Area Of Recommender Systems Research Articles

An acronym for Internet Movie Database is an online database of information related to films, television, series

Abstract: Movie recommender systems are meant to give suggestions to the users based on the features they love the most. A highly performing movie recommendation will suggest movies that match the similarities with the highest degree of performance. This study conducts a systematic literature review on movie recommender systems. It highlights the filtering criteria in the recommender systems, algorithms implemented in movie recommender systems, the performance measurement criteria, the challenges in implementation, and recommendations for future research. Some of the most popular machine learning algorithms used in movie recommender systems such as K-means clustering, principal component analysis, and self-organizing maps with principal component analysis are discussed in detail. Special emphasis is given to research works performed using metaheuristic-based recommendation systems. The research aims to bring to light the advances made in developing the movie recommender systems, and what needs to be performed to reduce the current challenges in implementing the feasible solutions. The article will be helpful to researchers in the broad area of recommender systems as well as practicing data scientists involved in the implementation of such systems. Index-terms : movie recommender, filtering techniques, performance metrics, K-means, metaheuristics

A comparison of embedding aggregation strategies in drug–target interaction prediction

The prediction of interactions between novel drugs and biological targets is a vital step in the early stage of the drug discovery pipeline. Many deep learning approaches have been proposed over the last decade, with a substantial fraction of them sharing the same underlying two-branch architecture. Their distinction is limited to the use of different types of feature representations and branches (multi-layer perceptrons, convolutional neural networks, graph neural networks and transformers). In contrast, the strategy used to combine the outputs (embeddings) of the branches has remained mostly the same. The same general architecture has also been used extensively in the area of recommender systems, where the choice of an aggregation strategy is still an open question. In this work, we investigate the effectiveness of three different embedding aggregation strategies in the area of drug–target interaction (DTI) prediction. We formally define these strategies and prove their universal approximator capabilities. We then present experiments that compare the different strategies on benchmark datasets from the area of DTI prediction, showcasing conditions under which specific strategies could be the obvious choice.

Attributed network embedding based on self-attention mechanism for recommendation method

Network embedding is a technique used to learn a low-dimensional vector representation for each node in a network. This method has been proven effective in network mining tasks, especially in the area of recommendation systems. The real-world scenarios often contain rich attribute information that can be leveraged to enhance the performance of representation learning methods. Therefore, this article proposes an attribute network embedding recommendation method based on self-attention mechanism (AESR) that caters to the recommendation needs of users with little or no explicit feedback data. The proposed AESR method first models the attribute combination representation of items and then uses a self-attention mechanism to compactly embed the combination representation. By representing users as different anchor vectors, the method can efficiently learn their preferences and reconstruct them with few learning samples. This achieves accurate and fast recommendations and avoids data sparsity problems. Experimental results show that AESR can provide personalized recommendations even for users with little explicit feedback information. Moreover, the attribute extraction of documents can effectively improve recommendation accuracy on different datasets. Overall, the proposed AESR method provides a promising approach to recommendation systems that can leverage attribute information for better performance.

Creating synthetic datasets for collaborative filtering recommender systems using generative adversarial networks

Research and education in machine learning requires diverse, representative, and open datasets that contain sufficient samples to handle the necessary training, validation, and testing tasks. Currently, the Recommender Systems area includes a large number of subfields in which accuracy and beyond-accuracy quality measures are continuously being improved. To feed this research variety, it is both necessary and convenient to reinforce the existing datasets with synthetic ones. This paper proposes a Generative Adversarial Network (GAN)-based method to generate collaborative filtering datasets in a parameterized way by selecting their preferred number of users, items, samples, and stochastic variability. This parameterization cannot be performed using regular GANs. Our GAN model is fed with dense, short, and continuous embedding representations of items and users, instead of sparse, large, and discrete vectors, to ensure fast and accurate learning, as compared to the traditional approach based on large and sparse input vectors. The proposed architecture includes a DeepMF model to extract the dense user and item embeddings and a clustering process to convert the dense GAN generated samples to the discrete and sparse samples necessary to create each required synthetic dataset. The results from three different source datasets show adequate distributions and expected quality values and evolutions in the generated datasets compared to the source datasets. Synthetic datasets and source codes are available to researchers.

ClayRS: An end-to-end framework for reproducible knowledge-aware recommender systems

Knowledge-aware recommender systems represent one of the most innovative research directions in the area of recommender systems, aiming at giving meaning to information expressed in natural language and obtaining a deeper comprehension of the information conveyed by textual content.Though rich and constantly evolving, the literature on knowledge-aware recommender systems is particularly scattered when considering software libraries. This makes it difficult to easily exploit advanced content representation and implement replicable experimental protocols. Accordingly, this work aims to fill in these gaps by introducing ClayRS, an end-to-end framework for replicable knowledge-aware recommender systems. ClayRS provides researchers and practitioners with the most recent state-of-the-art methodologies to build knowledge-aware content representations and also includes methods to exploit these representations in content-based recommendation algorithms. Finally, the structure of the framework also allows for building replicable pipelines to push forward the current research in the area and to develop accountable recommender systems.

The Influence of Personality Traits on User Interaction with Recommendation Interfaces

Users’ personality traits can take an active role in affecting their behavior when they interact with a computer interface. However, in the area of recommender systems (RS) , though personality-based RS has been extensively studied, most works focus on algorithm design, with little attention paid to studying whether and how the personality may influence users’ interaction with the recommendation interface. In this manuscript, we report the results of a user study (with 108 participants) that not only measured the influence of users’ personality traits on their perception and performance when using the recommendation interface but also employed an eye-tracker to in-depth reveal how personality may influence users’ eye-movement behavior. Moreover, being different from related work that has mainly been conducted in a single product domain, our user study was performed in three typical application domains (i.e., electronics like smartphones, entertainment like movies, and tourism like hotels). Our results show that mainly three personality traits, i.e., Openness to experience , Conscientiousness , and Agreeableness , significantly influence users’ perception and eye-movement behavior, but the exact influences vary across the domains. Finally, we provide a set of guidelines that might be constructive for designing a more effective recommendation interface based on user personality.

Rethinking Collaborative Metric Learning: Toward an Efficient Alternative Without Negative Sampling.

The recently proposed Collaborative Metric Learning (CML) paradigm has aroused wide interest in the area of recommendation systems (RS) owing to its simplicity and effectiveness. Typically, the existing literature of CML depends largely on the negative sampling strategy to alleviate the time-consuming burden of pairwise computation. However, in this work, by taking a theoretical analysis, we find that negative sampling would lead to a biased estimation of the generalization error. Specifically, we show that the sampling-based CML would introduce a bias term in the generalization bound, which is quantified by the per-user Total Variance (TV) between the distribution induced by negative sampling and the ground truth distribution. This suggests that optimizing the sampling-based CML loss function does not ensure a small generalization error even with sufficiently large training data. Moreover, we show that the bias term will vanish without the negative sampling strategy. Motivated by this, we propose an efficient alternative without negative sampling for CML named Sampling-Free Collaborative Metric Learning (SFCML), to get rid of the sampling bias in a practical sense. Finally, comprehensive experiments over seven benchmark datasets speak to the supriority of the proposed algorithm.

Understanding and mitigating multi-sided exposure bias in recommender systems

Masoud Mansoury is a postdoctoral researcher at Amsterdam Machine Learning Lab at University of Amsterdam, Netherlands. He is also a member of Discovery Lab collaborating with Data Science team at Elsevier Company in the area of recommender systems. Masoud received his PhD in Computer and Information Science from Eindhoven University of Technology, Netherlands, in 2021. He has published his research works in top conferences such as FAccT, RecSys, and CIKM. His research interests include recommender systems, algorithmic bias, and contextual bandits. This research conducted by Masoud Mansoury investigated the impact of unfair recommendations on different actors in the system and proposed solutions to tackle the unfairness of recommendations. The solutions were a rating transformation technique that works as a pre-processing step before recommendation generation and a general graph-based solution that works as a post-processing approach after recommendation generation for mitigating the multi-sided exposure bias in the recommendation results. For evaluation, he introduced several metrics for measuring the exposure fairness for items and suppliers, and showed that the proposed metrics better capture the fairness properties in the recommendation results. Extensive experiments on different publicly-available datasets confirmed the superiority of the proposed solutions in improving the exposure fairness for items and suppliers.

Movie Recommender Systems: Concepts, Methods, Challenges, and Future Directions.

Movie recommender systems are meant to give suggestions to the users based on the features they love the most. A highly performing movie recommendation will suggest movies that match the similarities with the highest degree of performance. This study conducts a systematic literature review on movie recommender systems. It highlights the filtering criteria in the recommender systems, algorithms implemented in movie recommender systems, the performance measurement criteria, the challenges in implementation, and recommendations for future research. Some of the most popular machine learning algorithms used in movie recommender systems such as K-means clustering, principal component analysis, and self-organizing maps with principal component analysis are discussed in detail. Special emphasis is given to research works performed using metaheuristic-based recommendation systems. The research aims to bring to light the advances made in developing the movie recommender systems, and what needs to be performed to reduce the current challenges in implementing the feasible solutions. The article will be helpful to researchers in the broad area of recommender systems as well as practicing data scientists involved in the implementation of such systems.

Interpretable Model for Collaborative Filtering Using an Extended Latent Dirichlet Allocation Approach

With the increasing use of AI and ML-based systems, interpretability is becoming an increasingly important issue to ensure user trust and safety. This also applies to the area of recommender systems, where methods based on matrix factorization (MF) are among the most popular methods for collaborative filtering tasks with implicit feedback. Despite their simplicity, the latent factors of users and items lack interpretability in the case of the effective, unconstrained MF-based methods. In this work, we propose an extended latent Dirichlet Allocation model (LDAext) that has interpretable parameters such as user cohorts of item preferences and the affiliation of a user with different cohorts. We prove a theorem on how to transform the factors of an unconstrained MF model into the parameters of LDAext. Using this theoretical connection, we train an MF model on different real-world data sets, transform the latent factors into the parameters of LDAext and test their interpretation in several experiments for plausibility. Our experiments confirm the interpretability of the transformed parameters and thus demonstrate the usefulness of our proposed approach.

A survey of recommender systems with multi-objective optimization

Recommender systems have been widely applied to several domains and applications to assist decision making by recommending items tailored to user preferences. One of the popular recommendation algorithms is the model-based approach which optimizes a specific objective to improve the recommendation performance. These traditional recommendation models usually deal with a single objective, such as minimizing the prediction errors or maximizing the ranking quality of the recommendations. In recent years, there is an emerging demand for multi-objective recommender systems in which multiple objectives are considered and the recommendations can be optimized by the multi-objective optimization. For example, a recommendation model may be built by optimizing multiple metrics, such as accuracy, novelty and diversity of the recommendations. The multi-objective optimization methodologies have been well developed and applied to the area of recommender systems. In this article, we provide a comprehensive literature review of the multi-objective recommender systems. Particularly, we identify the circumstances in which a multi-objective recommender system could be useful, summarize the methodologies and evaluation approaches in these systems, point out existing challenges or weaknesses, finally provide the guidelines and suggestions for the development of multi-objective recommender systems.

Generating decision support for alarm processing in cold supply chains using a hybrid k-NN algorithm

Real-time temperature monitoring is necessary in cold pharmaceutical supply chains (SCs), where exposures to extreme temperatures can lead to product quality deterioration. Temperature alarms (TAs) triggered by the current rule-based systems still require lengthy examinations before a suitable corrective measure (CM) can be chosen. However, provision of additional information relevant to TAs can expedite the examination process.In the related areas of recommender systems and false alarm/anomaly detection, k-nearest neighbors (k-NN) algorithm has proven to be successful because of its interpretability and ease of use. However, in the context of TA processing, it may suffer from some inherent limitations (i.e., varying neighborhood radius, unreliable classifications in sparse and noisy regions, and blindness to natural class boundaries). To overcome these limitations, we propose a hybrid k-NN (Hk-NN) algorithm based on the principles of local similarity and neighborhood homogeneity. It incorporates a two-step voting procedure with an entropy-optimized k-NN radius, decision trees with k-constrained leaves, and nearest neighbor predictions.We investigate 16,525 comments by alarm personnel for TAs in a pharmaceutical SC and encode them in terms of deviation causes and CMs (target features). We use SC data on cargo location, SC phase, sensor role, and temperature characteristics as predictor features for TA similarity estimation. In eight experimental setups, Hk-NN consistently outperforms k-NN with an optimized k in terms of accuracy, balanced accuracy, macro-average precision, recall, and specificity. At the same time, Hk-NN refrains from predicting observations, for which k-NN’s accuracy is close to a random guess.

Implementation of Recommendation Algorithm based on Recommendation Sessions in E-commerce IT System

The aim of the paper is to present a study as the implementation of the author’s Algorithm of the Recommendation Sessions ARS in an operating e‐commerce information system and to analyse basic parameters of the recommendation system created as a result of the implementation. The first part of the study contains a synthetic description of the area of recommendation systems. The next section presents the proprietary ARS recommendation algorithm based on recommendation sessions. The third part of the paper describes the mathematical model of the recommendation session built on the basis of the theory of graphs and networks, which such model makes the input data for the algorithm in question. The next part of the publication describes the possibilities of representing graph structures and the method of implementing a G graph (constituting a set of the recommendation session) in a relational database. The implementation of the ARS algorithm, based on the SQL standard, was also presented. The implementations in question have been developed on the basis of a working information system of the e‐commerce class. As a result of the implementation of the algorithm, a fully functional recommendation system was created, which can be adapted to various e‐commerce IT systems. The positive result of the work was confirmed by the research on the parameters of the recommendation system, included in the last part of the study.

Advantages of Using Factorisation Machines as a Statistical Modelling Technique

Factorisation machines originated from the field of machine learning literature and have gained popularity because of the high accuracy obtained in several prediction problems, in particular in the area of recommender systems. This article will provide a motivation for the use of factorisation machines, discuss the fundamentals of factorisation machines, and provide examples of some applications and the possible gains by using factorisation machines as part of the statistician’s model-building toolkit. Data sets and existing software packages will be used to illustrate how factorisation machines may be fitted and in what context it is worth being used.

When contextual information meets recommender systems: extended SVD++ models

ABSTRACT Collaborative filtering approach is widely used in the area of recommender systems. In fact, its predictive accuracy is supported by a large amount of additional information available on the internet, especially in social media. Singular Value Decomposition (SVD) is the most used matrix factorization method in the field of collaborative filtering, which is realized by using a latent factor vector of items and another one of users and introduces the users and item bias information. SVD++ is a derivation of the SVD model. It aims at adding implicit feedback information that is why the results of this model are improved. In this paper, different contributions are applied to SVD++ model through three different approaches which apply modification of latent factor vector while taking into account social and environmental information. Moreover, the experimental results showed that these proposed extensions ameliorate the prediction accuracy in terms of MAE and RMSE compared to SVD++ corresponding to four benchmarks, which are CiaoDVD, Food data, Adom data and Movielens-100k. According to the four used data sets, the experimental results showed that our proposed third contribution is effective in improving the prediction accuracy by 3.7%, 8.2%, 46.7% and 4%, respectively. These results also showed a reduction of the Mean Absolute Error by 0.0.71, 0.125, 0.61 and 0.038. Similar results were also obtained with the first and the second contribution.

Neural Attention Frameworks for Explainable Recommendation

Neural attention, an emerging technique used to identify important inputs within neural networks, have become increasingly popular in the area of recommender systems. Not only allowing to better identify what defines users and items, attention-based recommender systems are further able to provide accompanying explanations. However, these representations usually capture only part of users’ preferences and items’ attributes, resulting in limited reasoning and accuracy. We therefore propose Dual Attention Recommender with Items and Attributes (DARIA), a novel approach able to combine two dependable neural attention mechanisms to better justify its suggestions. Utilizing the personalized history of users, DARIA identifies the most relevant past activities while considering the real-world features that contributed to the similarity. In addition, we adopt the novel approach of self-attention and introduce Self-Attention Recommender based on Attributes and History (SARAH). As a variation to DARIA, SARAH utilizes two self-attention components to describe users by their most characteristic past activities and items by their best depicting attributes. Various experiments establish the significant improvement of SARAH and DARIA over seven key baselines in diverse recommendation settings. By comparing our two proposed frameworks, we demonstrate the potential benefit of applying self-attention in different scenarios.

Handling uncertainty in social media textual information for improving venue recommendation formulation quality in social networks

One of the major problems that social media front is to continuously produce successful, user-targeted information, in the form of recommendations, which are produced by applying methods from the area of recommender systems. One of the most important applications of recommender systems in social networks is venue recommendation, targeted by the majority of the leading social networks (Facebook, TripAdvisor, OpenTable, etc.). However, recommender systems’ algorithms rely only on the existence of numeric ratings which are typically entered by users, and in the context of social networks, this information is scarce, since many social networks allow only reviews, rather than explicit ratings. Even if explicit ratings are supported, users may still resort to expressing their views and rating their experiences through submitting posts, which is the predominant user practice in social networks, rather than entering explicit ratings. User posts contain textual information, which can be exploited to compute derived ratings, and these derived ratings can be used in the recommendation process in the lack of explicitly entered ratings. Emerging recommender systems encompass this approach, without however tackling the fact that the ratings computed on the basis of textual information may be inaccurate, due to the very nature of the computation process. In this paper, we present an approach which extracts features of the textual information, a widely available source of information in venue category, to compute a confidence metric for the ratings that are computed from texts; then, this confidence metric is used in the user similarity computation and venue rating prediction formulation process, along with the computed rating. Furthermore, we propose a venue recommendation method that considers the generated venue rating predictions, along with venue QoS, similarity and spatial distance metrics in order to generate venue recommendations for social network users. Finally, we validate the accuracy of the rating prediction method and the user satisfaction from the recommendations generated by the recommendation formulation algorithm. Conclusively, the introduction of the confidence level significantly improves rating prediction accuracy, leverages the ability to generate personalized recommendations for users and increases user satisfaction.

Evolution of recommender system over the time

Recommender system plays a supporting role in the process of information filtering. It plays a remarkable role in large-scale online shopping and product suggestions. This paper discusses various trends of recommender system such as content-based, collaborative-based and hybrid personalization techniques proposed for recommendations. It provides better insight and future directions of recommender systems. We have reviewed 142 articles from several journals and conference papers which were published from 1992 to 2019. We have used statistical descriptions to show the progression and drawbacks of the various notions of recommendation approaches. We have also discussed growing research demand in the area of recommender systems as well as the pros and cons of the currently available classifications. We have created a classification of recommender techniques, including various user inputs, knowledge from the database, the ways in which the recommendation will be presented to the user and the technologies which are used to create the recommendations.

Fuzzy Side Information Clustering-Based Framework for Effective Recommendations

Collaborative filtering (CF) is the most successful and widely implemented algorithm in the area of recommender systems (RSs). It generates recommendations using a set of user-product ratings by matching similarity between the profiles of different users. Computing similarity among user profiles efficiently in case of sparse data is the most crucial component of the CF technique. Data sparsity and accuracy are the two major issues associated with the classical CF approach. In this paper, we try to solve these issues using a novel approach based on the side information (user-product background content) and the Mahalanobis distance measure. The side information has been incorporated into RSs to further improve their performance, especially in the case of data sparsity. However, incorporation of side information into traditional two-dimensional recommender systems would increase the dimensionality and complexity of the system. Therefore, to alleviate the problem of dimensionality, we cluster users based on their side information using k-means clustering algorithm and each user's similarity is computed using the Mahalanobis distance method. Additionally, we use fuzzy sets to represent the side information more efficiently. Results of the experimentation with two benchmark datasets show that our framework improves the recommendations quality and predictive accuracy of both traditional and clustering-based collaborative recommendations.

An Efficient Hybrid Recommendation Model With Deep Neural Networks

Recently, deep learning has gained great popularity in the area of recommender systems. Various combinations of deep learning, collaborative recommendation and content-based recommendation have occurred. However, as one of the three most significant recommendation techniques, hybrid recommendation has little cooperation with deep learning. Besides, most current deep hybrid models only incorporate two simple recommendation methods together in post-fusion, leaving massive space for further exploration of better combinations. In this paper, we apply deep learning to hybrid recommendation, proposing a deep hybrid recommendation model DMFL (Deep Metric Factorization Learning). In DMFL, we combine deep learning with improved machine learning models to learn the interaction between users and items from multiple perspectives. Such deep hybrid learning helps to reflect the user preference more comprehensively and strengthen model's ability of generalization. We also propose a more accurate method of user feature representation, taking both long-term static characteristics and short-term dynamic interest changes of users into consideration. Furthermore, thorough experiments have been conducted on real-world datasets, which strongly proves the effectiveness and efficiency of the proposed model.