Case-based Recommender Systems Research Articles

Empowering Teachers in E-Learning: A Case-Based Recommender System for Effective Learning Design

In the rapidly evolving field of e-learning over the past decade, the learning design (LD) sector has emerged as a crucial determinant of success. It plays a vital role in ensuring effective instructional design, preserving best practices, enhancing educational quality, promoting personalization, and embracing technological innovation. However, the creation of effective LDs can be challenging due to the diverse range of learning methods, strategies, resources, learner profiles, evolving technologies, and communication platforms. This paper aims to address the research question of how to efficiently leverage recommender systems (RSs) and repurpose existing LD solutions to provide valuable support for teachers in the LD process. Specifically, this paper focuses on the implementation of case-based recommender system (CBRS) and presents the initial evaluation results. CBRS, which is a RS, assists teachers in locating and reusing pre-existing LDs. The initial evaluation results validate the system’s effectiveness in supporting teachers throughout the LD process by offering reliable and useful LDs. Furthermore, additional findings pertaining to the overall user perception and the perceived value of CBRS are discussed.

Exploiting the Interplay among Products for Efficient Recommendations

Recommender systems are built with the aim to reduce the cognitive load on the user. An efficient recommender system should ensure that a user spends minimal time in the process. Conversational Case-Based Recommender Systems (CCBR-RSs) depend on the feedback provided by the user to learn about the preferences of the user. Our goal is to use the feedback provided by the user effectively by exploiting the interplay among the products to build an efficient CCBR-RS. In this work, we propose two ways towards achieving that goal. In the first method, we utilize the higher order similarity and trade-off relationship among the products to propagate the evidence obtained through user feedback. In our second method, we utilize the diversity among cases/products along with the similarity and trade-off relationship to make the best use of the feedback provided by the user.

An Exploration for the Analysis of Ground Water Possibility in Case Based Recommendation System using Hybrid Cuckoo Search and Artificial Honey Bee Algorithm

Groundwater Possibility Exploration is one of the prime problems in human life. Human and other social species are directly or indirectly dependent on water resources. But with increasing population and instant changing environmental conditions, groundwater resources are also becoming less day by day. One approach of manual exploration can be used but that is most laborious and time consuming task. So, there is need of some autonomous approach with which we can find the more groundwater resources without digging the bore well. This paper presents an autonomous approach in case based recommendation system using hybrid cuckoo search and artificial honey bee algorithm for groundwater possibility detection. The proposed concept is structured in the manner to have the input in the form six attributes of slope, geology, landuse, lineament, soil & landform and give output in the form of low, intermediate and high possibility. In this method, case based reasoning used in the manner to retrieve the previous knowledge of use cases. The cases are actually the host bird’s nest and input is cuckoo’s egg. CS originates from the behaviour of certain species of cuckoo which lay eggs in other birds nest in parasitic manner. If cuckoo’s egg adapts the behaviour of host nests it will exist otherwise the host bird will discard cuckoo’s egg. The minimum similarity value is calculated using ABC algorithm. CBR life cycle further uses the information from this metaphor. The overall method is evaluated using the parameters of sensitivity, specificity and accuracy.

A similarity measurement framework of product-service system design cases based on context-based activity model

Design experience from previous design cases could help designers solving current design problems. Therefore, a case-based recommender system can recall suitable design cases for designers based on a similarity measurement mechanism. In the context of Product-Service System (PSS) design, the measurement of similarity between different cases becomes more challenging because of the complex nature of the PSS design. In this research, we propose a similarity measurement framework of PSS design cases based on the context-based activity model. In the proposed framework, a PSS design case is indexed and quantified by design activity element, design process, and function requirement. Ways to measure similarity between design indexes and design cases are also specified. A case study along with an empirical validation was conducted to validate the framework.

A framework for Personalized Wealth Management exploiting Case-Based Recommender Systems

Wealth Management is a business model operated by banks and brokers, that offers a broad range of investment services to individual clients, in order to help them reach their investment objectives. Wealth management services include investment advisory, subscription of mandates, sales of financial products, collection of investment orders by clients. Due to the complexity of the task, which largely requires a deep knowledge of the financial domain, a recend trend in the area is to exploit recommendation technologies to support financial advisors and to improve the effectiveness of the process. This paper proposes a framework to support financial advisors in the task of providing clients with personalized investment strategies. Our methodology is based on the exploitation of case-based reasoning. A prototype version of the platform has been adopted to generate personalized portfolios, and the performance of the framework shows that the yield obtained by recommended portfolios overcomes that of portfolios proposed by human advisors in most experimental settings.

MOOC-Rec: A Case Based Recommender System for MOOCs

Learning processes and learning potentials are continuously investigated. Exploration of the possibilities of Technology Enhanced Learning (TEL) led to the development of many solutions and recently to Massive Open Online Courses (MOOCs). MOOCs are probably the most important “novelty” in the field of e-learning of the last years. MOOCs are capable of providing several ten thousands of learners with access to courses over the web. MOOCs have recently gained much attention especially in leading universities and are now often considered as a highly promising form of teaching. More and more universities are currently working to offer their courses in the form of MOOC providing learners with a wide variety of choices. With MOOCs proliferation, learners will be exposed to various challenges and the traditional problem in TEL “finding the best learning resources” is more than ever up to date. Since information retrieval and searching for the appropriate learning resources is an essential activity in TEL, the development of recommender systems for learning has seen increased attention. Recommender systems permit to respond to the traditional problem. In the present paper, we address this major problem – the difficulty for learners to find courses which best fit their personal interests. We propose a system that recommends appropriate MOOCs in response to a specific request of the learner. Using the Case Based Reasoning (CBR) approach and a special retrieval information technique, the system proposes to the learners the most appropriate MOOCs (from different providers) fitting her/his request based on learner profile, needs and knowledge.

Personalized finance advisory through case-based recommender systems and diversification strategies

Recommendation of financial investment strategies is a complex and knowledge-intensive task. Typically, financial advisors have to discuss at length with their wealthy clients and have to sift through several investment proposals before finding one able to completely meet investors' needs and constraints. As a consequence, a recent trend in wealth management is to improve the advisory process by exploiting recommendation technologies. This paper proposes a framework for recommendation of asset allocation strategies which combines case-based reasoning with a novel diversification strategy to support financial advisors in the task of proposing diverse and personalized investment portfolios. The performance of the framework has been evaluated by means of an experimental session conducted against 1172 real users, and results show that the yield obtained by recommended portfolios overcomes that of portfolios proposed by human advisors in most experimental settings while meeting the preferred risk profile. Furthermore, our diversification strategy shows promising results in terms of both diversity and average yield.

Case-based recommender systems

We describe recommender systems and especially case-based recommender systems. We define a framework in which these systems can be understood. The framework contrasts collaborative with case-based, reactive with proactive, single-shot with conversational, and asking with proposing. Within this framework, we review a selection of papers from the case-based recommender systems literature, covering the development of these systems over the last ten years.

사례기반추론 기법을 이용한 개인화된 추천시스템 설계 및 구현

본 논문에서는 인터넷 컨텐츠 사이트에서 묵시적인 평가정보를 이용한 새로운 사례기반 추천시스템을 설계하고 구현하였다. 본 시스템은 크게 사용자 프로파일 생성 모듈, 유사도 계산 및 추천 모듈, 개인화된 메일링 모듈로 구성된다. 사용자 프로파일 생성 모듈에서는 사용자가 컨텐츠를 이용하면서 남긴 로그 기록을 이용하여 컨텐츠에 대한 개인별 선호도를 추출할 수 있는 속성내, 속성간 가중치를 제시하였다. 유사도 계산 및 추천 모듈에서는 사용자 프로파일과 새로운 컨텐츠간의 유사도를 측정하기 위한 유사도 계산식을 제시하였다. 개인화된 메일링 모듈에서는 개인별 선호도에 의해 구성된 추천 컨텐츠를 플렛폼-독립적인 XML 문서 형식으로 변환하여 발송한다. 제안된 모델에 대한 추천 효율을 검증하기 위해 평균절대오차(MAE)와 반응자작용특성(ROC) 값을 이용하여 제안한 추천 모델과 협동적 필터링 기법과 비교 실험하였다. 실험결과, 본 논문에서 제안한 모델의 추천 효율이 기존의 협동적 필터링 기법보다 우수함을 보였다. We design and implement a new case-based recommender system using implicit rating information for a digital content site. Our system consists of the User Profile Generation module, the Similarity Evaluation and Recommendation module, and the Personalized Mailing module. In the User Profile Generation Module, we define intra-attribute and inter-attribute weight deriver from own's past interests of a user stored in the access logs to extract individual preferences for a content. A new similarity function is presented in the Similarity Evaluation and Recommendation Module to estimate similarities between new items set and the user profile. The Personalized Mailing Module sends individual recommended mails that are transformed into platform-independent XML document format to users. To verify the efficiency of our system, we have performed experimental comparisons between the proposed model and the collaborative filtering technique by mean absolute error (MAE) and receiver operating characteristic (ROC) values. The results show that the proposed model is more efficient than the traditional collaborative filtering technique.

A Dynamic Approach To Feature Weighting

The main objective of a personalized recommender system is to filter and present (recommend) to the user the most appropriate items according to his preferences. In many Case Based Recommendation systems, thk goal is achieved by using weighted similarity measures. Thus, weighting the features, i.e. describing the items to be recommended, is a key issue in such systems. In this paper, we propose a dynamic weighting scheme for a Case Based Recommendation System, which is based on statistics of data extracted from past sessios. The applications of these ideas to an interactive, Case-Based travel recommender system, called Dietorecsl, that guides European travelers for their travel decision making processes, are described.