User's Taste Research Articles

Trip Planner (using Generative AI)

The aim of this project is to develop an AI based travel planner that intends to alter planning and excursion encounters for people. Using sophisticated models and instructions for computers, such as statistical techniques applied in learning how humans learn best from their past experiences so they can anticipate future instances on them even when these are unknown for sure beforehand; data showing different types of transportation methods including those involving moving oneself put up for sale together with lodging places like hotels; field trips or tours where individuals can go out and participate in other events besides just sitting at home – all serving as input sources yielding recommendations specific enough standalone resources without being overly complex through averaging available data over time (Hong 2012). The In this regard, the trip planner will evaluate the personal user tastes, historical information concerning the destinations, as well as the contemporary pieces of advice on where to go today, where to get an accommodation or how to move by different types of vehicles, what to do. Besides, the system will support the natural language communication.

Development of a Hybrid Decision Support System for Selecting the Air Filters Based on Multiple Criteria

To select an air purifier that fully meets the needs of consumers, this study was conducted to provide the most objective and effective suggestions in making decisions and evaluating different smart purifier options that suit the tastes of users. Such decision-making is complicated for non-professionals when there are countless large and small brands on the market today with many different prices, features, and incentives. In such complex situations, decision-making with TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) can be used to eliminate risk and to better represent the preferences of decision-makers. From modeling to using the Entropy method and the TOPSIS method, all are done to serve the ultimate goal of choosing a good air purifier that meets the needs of customers.

Music Recommendation Using Facial Emotion Recognition

It can be very befuddling for people to choose which music to tune in to from a wide run of alternatives accessible. Different proposal frameworks have been made for particular spaces like music, feasting, and shopping, catering to the user's inclinations. Our essential objective is to supply music recommendations that adjust with the user's taste. By analyzing facial expressions and client feelings, ready to pick up experiences into their current mental or enthusiastic state. Music and recordings offer a extraordinary opportunity to show clients with a huge number of choices based on their slants and past data. It is well known that humans make use of facial expressions to express more clearly what they want to say and the context in which they meant their words. More than 60 percent of the users believe that at a certain point of time the number of songs present in their songs library is so large that they are unable to figure out the song which they have to play. By developing a recommendation system, it could assist a user to make a decision regarding which music one should listen to helping the user to reduce his/her stress levels. The user would not have to waste any time in searching or to look up for songs and the best track matching the user’s mood is detected, and songs would be shown to the user according to his/her mood. The image of the user is captured with the help of a webcam. The user’s picture is taken and thenas per the mood/emotion of the user an appropriate song from the playlist of the user is shown matching the user’s requirement.

Design of music recommendation system based on EDA and K-means cluster analysis

This paper describes the construction and analysis of a music recommendation system, through visual and exploratory analysis of the open source dataset of music recommendation system in the UCL database, exploring the relationship between music popularity and music acoustic properties, using the K-means clustering algorithm for clustering analysis of genres and songs, and finally building a music recommendation system that can, based on the user input of music data, to match the ten music songs that best meet the listener's taste. The analyses in this paper show that there is a certain correlation between music popularity and the indicators of sound, danceability, energy, instrumentality, liveliness, loudness, language, rhythm, mood, duration and tone. The indicators of energy, liveliness, loudness and tempo are positively correlated with the popularity of music, while the indicators of mood and duration are negatively correlated with the popularity of music. In addition, this paper uses K-means clustering algorithm to cluster analysis of genres and songs, and shows the similarity and difference between different genres and songs through visualisation, which provides important data support for the construction of music recommendation system. Finally, this paper establishes a music recommendation system based on K-means clustering algorithm, which can match the ten music songs that best meet the listener's taste according to the music data input by the user. The system is intelligent and personalised, and can recommend music works that are more in line with the user's taste based on the user's preferences and historical listening records. The results of this paper provide useful reference and support for the algorithms and models of the music recommendation system, which can be further researched in depth in the future to improve its intelligence and personalisation level and provide users with better music services.

A Cross-Platform Movie Filtering and Recommendation System Using Big Data Analytics

One of the best forms of entertainment today is online multimedia with movies and various other entertainment sources at the tip of our fingers. However, finding a movie from the numerous options available in a movie database to suit a user's taste is a non-trivial task. Most recommendation systems do not consider the user's limitations, such as whether the user has the means to watch the movie suggested by the system. This paper proposes a system to provide recommendations that are platform specific. Two sources of movies have been considered, viz., Netflix and Amazon Prime, and other data sets can be included as well. In addition to providing recommendations, the proposed system helps filter movies based on user-specified criteria. It applies big data analytics for faster execution of both movie recommendation and data filtering and can work with large and distributed movie databases.

An Intention-Aware Markov Chain Based Method for Top-K Recommendation

Recommender systems play significant roles in business, especially in e-commerce. Nevertheless, users’ behaviors are usually mixing and drifting, which is hard to tackle. Current sequential methods of item-wise interest extracting suffer from the intricacy and sparsity of data. Inspired by that category-wise interests may be intrinsic drivers of user behaviors and heterogeneous actions may reveal certain behavior patterns, we introduce intention as a tuple of category and action to address the data issues. In this paper, an intention-aware Markov chain based sequential recommendation model (IMRec) is proposed. We model the overall preferences of users as the integration of long-term preferences and short-term intents. In particular, the matrix factorization method is adopted to extract the long-term user-item preference. For the modeling of the short-term intents transition, we adopt high-order Markov chain based methods. A factorized mixture transition distribution model for high-order Markov chain approximation is leveraged in this paper to reduce the algorithm complexity. An auxiliary loss on intention representation is utilized, which brings considerable performance improvements. Experiments on real-world datasets demonstrate that our model outperforms state-of-the-art baseline models in terms of three common metrics, and shows superior stability, scalability, and training efficiency. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —In e-commerce, sequential recommenders are essential to facilitate decision-making and promote business. A big challenge is to capture the sequential patterns from the mixing and drifting user-item interaction sequences. Customer behaviors are often driven by their inherent intentions, which may present more stable and reliable patterns. Motivated by that, we propose a novel intention-aware next-item recommendation algorithm with high performance. Our method models the user-item preferences as the integration of long-term preferences and short-term intents. More specifically, long-term preferences show the general tastes of users, which are modeled by latent factors of users and items. Additionally, the intention is defined as a tuple of category and action. We model the short-term intents as the intention transition from the past intentions by a high-order Markov chain. We further leverage a factorization-based mixture transition distribution model for high-order Markov chain approximation and reduce the algorithm complexity. The proposed model is validated in 4 real-world datasets and shows good recommendation performance, performance stability, model scalability, and training efficiency. Our model provides an effective recommendation method at low computational costs for e-commerce companies.

Research on Tiktok Marketing Strategy of Chinese Sports Brands

With the continuous progress of technology and the popularization of social media, the way of brand marketing has gradually changed. Traditional advertising channels can no longer meet the needs of today's young consumers, so brands have turned to social media platforms to cater to the tastes of young users, and sports brands have begun to focus on digital marketing channels to attract more consumers. As one of the most popular short-form video platforms in China, Jieyin, with its large user base and high degree of interactivity, has become one of the most popular platforms that brands are competing to enter. Taking Li Ning, a famous Chinese sports brand, as an example, this paper uses the data method and logical analysis to study the effectiveness and impact of the ShakeEn marketing strategy, and discusses how Li Ning utilizes the ShakeEn platform to expand brand awareness, increase sales and interact with consumers.

Effect of collaborative-filtering-based recommendation algorithms on opinion polarization.

A central role in shaping the experience of users online is played by recommendation algorithms. On the one hand they help retrieving content that best suits users taste, but on the other hand they may give rise to the so-called "filter bubble" effect, favoring the rise of polarization. In the present paper we study how a user-user collaborative-filtering algorithm affects the behavior of a group of agents repeatedly exposed to it. By means of analytical and numerical techniques we show how the system stationary state depends on the strength of the similarity and popularity biases, quantifying respectively the weight given to the most similar users and to the best rated items. In particular, we derive a phase diagram of the model, where we observe three distinct phases: disorder, consensus, and polarization. In the last users spontaneously split into different groups, each focused on a single item. We identify, at the boundary between the disorder and polarization phases, a region where recommendations are nontrivially personalized without leading to filter bubbles. Finally, we show that our model well reproduces the behavior of users on the online music platform last.fm. This analysis paves the way to a systematic analysis of recommendation algorithms by means of statistical physics methods and opens the possibility of devising less polarizing recommendation algorithms.

Culture Shock of the Covid-19 on TikTok: Users Taste Shift for Video Content in China

The Covid-19 pandemic has had a global impact and served as an economic and cultural shock. In 2022, Shanghai was in an entire lockdown for two months due to quarantine policies adopted by China to prevent the pandemic from spreading. During this period, social media became a crucial part of peoples lives, used for communication, entertainment, and receiving news. This article investigates the post-pandemic effect of Covid-19 on social media by conducting a study using customized surveys to examine users preference shift of video content on TikTok, Chinas most popular short-video social platform. The study found a correlation between TikTok users preference shift for short video content and the Covid-19 pandemic in China. The participants were divided into social groups based on age, gender, and occupation, but the study did not find any significant differences in their responses to this correlation. There were high shifts in taste for video content related to the Covid-19 pandemic, such as health news and indoor exercise. The study shows that shifts in the purpose of using social media are significant when experiencing a cultural shock. Users often respond to cultural shock by switching to certain types of video content. Future scholars studying media psychology and exploring the elements that shift consumers gratification and purposes of using social media can benefit from this article.

Anticancer Recipe Recommendation Based on Cancer Dietary Knowledge Graph

Many recipes contain ingredients with various anticancer effects, which can help users to prevent cancer, as well as provide treatment for cancer patients, effectively slowing the disease. Existing recipe knowledge graph recommendation systems obtain entity feature representations by mining latent connections between recipes and between users and recipes to enhance the performance of the recommendation system. However, it ignores the influence of time on user taste preferences, fails to capture the dependency between them from the user’s dietary records, and is unable to more accurately predict the user’s future recipes. We use the KGAT to obtain the embedding representation of entities, considering the influence of time on users, and recipe recommendation can be viewed as a long-term sequence prediction, introducing LSTM networks to dynamically adjust users’ personal taste preferences. Based on the user’s dietary records, we infer the user’s preference for the future diet. Combined with the cancer knowledge graph, we provide the user with diet recommendations that are beneficial to disease prevention and rehabilitation. To verify the effectiveness and rationality of PPKG, we compared it with three other recommendation algorithms on the self-created datasets, and the extensive experimental results demonstrate that our algorithm performance performs other algorithms, which confirmed the effectiveness of PPKG in dealing with sequence recommendation.

Enhanced fractional prediction scheme for effective matrix factorization in chaotic feedback recommender systems

The biggest wish of the e-commerce industry is to forecast and estimate the taste and repugnance of users by utilizing user ratings for various products. Chaotic users' feedback drives the e-commerce industry to develop efficient, robust, and smart recommendation systems with the capability to deal with the inconsistencies in users' rating patterns and provide extremely related, appropriate, and timely recommendations. A few fractional order-based solutions for an effective matrix factorization procedure are suggested to boost recommender systems' performance regarding recommendations speed and precision. We further investigate a newly proposed enhanced fractional stochastic gradient descent (EFSGD) technique for accelerating recommendations speed through efficient matrix factorization. The Faa di Bruno fractional derivative used in EFSGD exploits the users' historical feedback for providing accurate predictions. As a result of the growing computational complexity of recommendation models, it will a challenge for EFSGD to extract group of prominent features and ignore the cluster of redundant users' and items' latent attributes for increasing the recommendation speed and reducing the computational complexity respectively. Therefore, (a) to resolve chaotic users' ratings patterns problem and (b) reduce the complexity by identifying and selecting groups of highly correlated latent features, an innovative elastic net regularized enhanced fractional adaptive model is developed for efficient matrix factorization. The proposed model for recommender systems outperforms baseline and state-of-the-art in terms of convergence speed, computational cost, and prediction accuracy. The substantial performance of the suggested strategy is verified through various latent factors, ratings prediction-based valuation measures, learning-rates, and fractional order values. Whereas the authenticity is verified through Movie-Lens and FilmTrust datasets.

All-in-one picture: visual summary of items in a recommender system

Navigation through large volumes of images is a complex and tedious task that requires tools to facilitate the exploration and discovery of visual information. Photo summaries are one of these tools, which consist of selecting a reduced set of images that best represent the original data source. However, creating photo summaries in the context of recommender systems poses several challenges: How to select the most relevant images for each item? How to encode each image? How to evaluate the quality of the generated summary? In this manuscript, we propose a clustering-based method to create a visual summary in the context of a restaurant recommender system, which includes the photos taken by users who visited the restaurants (items) in a given city. These photos are encoded using a deep neural network that takes into account not only their content but also the relationships between users and restaurants. This encoding will allow us to create a visual summary that captures the essence of user tastes and illustrates the gastronomic offer of the city. We also propose a similarity measure between items based on the users who have visited them and an evaluation method that calculates to what extent the summary obtained represents the original data source. The experimentation carried out includes five datasets and the obtained results demonstrate the adequacy of our proposal for the construction of these summaries.

Individualized Movie Recommendation Algorithm Based on Machine Learning

With the continuous acceleration of economic growth and the remarkable improvement of people's living standards, people's individualized needs have increased. Moreover, due to the growth of computer technology and network technology, many forms of entertainment and services have been integrated into the network. The recommendation engine actively pushes the information that meets the user's taste and preference to the user by analyzing the user's behavior data on the website when the user's needs are not clear. This article will study how to apply the deep learning algorithm to the field of movie recommendation system to solve the problems existing in the traditional recommendation algorithm based on machine learning (ML), and combine the existing mainstream big data (BD) to improve the execution efficiency of the model. Individualized movie recommendation system saves users the time of searching, viewing and screening, and discovers users' potential movie preferences at the same time. Most users think that the algorithm can help them find the type they want from a large quantity of film and television resources, so as to meet the needs of users and tap their potential interests.

Regulating online search in the EU: From the android case to the digital markets act and digital services act

This paper offers an analysis of the impacts of public regulations of internet search. Through a theoretical model, we consider three regulations adopted by the EU. The first is the Android choice screen implemented in the European Economic Area in 2020. The other two are the recently adopted Digital Markets Act (DMA) and Digital Services Act (DSA). We find that interventions involving user choice, like the Android choice screen, can be strengthened by other interventions focused on search engine quality and data portability. Since the regulations in the DMA target these multiple dimensions simultaneously, we expect it to deliver the strongest and most effective impacts on the online search market. We also argue that the impacts of the regulations may vary over different time horizons: when first implemented, the DSA may simply direct users to search engines with better privacy protection, but over time it has the potential to change user tastes for privacy and, hence, drive search engines to invest more in privacy protection.

Modelling Parking Choice Behaviour Considering Alternative Availability and Systematic and Random Variations in User Tastes

Parking choice models proposed over recent years have been aimed at choosing the relevant variables and pricing policies affecting said choice. In our analyses, we have encountered some gaps to cover; for this reason, we introduce a methodology for addressing user parking choices, including the availability of parking alternatives in each of the studied zones presenting pivoted scenarios. This methodology has been developed based on a stated preferences survey design to define user behaviour when making parking choices through discrete-choice models. Furthermore, the estimated models have considered systematic and random variations in user tastes and the correlation between similar or related parking alternatives. This methodology has made it possible to provide greater realism in generating fictitious parking scenarios. The results show the importance of the fee attribute and the differences in the users’ preferences depending on whether they live inside or outside the study area and their arrival time at the destination. Several scenarios have been simulated, and in these scenarios, it is found that removing free parking areas or restricting free parking in favour of parking fee regulation in congested zones can be an efficient policy measure to promote other parking alternatives and reduce the number of vehicles searching for a parking space.

An Efficient Approach to Manage Natural Noises in Recommender Systems

Recommender systems search the underlying preferences of users according to their historical ratings and recommend a list of items that may be of interest to them. Rating information plays an important role in revealing the true tastes of users. However, previous research indicates that natural noises may exist in the historical ratings and mislead the recommendation results. To deal with natural noises, different methods have been proposed, such as directly removing noises, correcting noise by re-predicting, or using additional information. However, these methods introduce some new problems, such as data sparsity and introducing new sources of noise. To address the problems, we present a new approach to managing natural noises in recommendation systems. Firstly, we provide the detection criteria for natural noises based on the classifications of users and items. After the noises are detected, we correct them with threshold values weighted by probabilities. Experimental results show that the proposed method can effectively correct natural noise and greatly improve the quality of recommendations.

Accurate bundle matching and generation via multitask learning with partially shared parameters.

How can we recommend existing bundles to users accurately? How can we generate new tailored bundles for users? Recommending a bundle, or a group of various items, has attracted widespread attention in e-commerce owing to the increased satisfaction of both users and providers. Bundle matching and bundle generation are two representative tasks in bundle recommendation. The bundle matching task is to correctly match existing bundles to users while the bundle generation is to generate new bundles that users would prefer. Although many recent works have developed bundle recommendation models, they fail to achieve high accuracy since they do not handle heterogeneous data effectively and do not learn a method for customized bundle generation. In this paper, we propose BundleMage, an accurate approach for bundle matching and generation. BundleMage effectively mixes user preferences of items and bundles using an adaptive gate technique to achieve high accuracy for the bundle matching. BundleMage also generates a personalized bundle by learning a generation module that exploits a user preference and the characteristic of a given incomplete bundle to be completed. BundleMage further improves its performance using multi-task learning with partially shared parameters. Through extensive experiments, we show that BundleMage achieves up to 6.6% higher nDCG in bundle matching and 6.3× higher nDCG in bundle generation than the best competitors. We also provide qualitative analysis that BundleMage effectively generates bundles considering both the tastes of users and the characteristics of target bundles.

PENYULUHAN DAN PENDAMPINGAN PENGOLAHAN KEARIFAN LOKAL IKAN BANDENG TARAKAN SEBAGAI UPAYA PENANGANAN STUNTING PADA BALITA

Community empowerment in communities affected by covid_19, especially housewives who have stunted toddlers provides its own color for business development that leads to the utilization of milkfish which is processed into various variants in line with the development of technological innovation in the culinary field of society. The methods used are outreach, training and mentoring, monitoring, and evaluation. The training participants were housewives from Gunung Lingkas Village, Tarakan City. The results achieved are that the processing of milkfish is a representative form of changing the paradigm of society in utilizing milkfish which has been processed traditionally with existing local ingredients, then switches to modern processing using modern technology to give the taste and tastes of service users who are passionate about Modern processed foods such as Milkfish Presto, Shredded Milkfish, Milkfish Sticks and Milkfish Crackers. "Through training on processing milkfish products with various and innovative variants" which has been organized by the Community Service Team at the University of Borneo, it is hoped that it can increase knowledge and skills for housewives to be able to process milkfish in a more innovative and modern way to increase entrepreneurial activity and income among them. The training participants were very enthusiastic about participating in these activities and the participants had great hopes that community service activities such as milkfish processing training could be carried out on an ongoing basis and by providing various materials to increase their knowledge and skills towards a prosperous society in the future

Computational Photography and Image Editing for Artistic C Expression

The advent of computational photography and advanced image manipulation techniques has sparked a creative renaissance in the visual arts, giving artists and photographers fresh opportunities to express themselves via their work. This study surveys the current literature on the use of computational photography and image manipulation for creative expression, illuminating the many methods, strategies, and tools that have been developed and deployed by visual artists and photographers. We also provide a number of methods for collecting data that may be used to learn about the tastes of users and the obstacles they face when trying to come up with original content. The findings of this study have important implications for the application of computational photography and image editing in the future of artistic expression, as these methods and tools can help artists produce more expressive and creative photographs and elevate the role of technology in the creative process. Advanced machine learning techniques, neural style transfer, and improved tool and interface development should be the focus of future study in this field. We predict further development and expansion of the field of computational photography and image modification as a means of artistic expression, as evidenced by the findings of this study.

Sistem Rekomendasi Content-based Filtering Menggunakan TF-IDF Vector Similarity Untuk Rekomendasi Artikel Berita

The population of active students in the Informatics Bachelor Program, Universitas Amikom Yogyakarta, in the odd semester of 2021 is 3,870. Efforts to track interest in the three concentration options were carried out early on through article literacy recommendations. Various articles are produced continuously and provided on an ongoing basis to students. However, the many articles offered daily make students overwhelmed and tend to choose articles that do not match what they want. To help solve this problem, recommender system is developed. A recommender system helps to estimate the prediction value or relevancy of an article and create a ranking according to user's taste. Content-based Filtering technique is used in this research. Using the dataset from Kabar Informatika news portal of University of Amikom Yogyakarta, the developed Content-based Filtering Recommendation System is able to produce Recall@5 score at around 73% and Recall@10 at around 80%.