A Personalized Recommender System from Probabilistic Relational Model and Users’ Preferences

A Unified Recommendation Framework Based on Probabilistic Relational Models

Nowadays, large volumes of valuable data of different degrees of veracity can be easily generated or collected at a high velocity from a wide variety of data sources. Embedded in these big data are implicit, previously unknown and potentially useful information and knowledge. Hence, big data analytics are in demand as they assist users to analyze the big data and discover new knowledge. This in turn helps users gain insight into big data, obtain useful information, and make personalized recommendation. In this paper, we focus on big data analytics on music-i.e., music analysis-for personalized music recommendation systems. In particular, we present a system that exploits the frequent tags attached to the tracks in an user's listening history and a hidden Markov model (HMM) for generating predictions and thus making personalized recommendation. Knowledge learned from this personalized music recommendation can be transferred to building other personalized recommendation systems.

Purpose – The purpose of this paper is not only to design a more effective recommendation system for libraries, but also to eliminate many of the weaknesses found in the existing library recommender systems.Design/methodology/approach – A novel library recommender system was designed for English collections by integrating personal ontology model and collaborative filtering model with domain specification.Findings – The trend of the traditional library is evolving toward that of digital library. The personal ontology recommender (PORE) system offers a friendly user interface and provides several personalized services.Research limitations/implications – This system is only implemented and tested in the Library of National Chung Hsing University in Taiwan.Originality/value – The paper demonstrates a good methodology to offer an active, effective, and personalized recommendation system for library patrons.

Everyday there are masses of information generated and the existence of a large amount of information makes it hardly to mining the wanted information. Personalized recommendation is the process to alleviative the problem. Collaborative filtering is one of the most popular technologies in the personal recommendation system. As the user rating matrix becoming extremely sparsity, traditional collaborative filtering recommendation algorithm calculates similarity between items using the rating data, and it does not consider the semantic relationship between different items, thus recommendation quality is very poor. To solve this problem, the paper combines the item semantic similarity and the item rating similarity, which takes into account the influence of item semantic and user rating to enhance the item-based collaborative filtering. The personalized collaborative filtering recommendation algorithm combining the item semantic similarity and item rating similarity can mitigate the sparsity problem in the electronic commerce recommender systems.

More and more recommender systems build close relationships with their users by adapting to their needs and therefore providing a personal experience. One aspect of personalization is the recommendation and presentation of information and products so that users can access the recommender system more efficiently. However, powerful filtering technology is required in order to identify relevant items for each user. In this paper we describe how collaborative filtering and content-based filtering can be combined to provide better performance for information filtering. We propose the personalized design recommender system of textile design applying both technologies as one of the methods in the material development centered on customer’s sensibility and preference. Finally, we plan to conduct empirical applications to verify the adequacy and the validity of our personalized design recommender system.

This distinctive research paper investigates a fresh slant to process personalized music recommendation to a streamer based on the customized streaming trends focusing on today's music streaming services. Music Recommendation Systems (MRS) are one of the sole criteria that gives streaming application such as Spotify, Apple Music and other current major streaming companies a better edge over others. Thus, research into building more efficient MRS is majorly invested upon. The dependency on the streamer and item-based activity is the sole idea behind personalized recommendations. The study is focused on initiating algorithms based on Machine Learning (ML) models & Data Sciences (DS). The model has been worked so as to choose the effective model when required. Major models that the research has focused on is Genre Classification using metrics, Content-Based filtering, Collaborative filtering and the major new research ideology based on Forgetting curve Theory (FCT) that focuses on finding a correlation between model the concept that hypothesizes the decline of memory retention in time with music retention capabilities. Our Dataset is much or less based and augmented from the Spotify released Million Track Dataset[1] that has been published and licensed to Kaggle for research purpose.

Improving accuracy and diversity of personalized recommendation through power law adjustments of user similarities

A location-based orientation-aware recommender system using IoT smart devices and Social Networks

Online news are being spread through social media by news agencies to encourage people to read news from their site. After users log in to their site, users will continue to read the news if it is relevant to their personalized news recommendation. However, nowadays, a personalized recommendation could be provided to users if the site has records of users' browsing history, and users must have logged in to the news site. This could be problematic if the readers are anonymous users, and the site does not have enough browsing history records of these users. It will be difficult for news agencies to increase the number of daily readers on their site, especially if they have to compete with other agencies for providing a good personalized recommendation. Therefore, a personalized recommendation system is necessary for anonymous users who only visit the news site occasionally so that the website can recommend the news they prefer to read by implementing CSR Sparse Matrix Vector Multiplication and proximity processing as sparse matrix applied method. The Bayesian framework for user interest will also be implemented with the modification in its term. This research proved that personalized news recommendations could still be provided to readers with limited knowledge of the users' searching history.

Personalized recommendation systems have gained an increasing importance with the rapid development of Internet technologies. Collaborative filtering (CF) is the most promising technique in recommender systems, providing personalized recommendations to users based on their previously expressed preferences and those of other similar users. However, data sparsity and prediction accuracy are still major concerns related to CF techniques. Generally, the user-item matrix is quite sparse, which directly leads to the poor quality of predictions. In order to handle these problems, this paper proposes a novel approach to CF employing fuzzy case-based reasoning (FCBR), called CF-FCBR technique. Using fuzzy set theory for the computation of similarity between users and items, the proposed approach is twofold: offline and online. The offline processing is used to predict the missing values of user-item matrix and the online processing is employed for the process of recommendations generation. Our proposed approach helps in alleviating sparsity problem thereby improving recommendation accuracy. The experimental results clearly reveal that the proposed scheme, CF-FCBR is better than other traditional methods.

With the exponential growth of streaming services, the demand for effective recommender systems has become paramount. Recommender systems, powered by machine learning algorithms, play a critical role in analyzing user behavior and preferences to deliver personalized content recommendations. However, the abundance of available algorithms poses a challenge for streaming platforms in determining the most suitable approach for their recommender systems. This research paper presents an evaluation of various machine learning algorithms used in recommender systems for streaming services. Through a comparative analysis of collaborative filtering, content-based filtering, matrix factorization techniques, deep learning algorithms, and emerging approaches such as reinforcement learning and hybrid models, this study aims to provide insights into the strengths, limitations, and performance of each approach. By assessing the effectiveness of these algorithms, exploring their applications, and discussing recent advancements, this research contributes to the advancement of recommendation technology in streaming services, ultimately enhancing user satisfaction and engagement.

The OTT platform, which provides various media contents in the modern society, seeks to improve the user experience by utilizing a personalized recommendation system. However, current recommendation systems are having difficulty in making accurate recommendations because they do not fully understand user's tendencies. This study proposes the use of a personalized recommendation algorithm using BiVAE (bilateral variational autoencoder) to solve this problem, and considers group-specific applications through MBTI. BiVAE is a probabilistic generation model that compresses and generates data considering of bidirectional information flow, providing richer expression and reconstruction capabilities by encoding and decoding in both directions. As a result of applying four types of data to the model, when applying BiVAE for each group using MBTI, the accuracy of OTT genre recommendation was shown to be superior to that of other methods. In addition, when considering MBTI, a personal recommendation using BiVAE showed higher accuracy compared to the existing recommendation system. This result means that the personalized recommendation system considering the user's personal characteristics and personality is important.

Lifelong learners who select learning activities to attain certain learning goals need to know which are suitable and in which sequence they should be performed. Learners need support in this way-finding process, and we argue that this could be provided by using Personalised Recommender Systems (PRSs). To enable personalisation, collaborative filtering could use information about learners and learning activities, since their alignment contributes to learning efficiency. A model for way-finding presents personalised recommendations in relation to information about learning goals, learning activities and learners. A PRS has been developed according to this model, and recommends to learners the best next learning activities. Both model and system combine social-based (i.e., completion data from other learners) and information-based (i.e., metadata from learner profiles and learning activities) approaches to recommend the best next learning activity to be completed.

In the era of information abundance, the demand for personalized content recommendations has become paramount. Recommendation engines, particularly those employing collaborative filtering, play a pivotal role in delivering tailored suggestions based on user preferences. As technology evolves, the need to enhance the effectiveness of prediction algorithms within these engines becomes increasingly crucial. This research endeavors to contribute to this evolving landscape by delving into collaborative filtering methodologies, identifying challenges, and proposing novel strategies to elevate the accuracy and relevance of predictions in recommendation systems. Through this exploration, we aim to not only refine existing models but also pave the way for more sophisticated and reliable personalized content recommendations.
 This research aims to enhance prediction accuracy in recommendation engines utilizing collaborative filtering. Through an in-depth exploration of collaborative filtering techniques, we propose innovative approaches to improve the effectiveness of predictions. Our study addresses key challenges in collaborative filtering models, offering insights into refined algorithms and methodologies. By fine-tuning the collaborative filtering process, we anticipate a substantial boost in the overall performance of recommendation engines, ultimately advancing the field of personalized content suggestion. The simulation is performed using Java language and using two datasets Movie Lens 1M and Movie Lens 100K.The proposed model was evaluated using the Mean Absolute Error, Precision, and Recall.
 The proposed model achieved a mean absolute error value ranging between 0.78 and 0.84 using the Movie Lens 100K dataset, and a mean absolute error value ranging between 0.72 and 0.74 using the Movie Lens 1M dataset for different values of the number of user groups. As for precision and recall, the precision of the proposed model ranged between 0.97 and 0.985 using the Movie Lens 100K data set, and a precision value ranging between 0.944 and 0.954 using the Movie Lens 1M data set, also for different values of the number of user groups.
 As for the recall results, the proposed model achieved a recall value ranging between 0.755 and 0.85 using the Movie Lens 100K dataset, and a recall value ranging between 0.72 and 0.75 using the Movie Lens 100K dataset, also for different values of the number of user groups. These results were compared with the PMF, HPF, and NMF algorithms, where the proposed model proved its clear superiority over these algorithms. Using this analysis of the matrix allows us to obtain a good prediction accuracy of users' preferences and to find common groups of people with similar preferences.

Recommender systems (RSs) have become pillars of personalization in contemporary retail that is improving customer experience and business value. With the help of sophisticated algorithms, retailers will be able to offer personalized product recommendations taking into account the personal preferences of consumers and thus minimizing the information overload and making decisions more easily. Most recommender systems are based on two major approaches, which are Collaborative Filtering (CF) and Content-Based Filtering (CBF). Unlike CBF which pays attention to item attributes and individual interactions of a user to create an item, CF examines user behaviors and preferences of a population to determine the relevant items. The hybrid models unify these two methods in an attempt to ease their disadvantages, which include cold-start, sparsity, and lack of diversity. In addition to the development of algorithms, combination of deep learning and diversification methods has also contributed to the relevance, novelty, and adaptability of recommendations. This review explores the application of recommender system in retail with an overview of the principles of CF and CBF, their contribution to customer engagement, and the latest trends in hybrid and AI-driven recommendation systems. This paper synthesizes the main body of research to show the potential transformational nature of personalized recommendations in maximizing customer satisfaction, loyalty, and revenue generation and outlining the challenges and future research opportunities to further enhance the use of personalized recommendations in retail.