Big Data Analytics for Personalized Recommendation Systems

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

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.

Although personalized recommendation system is widely applied on various platforms and commercial websites, fewer studies have examined the impact of personalized recommendations on digital satisfaction and the experience of users from their perspective. Will the attitude toward personalized recommendations affect digital satisfaction? Why do some people hold a negative attitude towards personalized recommendations? This study uses the quantitative research methods of questionnaires to collect data with a sample size of 1002. The result shows that there is a positive correlation between the attitude toward personalized recommendations and digital satisfaction. Approximately 25.4% of participants reported that they do not prefer personalized recommendations who are senior people having a low sense of media literacy and online experience with lower information overload. This article adopts information overload theory and user involvement theory to explain the correlation between personalized recommendation and digital satisfaction involving the discussion of its advantages and disadvantages, attempting to call attention to the neglected Generation X.

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.

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.

We have been witnessing digital music industry has been grown rapidly in recent years. One Innovation which helps this industry improving its customer satisfaction, customer loyalty, and customer engagement, is personalized recommendation system. It has not only been an interesting innovation in digital music service, but also in almost all digital services. Telkomsel, the leader of the cellular industry in Indonesia, would also like to compete in providing“On Demand Music Streaming Service” by launching its own brand, Langit Musik and personalized recommendation system is supposed to be one of the improvements which can be implemented on top of it. Unfortunately, this personalized recommendation was not yet implemented. This research builds model to predict customer preferences for artists in Langit Musik service, to provide more personalized recommendations for each customer. This study applies an implicit preference from the amount of music listening for customers in period of l and3 months, from Mobile Apps and Unstructured Supplementary Service Data (USSD). The modeling uses a collaborative filtering approach with matrix factorization method and measure the model accuracy using Receiver Operating Characteristic / Area Under the Curve (ROC/AUC). The AUC value indicates the prediction quality of the model above prediction from random method. In addition, it was also concluded that the matrix factorization method provides advantages in resource efficiency. The contribution of this research is to improve the customer experience, satisfaction, loyalty and engagement to Langit Musik.

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.

Short videos have become increasingly popular in recent years, leading to the rise of many short video platforms with large user bases, such as leading platforms like TikTok. The user groups on these platforms are vast. With the rise of short-video platforms, TikTok, as one of the leading players, has become a highly influential social media platform worldwide. With the development of short videos, personalized recommendations are also playing an increasingly important role in short videos and are constantly evolving. This report examines the definition, classification, and evolution of personalized recommendation systems, along with their applications and the development trends and suggested countermeasures in short video platforms, with the objective of analyzing the current status and emerging trends of personalized recommendations in this domain. Research has found that personalized recommendations are of great help to the development of short videos. Enterprises need to keep up with the times and constantly update their personalized recommendation systems to enhance user experience and retain more users. Meanwhile, users also need to improve their ability to distinguish during the process of using short videos. In addition, the government should supervise enterprises to ensure the healthy application and development of personalized recommendations in short videos. It provides valuable reference information for content creators, short video platforms, and end users.

Systems involving artificial intelligence (AI) are protagonists in many everyday activities. Moreover, designers are increasingly implementing these systems for groups of users in various social and cooperative domains. Unfortunately, research on personalized recommendation systems often reports negative experiences due to a lack of diversity, control, or transparency. Providing a meta-analysis of the interaction design strategies for group recommendation systems (GRS) offers designers and practitioners a departure to address these issues and imagine new interaction possibilities for this context. Therefore, we systematically reviewed the ACM, IEEE, and Scopus digital libraries to identify GRS interface designs, resulting in a final corpus of 142 academic papers. After a systematic coding process, we used descriptive statistics and thematic analysis to uncover the current state of the art regarding interaction design strategies for GRS in six areas: (1) application domains; (2) devices chosen to implement the systems; (3) prototype fidelity; (4) strategies for profile transparency, justification, control, and diversity; (5) strategies for group formation and final group consensus; and, (6) evaluation methods applied in user studies during the design process. Based on our findings, we present an exhaustive typology of interaction design strategies for GRS and a set of research opportunities to foster human-centered interfaces for personalized recommendations in cooperative and social computing contexts.

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.

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.

This study aims to explore the impact of personalized news recommendation systems based on big data and artificial intelligence algorithms on audience cognition and behavior. With the rapid development of information technology, news recommendation systems have been widely used in the media industry, but their impact on audiences has not been fully studied. This paper adopts a mixed research method, combining questionnaire surveys, in-depth interviews and experimental research, and conducts a three-month follow-up survey on 500 news app users. The results show that the personalized recommendation system significantly increases users' news reading volume and retention time, but also leads to the intensification of information cocoon effect and cognitive bias. In addition, the study finds that the transparency of the recommendation algorithm, the user's media literacy level and the diversity of news sources are key factors affecting audience cognition and behavior. Based on the research findings, this paper proposes suggestions for optimizing the news recommendation system, improving users' media literacy and promoting information diversification, in order to minimize the negative impact of personalized recommendations while improving user experience. This study has important theoretical and practical significance for understanding the characteristics of audience behavior in the intelligent media environment, improving the design of news recommendation systems and formulating relevant policies.

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

In the traditional e-commerce system, the keyword matching algorithm is used to implement the function of commodity search, and only the goods containing the customer input keyword can be obtained. In view of this limitation, a personalized recommendation system for e-commerce based on big data analysis is studied. The use of improving the search algorithm for goods, personalization system rules of e-commerce and text matching algorithm are introduced, so in the search of goods, it not only displays the exact matching of goods, but provides the goods similar to their requirements, for reference. The research shows that the personalized recommendation system of e-commerce based on big data analysis can not only increase the transaction opportunities, explore potential customers, but also improve the level of personalized service. This is very important for the enterprise economy and the personalized development of e-commerce.

Simulation of E-learning in English personalized learning recommendation system based on Markov chain algorithm and adaptive learning algorithm