Temporal dynamics of user preferences in learning content recommendation systems using SCL1-LSTM technique

Social Learning as a new concept of learning model emphasizes an individual's activity and formation of relationships with other people. On the contrary, traditional recommendation system provides a target user with the appropriate recommendation information after analyzing a user's preference based on the user's profiles and rating histories. These kinds of systems need to modify recommendation algorithm; these traditional recommendation systems are limited to only two attributes - user profiles and rating histories — that includes the problem of recommendation reliability and accuracy. In this paper, we present a user-context based collaborative filtering (UCCF) using user-context and social relationships. The UCCF analyzes user-context and social relationships, and generates a similar user group which uses the user's recommendation score from similar user groups. The UCCF reflects strong ties of users who have similar tendency and improves reliability and accuracy of the content and expert recommendation system.

Recommender systems play an important role in most modern e-commerce applications. They have allowed users to become aware of the myriad choices available to them. The ease of information and the abundance of options have helped users make educated decisions. A recommender system studies a user's preferences and continues learning the user's changing interests, so as to suggest items that incline with the user's interests. In cases where a user is new to the application, or the user prefers not to discourse preferences, the recommender system is unable to gather the user's preference on any item. This is called the cold start problem; wherein the system can make valid recommendations only once the user starts informing the system about his/her choices. In this paper, we discuss the challenges faced by the cold start problem and how this problem may be alleviated using social media. We suggest an approach where we collect public information from users' social media accounts and analyze this information to understand their preferences. In particular, we gather the new user's information using their Twitter profile; i.e., the user's interest and preferences are extracted from his/her Twitter profile by analyzing his/her tweets. These interests will help the system understand what kind of movies the user will be most interested in. We compare these preferences with the metadata about the individual items. Using this approach, we develop a movie recommendation system wherein we produce top-N movie recommendations for a user. We used the MovieTweetings dataset to model the application. Two sets of results have been produced. In the first, smaller set of 770 users, 72.67% of users have received 100% accurate movie recommendations while nearly 80% of users got more than 75% accuracy. For the second, larger set of more than 3,500 users, 53 % of users have received 100% accurate recommendations while 72% of users got more than 75% accuracy. These encouraging results have demonstrated that the approach is effectively in alleviating cold start problems in recommendation systems, and may be applicable to many other e-commerce applications.

In this paper, we present our vision and some initial experiments on how to anticipate significance, similarity or polarity of various types of (preferably implicit) user feedback and how to form individual user preference for recommendation. Throughout the corporate web, we can observe the same patterns or actions in user behavior (e.g. page-view, amount of scrolling, rating or purchasing). Recorded user behavior – user feedback – is often used as base for personalized recommendation, but the connection between the feedback and user preference is often unclear or noisy.Our goal is to analyze user behavior in order to understand its relation to the user preference. We report on some initial experiments on a real-world e-commerce application. We describe our new models and methods how to combine various feedback types and how to learn user preferences.

One of the well-known issues with content recommender system is that they tend to become over-specialized, which often has a negative influence on user experience. This can be solved by diversification of the recommendation list, a process that implements a tradeoff between accuracy and diversity of recommended items. Normally, item metadata is used in the diversity measure. In certain cases however, the item metadata may not be available thus a different approach to measure diversity is required. The aim of this preliminary study is to determine whether latent features can be used to measure the diversity of recommended items. In order to resolve this we generated recommendation lists for 43 different users using the LDOS-CoMoDa dataset. We then evaluated the diversity of these lists using the standard intra-list diversity measure. In addition we calculated the diversity of each list by comparing the latent features (calculated using the matrix factorization approach) of each item on the list. The comparison of both value sets showed that they show similar characteristics which implies that latent feature space offers an alternative method of evaluating item diversity when no metadata is present.

Recommender systems play a crucial role in enhancing user experience across various digital platforms by delivering relevant and personalized content. However, many recommender systems still face challenges in providing accurate recommendations, especially in cold-start situations and when user data is limited. This study aims to address these issues by optimizing content recommendation systems using Neural Collaborative Filtering (NCF), a deep learning-based approach capable of capturing non-linear relationships between users and items. We compare the performance of NCF with traditional methods such as Matrix Factorization (MF) and Content-Based Filtering (CBF) using the MovieLens-1M dataset. The research method employed is a quantitative approach that encompasses several stages, including preprocessing, model training, and evaluation using metrics such as Root Mean Squared Error (RMSE) and Precision@K. The results of this research are significant, demonstrating that NCF achieves the lowest RMSE of 0.870, outperforming MF with an RMSE of 0.950 and CBF with an RMSE of 1.020. Additionally, the Precision@K achieved by NCF is 0.73, indicating the model’s superior ability to provide more relevant recommendations compared to baseline methods. Hyperparameter tuning reveals that the optimal combination includes an embedding size of 16, three hidden layers, and a learning rate of 0.005. Despite its excellent performance, NCF still faces challenges in handling cold-start cases and requires significant computational resources. To address these challenges, integrating additional metadata and exploring regularization techniques such as dropout are recommended to enhance generalization. The implications of the findings from this study suggest that NCF can significantly improve prediction accuracy and recommendation relevance, thus having the potential for widespread application across various domains, such as e-commerce, streaming services, and education, to enhance user experience and the efficiency of recommendation systems. Further research is needed to explore innovative solutions to address cold-start challenges and reduce computational demands.

Linear and nonlinear features of EEG microstate associated with insomnia.

An essential problem in real-world recommender systems is that user preferences are not static and users are likely to change their preferences over time. Recent studies have shown that the modelling and capturing the dynamics of user preferences lead to significant improvements on recommendation accuracy and, consequently, user satisfaction. In this paper, we develop a framework to capture user preference dynamics in a personalized manner based on the fact that changes in user preferences can vary individually. We also consider the plausible assumption that older user activities should have less influence on a user’s current preferences. We introduce an individual time decay factor for each user according to the rate of his preference dynamics to weigh the past user preferences and decrease their importance gradually. We exploit users’ demographics as well as the extracted similarities among users over time, aiming to enhance the prior knowledge about user preference dynamics, in addition to the past weighted user preferences in a developed coupled tensor factorization technique to provide top-K recommendations. The experimental results on the two real social media datasets—Last.fm and Movielens—indicate that our proposed model is better and more robust than other competitive methods in terms of recommendation accuracy and is more capable of coping with problems such as cold-start and data sparsity.

E-learning is an important education media in the current internet scenario. However, selection of suitable content for effective and fast learning is a challenging problem to the learners. E-learning content selection through group discussions using social networks is a new approach that helps the learners to choose the suitable content from the web. For this purpose, we propose a new content recommendation system for recommending suitable e-content for the e-learners. This content recommendation system uses an existing classification algorithm and the proposed ranking algorithm for making effective decision over the e-content for the respective users.

Collaborative filtering is one of the most widely-used algorithms in recommendation systems. In user-based collaborative filtering algorithm, current users' nearest neighbors are used to recommend items because they have similar preference, but users' preference varies with time, which often affects the accuracy of the recommendation. As a result of the varying users' preference, many researches about recommendation systems are focusing on the time factor, to find a way to make up for the change in preferences of users. The existing time-related algorithms usually add time factor in the training phase and make this procedure more complicated. To catch the newest preference of the users and improve the accuracy of the recommendation without complicating the training phase, a timesensitive collaborative filtering model is proposed in this paper, which keeps the original training phase and make some changes in the prediction phase. During the recommendation process, the proposed model orders the items by time for each user as a sequence. The sequence is called time-behavior sequence. First it finds the last item from current user's time-behavior sequence which represents the newest preference of the current user. Secondly, it locates the item in nearest neighbors' timebehavior sequence and saves the timestamp of the item. Lastly, it recommends the items whose timestamps are greater than the saved timestamp from the nearest neighbors' time-behavior sequence. Experiments on the MovieLens dataset show that the proposed time-sensitive collaborative filtering model gives better recommendation quality than the traditional user-based collaborative filtering recommendation algorithm. It can catch the newest preferences of the users and increase the accuracy of recommendation, without changing the training phase.

SDLDA: lncRNA-disease association prediction based on singular value decomposition and deep learning

정보기술의 발전과 인터넷 사용의 증가로 이용가능한 정보들의 양이 폭발적으로 증가한다. 콘텐츠 추천 시스템은 사용자가 원하지 않는 정보를 필터링하고 유용한 정보를 추천하는 서비스를 제공한다. 기존의 추천 시스템은 데이터마이닝 기법으로 웹 접속 기록 및 유형과 사용자가 요구하는 정보를 서비스 제공자 측면에서 분석하여 콘텐츠를 제공한다. 사용자의 선호도와 생활패턴 등의 사용자 측면에서의 정보들의 표현이 어려웠기 때문에 제한된 서비스를 제공할 수 밖에 없었다. 시맨틱 웹 기술은 이미지, 문서 등의 모든 객체를 대상으로 목적에 맞는 정보를 수집, 가공, 응용할 수 있도록 데이터 간에 잘 정의된 의미 있는 관계를 만들 수 있다. 본 논문에서는 시맨틱 웹 환경에서 개인화 프로파일을 동적으로 갱신하여 반영할 수 있는 콘텐츠 추천 검색 시스템을 제안한다. 개인화 프로파일은 프로파일의 특징을 담고 있는 컬렉터, 다양한 컬렉터들로부터 프로파일을 수집하는 수집기, 프로파일 특성에 기반한 고유의 프로파일 컬렉터를 해석하는 해석기로 구성된다. 개인화 모듈은 콘텐츠 추천 서버에서 개인화 프로파일과 주기적으로 동기화할 수 있도록 도와준다. 추천 콘텐츠로 음악을 선택하여 서비스 시나리오에 따라 개인화 프로파일이 콘텐츠 추천 서버에 전달되어 사용자의 선호도와 생활패턴이 반영된 추천리스트를 제공하는지 실험한다. With the advance of information technologies and the spread of Internet use, the volume of usable information is increasing explosively. A content recommendation system provides the services of filtering out information that users do not want and recommending useful information. Existing recommendation systems analyze the records and patterns of Web connection and information demanded by users through data mining techniques and provide contents from the service provider's viewpoint. Because it is hard to express information on the users' side such as users' preference and lifestyle, only limited services can be provided. The semantic Web technology can define meaningful relations among data so that information can be collected, processed and applied according to purpose for all objects including images and documents. The present study proposes a content recommendation search system that can update and reflect personalized profiles dynamically in semantic Web environment. A personalized profile is composed of Collector that contains the characteristics of the profile, Aggregator that collects profile data from various collectors, and Resolver that interprets profile collectors specific to profile characteristic. The personalized module helps the content recommendation server make regular synchronization with the personalized profile. Choosing music as a recommended content, we conduct an experience on whether the personalized profile delivers the content to the content recommendation server according to a service scenario and the server provides a recommendation list reflecting the user's preference and lifestyle.

This research investigates the application of artificial intelligence (AI) in digital learning environments and its impact on learning outcomes. A comprehensive review of literature was conducted, encompassing studies from various educational levels and settings. The analysis reveals promising findings regarding the effectiveness of AI interventions, including intelligent tutoring systems, adaptive learning platforms, virtual assistants, and content recommendation systems, in enhancing learning outcomes. Learners exhibit high levels of engagement and satisfaction with AI-enhanced learning environments, appreciating the interactive features and personalized support provided by AI technologies. However, challenges and limitations associated with AI implementation, such as technical issues, privacy concerns, and ethical considerations, were identified. The research contributes valuable insights into the potential benefits and risks of AI in education, with implications for both research and practice. Future research directions include optimizing AI algorithms, exploring ethical and social implications, and addressing educator training needs to ensure the successful integration of AI technologies into teaching and learning processes.

Latent semantic feature extraction (LSFE) is a feature extraction framework to obtain meaningful features from large volumes of data. In this chapter, we give a brief introduction to LSFE and mainly focus on one very important LSFE method, singular value decomposition (SVD). Besides the classical theory of SVD, we also discuss the SVD updating technique and SVD with compressive sampling. Finally, through case studies, we show how SVD can be used in a movie recommendation system.

Industrial organizations are turning to recommender systems (RSs) to provide more personalized experiences to customers. This technology provides an efficient solution to the over-choice problem by quickly combing through large amounts of information and supplying recommendations that fit each user's individual preferences. It is quickly becoming an integral part of operations, as it yields successful and convenient results. This research provides an enhanced integrated fuzzy logic-based deep learning technique (EIFL-DL) for recent industrial challenges. Extracting useful insights and making appropriate suggestions in industrial settings is difficult due to the fast development of data. Traditional RSs often struggle to handle the complexity and uncertainty inherent in industrial data. To address these limitations, we propose an EIFL-DL framework that combines fuzzy logic and deep learning techniques to enhance recommendation accuracy and interpretability. The EIFL-DL framework leverages fuzzy logic to handle uncertainty and vagueness in industrial data. Fuzzy logic enables the modelling of imprecise and uncertain information, and the system is able to capture nuanced relationships and make more accurate recommendations. Deep learning techniques, on the other hand, excel at extracting complex patterns and features from large-scale data. By integrating fuzzy logic with deep learning, the EIFL-DL framework harnesses the strengths of both approaches to overcome the limitations of traditional RSs. The proposed framework consists of three main stages: data preprocessing, feature extraction, and recommendation generation. In the data preprocessing stage, industrial data is cleaned, normalized, and transformed into fuzzy sets to handle uncertainty. The feature extraction stage employs deep learning techniques, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), to extract meaningful features from the preprocessed data. Finally, the recommendation generation stage utilizes fuzzy logic-based rules and a hybrid recommendation algorithm to generate accurate and interpretable recommendations for industrial applications.

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.