Types Of Recommender Systems Research Articles

FEIR: Quantifying and Reducing Envy and Inferiority for Fair Recommendation of Limited Resources

Recommendation in settings such as e-recruitment and online dating involves distributing limited opportunities, which differs from recommending practically unlimited goods such as in e-commerce or music recommendation. This setting calls for novel approaches to quantify and enforce fairness. Indeed, typical recommender systems recommend each user their top relevant items, such that desirable items may be recommended simultaneously to more and to less qualified individuals. This is arguably unfair to the latter. Indeed, when they pursue such a desirable recommendation (e.g., by applying for a job), they are unlikely to be successful. To quantify fairness in such settings, we introduce inferiority : a novel (un)fairness measure that quantifies the competitive disadvantage of a user for their recommended items. Inferiority is complementary to envy : a previously-proposed fairness notion that quantifies the extent to which a user prefers other users’ recommendations over their own. We propose to use both inferiority and envy in combination with an accuracy-related measure called utility : the aggregated relevancy scores of the recommended items. Unfortunately, none of these three measures are differentiable, making it hard to optimize them, and restricting their immediate use to evaluation only. To remedy this, we reformulate them in the context of a probabilistic interpretation of recommender systems, resulting in differentiable versions. We show how these loss functions can be combined in a multi-objective optimization problem that we call FEIR (Fairness through Envy and Inferiority Reduction), used as a post-processing of the scores from any standard recommender system. Experiments on synthetic and real-world data show that the proposed approach effectively improves the trade-offs between inferiority, envy and utility, compared to the naive recommendation and the state-of-the-art method for the related problem of congestion alleviation in job recommendation. We discuss and enhance the practical impact of our findings on a wide range of real-world recommendation scenarios, and we offer implementations of visualization tools to render the envy and inferiority metrics more accessible.

Adversarial Item Promotion on Visually-Aware Recommender Systems by Guided Diffusion

Visually-aware recommender systems have found widespread applications in domains where visual elements significantly contribute to the inference of users’ potential preferences. While the incorporation of visual information holds the promise of enhancing recommendation accuracy and alleviating the cold-start problem, it is essential to point out that the inclusion of item images may introduce substantial security challenges. Some existing works have shown that the item provider can manipulate item exposure rates to its advantage by constructing adversarial images. However, these works cannot reveal the real vulnerability of visually-aware recommender systems because (1) the generated adversarial images are markedly distorted, rendering them easily detected by human observers; (2) the effectiveness of these attacks is inconsistent and even ineffective in some scenarios or datasets. To shed light on the real vulnerabilities of visually-aware recommender systems when confronted with adversarial images, this paper introduces a novel attack method, IPDGI (Item Promotion by Diffusion Generated Image). Specifically, IPDGI employs a guided diffusion model to generate adversarial samples designed to promote the exposure rates of target items (e.g., long-tail items). Taking advantage of accurately modeling benign images’ distribution by diffusion models, the generated adversarial images have high fidelity with original images, ensuring the stealth of our IPDGI. To demonstrate the effectiveness of our proposed methods, we conduct extensive experiments on two commonly used e-commerce recommendation datasets (Amazon Beauty and Amazon Baby) with several typical visually-aware recommender systems. The experimental results show that our attack method significantly improves both the performance of promoting the long-tailed (i.e., unpopular) items and the quality of generated adversarial images.

Causality-aware social recommender system with network homophily informed multi-treatment confounders

Typical recommender systems utilize observed ratings of users as inputs to learn their preferences and aim to output recommendations of new items that users will like by predicting their potential ratings. The real world is driven by causality given that whether or not a user is made exposed to the item may significantly affect the rating. While some recent methods have taken a causal view to mitigate the confounding bias in observed rating data, few have recognized recommendation as a multiple causal inference problem that concerns numerous items simultaneously in practice. By framing the recommendation as a multiple causal inference problem, this paper develops a causality-aware social recommender that incorporates underlying social network structures with matrix factorization methods for deconfounding in networked observational data to enhance social recommendations. Leveraging social network information, which inherently confounds with the preferences of users, is particularly crucial but not trivial for inferring unobserved confounders in social recommender systems. Considering that connected users in a social network share similar attributes, we propose to incorporate the network homophily into the matrix factorization models through regularization to better learn the latent variables. The extracted latent variables then capture the network informed multi-treatment confounders to mitigate the confounding bias and improve the rating prediction accuracy. The models are estimated through a proximal gradient-based optimization framework, which not only eases the incorporation of the network structure constraints but also improves the computational efficiency of the proposed method. Simulation and case studies are conducted to validate the proposed method.

Methods of preference aggregation in group recommender systems

The rapid growth of data volumes has led to information overload, which impedes informed decision-making. To solve this problem, recommender systems have emerged that analyze user preferences and offer relevant products on their own. One type of recommender system is group recommender systems, which are designed to facilitate collaborative decision-making, increase user engagement, and promote diversity and inclusion. However, these systems face challenges such as accommodating diverse group preferences and maintaining transparency in recommendation processes. In this study, we propose a method for aggregating preferences in group recommendation systems to retain as much information as possible from group members and improve the accuracy of recommendations. The proposed method provides recommendations to groups of users by avoiding the aggregation process in the first steps of recommendation, which preserves information throughout the group recommendation process and delays the aggregation step to provide accurate and diverse recommendations. When the object of a collaborative filtering-based recommender system is not a single user but a group of users, the strategy for calculating similarity between individual users to find similarity should be adapted to avoid aggregating the preferences of group members in the first step. In the proposed model, the nearest neighbors of a group of users are searched, so the method of finding neighbors is adapted to compare individual users with the group profile. An experimental study has shown that the proposed method achieves a satisfactory balance between accuracy and diversity. This makes it well suited for providing recommendations to large groups in situations where accuracy is more or less important compared to diversity. These results support the assumption that retaining all information from group members without using aggregation techniques can improve the performance of group recommender systems, taking into account various features.

Exploring content-based group recommendation for suggesting restaurants in Havana City

Recommender systems (RSs) are a relevant kind of artificial intelligence-based systems focused on providing users with the information that best fit their preferences and needs in a search space overloaded of possible options. Specifically, group recommender systems (GRSs) are a special type of RS centered on recommending items that are consumed in groups and not individually, being TV program and touristic packages key examples of such items. The current work is focused on proposing a content-based group recommendation approach (CB-GRS) contextualized to the restaurant recommendation domain. In contrast to previous content-based group recommendation models, the proposal incorporates novel stages such as restaurants feature imputation, the generation of a virtual group profile, the use of feature weighting, and the automatic selection of the most appropriate aggregation approach for composing group recommendations. The proposal is evaluated in an original recommendation scenario, related to restaurant from Havana City in Cuba, where several restaurant attributes are identified for applying the proposed CB-GRS approach. The experimental protocol evaluates individually each component of the proposal, evidencing their importance as part of the whole framework. Furthermore, the comparison with previous works has been also developed. The proposed approach can be applied in other recommendation scenarios, and in addition, the developed experimental protocol is generalizable for the evaluation of further content-based individual and group recommendation approaches in the tourism domain.

Virtual One-day Trip Planner

A virtual one-day trip planner is necessary to simplify trip planning for users who wish to explore different places within a limited span of time. A Recommender system is used to make various suggestions according to the budget and time. A Recommendation system is of utmost importance to provide recommendations to users on all the aspects using the technology. Different types of recommendation systems are available, i.e., Hybrid Recommendation System, User-based Collaborative Filtering System and Item-Based Collaborative Filtering System. The user-based filtering compares a user with other users who have similar preferences based on the identical items ratings they have provided. On the other hand, Item-based filtering takes into consideration the previously rated preference of the user to make further recommendations. A Hybrid System consists of both types of collaborative filtering to make the recommendation to the user.

Recommender systems: models, challenges and opportunities

The purpose of this study is to provide a comprehensive overview of the latest developments in the field of recommender systems. In order to provide an overview of the current state of affairs in this sector and highlight the latest developments in recommender systems, the research papers available in this area were analyzed. The place of recommender systems in the modern world was defined, their relevance and role in people's daily lives in the modern information environment were highlighted. The advantages of recommender systems and their main properties are considered. In order to formally define the concept of recommender systems, a general scheme of recommender systems was provided and a formal task was formulated. A review of different types of recommender systems is carried out. It has been determined that personalized recommender systems can be divided into content filtering-based systems, collaborative filtering-based systems, and hybrid recommender systems. For each type of system, the author defines them and reviews the latest relevant research papers on a particular type of recommender system. The challenges faced by modern recommender systems are separately considered. It is determined that such challenges include the issue of robustness of recommender systems (the ability of the system to withstand various attacks), the issue of data bias (a set of various data factors that lead to a decrease in the effectiveness of the recommender system), and the issue of fairness, which is related to discrimination against users of recommender systems. Overall, this study not only provides a comprehensive explanation of recommender systems, but also provides information to a large number of researchers interested in recommender systems. This goal was achieved by analyzing a wide range of technologies and trends in the service sector, which are areas where recommender systems are used.

Modeling Users’ Curiosity in Recommender Systems

Today’s recommender systems are criticized for recommending items that are too obvious to arouse users’ interests. Therefore the research community has advocated some ”beyond accuracy” evaluation metrics such as novelty, diversity, and serendipity with the hope of promoting information discovery and sustaining users’ interests over a long period of time. While bringing in new perspectives, most of these evaluation metrics have not considered individual users’ differences in their capacity to experience those ”beyond accuracy” items. Open-minded users may embrace a wider range of recommendations than conservative users. In this paper, we proposed to use curiosity traits to capture such individual users’ differences. We developed a model to approximate an individual’s curiosity distribution over different stimulus levels. We used an item’s surprise level to estimate the stimulus level and whether such a level is in the range of the user’s appetite for stimulus, called Comfort Zone . We then proposed a recommender system framework that considers both user preference and their Comfort Zone where the curiosity is maximally aroused. Our framework differs from a typical recommender system in that it leverages human’s Comfort Zone for stimuli to promote engagement with the system. A series of evaluation experiments have been conducted to show that our framework is able to rank higher the items with not only high ratings but also high curiosity stimulation. The recommendation list generated by our algorithm has higher potential of inspiring user curiosity compared to the state-of-the-art deep learning approaches. The personalization factor for assessing the surprise stimulus levels further helps the recommender model achieve smaller (better) inter-user similarity.

Recommender Systems: An Overview, Challenges and Optimization Strategies, Future Scope

In an era characterized by information overload and the proliferation of digital content, recommender systems (RS) have emerged as indispensable tools for assisting users in navigating the vast landscape of choices. These systems employ sophisticated algorithms and methodologies to anticipate user preferences and provide tailored suggestions, thereby enhancing user experience and engagement across a diverse range of online platforms. They have made giant leaps of progress in accuracy and performance. Despite the revolutionary potential, recommender systems are not immune to challenges that can affect their efficiency. The key challenges include data sparsity-The uneven distribution of data regarding user-item interactions, cold start, scalability, ever changing priorities of user making the previous results obsolete .In This paper we set a stage for meticulous review of the RS and their applications in various fields ,the taxonomies involved in RS, discussing the two primary categories : collaborative filtering and content-based filtering. This paper at the later stage exposes us to the challenges in RS, the metrics used to evaluate the impact of the strategies used to overcome these challenges. Furthermore, this paper also envisions the future of RS which may open new research directions in this domain. Index: introduction, Types of RS, Challenges in RS, Optimization Strategies, Evaluation Metrics, Case Studies, Future Directions, Conclusion, References. Appendices.

Suspiciousness and Fast and Slow Thinking Impact on Trust in Recommender Systems

Abstract By offering consumers more proactive and individualized information services, recommender systems have proven to be a significant answer to the problem of information overload. And collaborative filtering approaches have proven to be an important component of many such recommender systems, allowing for the development of high-quality recommendations by harnessing the preferences of communities of similar users. In this study, we argue that individual personality traits may play an essential role in ensuring general end-user confidence in recommender systems. Individual elements to be examined include social suspiciousness as a personality feature and Daniel Kahneman’s fast and slow thinking styles, which distinguish between two modes of thought: System 1 is rapid, instinctual, and emotive, while System 2 is slower, more deliberate, and more rational. Due to a lack of explanation or inaccurate recommendation results, users may not trust current recommender systems, which poses a significant challenge for those systems. As such, having a trustworthy recommender system is essential. Three different trust-aware recommender system types are analyzed systematically in this study: robust systems that filter misleading information such as spam and fake news; social aware conscious systems that benefit from users’ social connections; and explainable systems that offer justifications for recommended products. On a sample size of 487 Romanian respondents, in an online survey, we have analyzed the impact of individual factors on the trustworthiness of the three types of recommender systems. Results show that fast thinking fully mediates the relationship between social suspiciousness and trust in all three types of recommender systems.

Implementing a Movie Recommendation System in Machine Learning Using Hybrid Approach

Abstract: In this paper, we have proposed a movie recommendation system using hybrid recommendation system. Today, there are a lot of recommendation systems available which are practically implemented in various websites and mobile apps. Variety exists in types of recommendation systems, user interfaces but most importantly, the accuracy of the recommendation systems. Determining a user’s possible future preference of movie or TV shows to watch is a complex task which requires a lot of relevant user data such as watch history of user, genres liked by the user, favorite actor or director, etc. Hence, the aim of this proposed system is to refine the search engine and make it more enhanced and accurate in terms of prediction. The system recommends the movies graphically based on both, user preference and similarity of individual user with other users. It also shows top rated movies worldwide and updates the recommendation after every choice of movie or show by the user

Personalized Travel Recommendation Systems: A Study of Machine Learning Approaches in Tourism

Recommender systems that utilize machine learning algorithms are a prominent tool in the design and implementation of personalized tourism experiences. These systems analyze user data to generate recommendations for destinations, attractions, accommodations, and activities based on user preferences, behavior, and similarity to other users. Collaborative filtering and content-based filtering are two widely used machine learning algorithms in recommender systems, and hybrid systems that combine both approaches have shown to be effective in producing more accurate recommendations. Tourism recommendation systems (TRS) provide several benefits, including personalization, convenience, improved user experience, and increased revenue for tourism businesses. These systems can suggest destinations, attractions, accommodations, and activities that match user preferences and past behaviors, ultimately simplifying the trip planning process. Machine learning algorithms can be trained on large datasets to generate personalized recommendations, and can continuously improve their effectiveness by incorporating new data and user feedback. This paper provides a state-of-the-art overview of various types of recommendation systems (RS), including those based on user preferences, behaviors, demographic profiles, and social network judgments. The paper also presents a comparison table for these approaches. Additionally, the paper discusses the different stages of the travel process and the sources of data that can be used to develop a recommender system. The concluding section of the paper highlights the importance of personalized recommendations in the tourism industry and the potential for future research in this area.

Trends and Techniques used in Tourist Recommender System : A Review

Traveling to other locations for pleasure, business, or other reasons is called tourism. In every sort of recommender system, there are a certain amount of users and items. Creating a recommendation systems is made more difficult by the abundance of information available online and the high volume of website visits. A recommender system pulls the user's preferences or interests from relevant data sets to reduce information overload. This calls for the development of a new recommended system that will deliver higher-quality recommendations for massive data sets. To solve these kinds of problems, we have found several approaches for making recommendations, including three different types: content-based filtering, collaborative filtering, and hybrid filtering. With each type of recommender system, this research also analyses several algorithms. The main aim of this paper is to review several trends and techniques currently being used in tourist recommender systems.

사용자 유사도 및 지역사회 운동 효과 기반 유사 요통 환자 추천에 관한 연구

In order to return to a healthy daily life of individuals who need rehabilitation exercise, it is necessary to provide continuous individualized rehabilitation exercise services after discharge at the hospital. However, in reality, it is difficult to provide services for the entire rehabilitation cycle of individuals because hospital and community rehabilitation data are not shared. In this paper, we consider the problem of providing a top-k similar patient list under the assumption that rehabilitation medical care and community exercise service are shared. The considered problem differs from the typical recommender system problems due to the lack of labels representing any two patients share the same or similar characteristics, which means that it is challenging to apply the existing recommender algorithms. We present a mathematical programming-based approach that enables us to identify the optimal feature set and similarity between patients. Then, a two-step algorithm was developed by utilizing the presented mathematical formulation, which results in a user-based collaborative filtering system. We report the experimental results for 12 experimental options using real-life clinical data of low back pain patients.

Movie Recommender System using Machine Learning

In this research, we propose a movie recommender system that can recommend movies to both new and existing customers. It searches movie databases for all of the relevant data, such as popularity and beauty that is required for a recommendation. We apply both content-based and collaborative filtering and evaluate their advantages and disadvantages. To build a system that delivers more exact movie recommendations, we employ hybrid filtering, which is a combination of the outcomes of these two processes. The recommendation engines are also used for business purposes and to make strategies for organizations. Due to the growing demands of customers and user’s recommendation systems plays a huge role. These recommender systems also help us to utilize our time in the busy world by giving us more relevant searches. These systems are generally used with the movie’s websites or with many commercial applications and are of great use. This type of recommendation system can be also used for precise results. It will make movies suggestions more relevant as per the need of the users.

Towards Analyzing the Online Learner's Behavior: An Expedition to Recommender System

E-learning courses are gaining significance nowadays and are also an active area for research. COVID-19 pandemic has challenged education system across the globe and many institutions have switched to the online platforms for survival. Due to this, a lot many online courses are available and it is very difficult for a novice user to select suitable course as per one’s preference. Despite the great motivation for MOOC courses and rapid growth of online platforms, there has been tremendous drop out for MOOC courses. This paper focuses on analyzing user activities or user’s learning behavior in order to recommend suitable and unsuitable courses according to user preference. One of the key issues is nowadays is the increasing dropout rate of MOOC learners. The completion rate of most of the online courses is very less as compared to the number of enrolled participants. After creating and analyzing the user learners’ profile, the recommender system recommends suitable courses to the learners so that dropout cases will decrease and the user will be able to choose appropriate courses as per their preferences. Sometimes under social influence or lack of awareness, learners opt those courses that are not appropriate for them. As each learner knowledge level is diverse so customization is required in order to improve online learning experience. In this paper, authors have described various challenges of the online learners, suggested how a recommender system should be advanced and different types of recommender systems.

Implementing a Hybrid Recommendation System to Personalize Customer Experience in E-Commerce Domain

With the rapid use of the internet, a huge amount of data is generated over the network with every passing second. On the other hand, the user demands information that is relevant to his personal search. The processing of such huge data is a challenging task. To serve the user with his specific required information, there is a need for an information retrieval mechanism that can process this large data. A recommendation system is such required technology that retrieves information in order to improve users’ access and thereby recommending items that are relevant to his explicitly mentioned behavior and preferences. The recommendation algorithm analyzes the huge dataset and focuses to recommend accurate content to the user. There are several recommendation systems in use today, some popular among them are Netflix, YouTube, Tinder, and Amazon. In this article, discussion is done on various types of recommendation systems, issues of recommendation systems, and use cases of widely used recommendation engines and their potential benefits. The work introduced in this paper is an integration approach of domain-specific and item-based recommendation system. The performance of the proposed algorithm is measured on evaluation metrics precision and recall. Experimental results prove that the approach introduced in this paper performed well as compared to existing methods.

The Construction of Accurate Recommendation Model of Learning Resources of Knowledge Graph under Deep Learning

With the rapid development of science and technology and the continuous progress of teaching, it is now flooded with rich learning resources. Massive learning resources provide learners with a good learning foundation. At the same time, learners want to be precise from many learning resources. Second, it becomes more and more difficult to quickly obtain the learning resources you want. Therefore, it is very important to accurately and quickly recommend learning resources to learners. During the last two decades, a large number of different types of recommendation systems were adopted that present the users with contents of their choice, such as videos, products, and educational content recommendation systems. The knowledge graph has been fully applied in this process. The application of deep learning in the recommendation systems has further enhanced their performance. This article proposes a learning resource accurate recommendation model based on the knowledge graph under deep learning. We build a recommendation system based on deep learning that is comprised of a learner knowledge representation (KR) model and a learning resource KR model. Information such as learner’s basic information, learning resource information, and other data is used by the recommendation engine to calculate the target learner’s score based on the learner KR and the learning resource KR and generate a recommendation list for the target learner. We use mean absolute error (MAE) as the evaluation indicator. The experimental results show that the proposed recommendation system achieves better results as compared to the traditional systems.

Multi-Criteria Ranking: Next Generation of Multi-Criteria Recommendation Framework

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.

Towards psychology-aware preference construction in recommender systems: Overview and research issues

User preferences are a crucial input needed by recommender systems to determine relevant items. In single-shot recommendation scenarios such as content-based filtering and collaborative filtering, user preferences are represented, for example, as keywords, categories, and item ratings. In conversational recommendation approaches such as constraint-based and critiquing-based recommendation, user preferences are often represented on the semantic level in terms of item attribute values and critiques. In this article, we provide an overview of preference representations used in different types of recommender systems. In this context, we take into account the fact that preferences aren’t stable but are rather constructed within the scope of a recommendation process. In which way preferences are determined and adapted is influenced by various factors such as personality traits, emotional states, and cognitive biases. We summarize preference construction related research and also discuss aspects of counteracting cognitive biases.