performance of the proposed approach in this study.

An Improved Recommender System Solution to Mitigate the Over-Specialization Problem Using Genetic Algorithms

Nowadays, recommendation systems offer a method of facilitating the user’s desire. It is useful for recommending items from a variety of areas such as in the e-commerce, medical, education, tourism, and industry domains. The e-commerce area represents the most active research we found, which assists users in locating the things they want. A recommender system can also provide users with helpful knowledge about things that could be of interest. Sometimes, the user gets bored with recommendations which are similar to their profiles, which leads to the over-specialization problem. Over-specialization is caused by limited content data, under which content-based recommendation algorithms suggest goods directly related to the customer profile rather than new things. In this study, we are particularly interested in recommending surprising, new, and unexpected items that may likely be enjoyed by users and will mitigate this limited content. In order to recommend novel and serendipitous items along with familiar items, we need to introduce additional hacks and note of randomness, which can be achieved using genetic algorithms that brings diversity to recommendations being made. This paper describes a Revolutionary Recommender System using a Genetic Algorithm called RRSGA which improves the fitness functions for recommending optimal results. The proposed approach employs a genetic algorithm to address the over-specialization issue of content-based filtering. The proposed method aims to incorporate genetic algorithms that bring variety to recommendations and efficiently adjust and suggest unpredictable and innovative things to the user. Experiments objectively demonstrate that our technology can recommend additional products that every consumer is likely to appreciate. The results of RRSGA have been compared against recommendation results from the content-based filtering approach. The results indicate the effectiveness of RRSGA and its capacity to make more accurate predictions than alternative approaches.

Improving recommender system via knowledge graph based exploring user preference

Improving recommender system via knowledge graph based exploring user preference

A model to improve recommendation systems based on Spark platform

A model to improve recommendation systems based on Spark platform

A model to improve Recommendation Systems based on Spark platform

A model to improve Recommendation Systems based on Spark platform

A model to improve recommendation systems based on Spark platform

With the popularity of using the internet, the evolution of technology, the rapid growth of data volume, and applying recommendation systems in diverse life domains, adopting recommendation systems became indispensable. Most of the traditional recommendation systems are built un-oriented of real-time data challenges (such as item cold-start, scalability, heterogeneous data, items change, and users' opinions change), resulting in ineffective, unfulfilling recommendations. Therefore, what is needed is a real-time-based recommendation model that can make the user find what s/he needs without even searching for it; it's like building a model aware of the users' nature and behaviour. This paper aims at building a recommendation model that fulfils the user requirements and overcomes the shortcomings of the traditional recommendation systems. The proposed recommendation model is built on Apache Spark, Apache Kafka and alternative least square (ALS) algorithm, and the model is divided into two parts: recommendation system and real-time analytics dashboard. An e-commerce website is chosen as a case study to apply our model and the results are presented.

A similarity measure model based on the dissimilarity degree between users

This article offers a new users-similarity measure to improve recommender systems’ accuracy even when a few ratings are available. This model is based on the degree of dissimilarity between common ratings. On real data sets, we conducted our experiments to evaluate the proposed model’s accuracy. Experiments demonstrate the model’s capacity to locate a suitable neighborhood, resolve the user’s cold-start problem, and significantly improve recommendation accuracy. Thus, this research may be considered a supplement to previous studies on user cold-start and good neighbor selection difficulties in user-based collaborative filtering algorithms.

Improving recommender systems via a Dual Training Error based Correction approach

We propose a method to improve the prediction performance of recommender systems via a Dual (user anditem) Training Error based Correction approach (DTEC). The proposed method is applied to the Synthetic Coordinate Recommendation system (SCoR) (Papadakis et al., 2017) and to other Ithree state-of-the-art systems. Initially, a recommender system is used Ito provide recommendations for users and items. Subsequently, we introduce a second stage, after initial execution of the recommender system, that improves its predictions taking into account the error in the training set between users and items and their similarity. These corrections can be performed from both user and item viewpoints, and finally a dual system is proposed that efficiently combines both corrections. DTEC computes a model that makes zero the recommendation error in the training set, and then applies it on the test set to improve the rating predictions. The proposed DTEC approach is applicable Ito any model-based recommender system with positive training error, potentially increasing the accuracy of the recommendations. The experimental results demonstrate the efficiency and high performance of DTEC on four well-known, real-world datasets.

A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects

Recommender systems have significantly developed in recent years in parallel with the witnessed advancements in both internet of things (IoT) and artificial intelligence (AI) technologies. Accordingly, as a consequence of IoT and AI, multiple forms of data are incorporated in these systems, e.g. social, implicit, local and personal information, which can help in improving recommender systems’ performance and widen their applicability to traverse different disciplines. On the other side, energy efficiency in the building sector is becoming a hot research topic, in which recommender systems play a major role by promoting energy saving behavior and reducing carbon emissions. However, the deployment of the recommendation frameworks in buildings still needs more investigations to identify the current challenges and issues, where their solutions are the keys to enable the pervasiveness of research findings, and therefore, ensure a large-scale adoption of this technology. Accordingly, this paper presents, to the best of the authors’ knowledge, the first timely and comprehensive reference for energy-efficiency recommendation systems through (i) surveying existing recommender systems for energy saving in buildings; (ii) discussing their evolution; (iii) providing an original taxonomy of these systems based on specified criteria, including the nature of the recommender engine, its objective, computing platforms, evaluation metrics and incentive measures; and (iv) conducting an in-depth, critical analysis to identify their limitations and unsolved issues. The derived challenges and areas of future implementation could effectively guide the energy research community to improve the energy-efficiency in buildings and reduce the cost of developed recommender systems-based solutions.

Course Recommender System Architecture with Sentiment Score

Recommender systems are widely used these days for recommendation of courses. The recommender systems have gained acceptance among users because of their better results. This paper proposes a novel model on optimizing recommender system using sentiment score as a booster method. TOPSYS method is best suited for multi-criteria decision making (MCDM) problems. Sentiment Analysis has been added to existing recommender systems in the form of sentiment score for improving the recommendations. The sentiment analysis will add novelty and will eventually improve recommender system.

Personalized recommendation by matrix co-factorization with multiple implicit feedback on pairwise comparison

Recommendation systems have been tremendously important to assist users to find relevant items. With the information-overloaded problem, it becomes crucial to understand users’ behavior by learning their preferences during the interaction to construct a profile for exploitation in selecting relevant items. Relevant feedback to capture the users’ behavior may not only explicitly exist but also implicitly available. In the real world, it is common that explicit feedback may be unavailable, and the recommender systems rely only on implicit feedback. When only implicit feedback exists, there are interpretability issues on performing recommender systems. In addition, multiple implicit feedback may cause diverse interpretability due to different characteristics and distributions. This study aims to propose a decomposition approach by incorporating joint information rating to improve recommender systems. In prior to the development of the decomposition approach, we develop a framework to explore the proper rating transformation on multiple implicit feedback. The best rating transformation approach is evaluated using the traditional recommender systems and is used as the input for the joint information rating in the decomposition approach using matrix co-factorization. The proposed matrix co-factorization incorporates multiple implicit feedbacks (i.e., frequency and duration). The result proves that incorporating multiple implicit feedbacks with matrix co-factorization improves the recommendation quality.