Changes In User Preferences Research Articles

A novel method for a technology enhanced learning recommender system considering changing user interest based on neural collaborative filtering

This study introduces an advanced recommender system for technology enhanced learning (TEL) that synergizes neural collaborative filtering, sentiment analysis, and an adaptive learning rate to address the limitations of traditional TEL systems. Recognizing the critical gap in existing approaches—primarily their neglect of user emotional feedback and static learning paths—our model innovatively incorporates sentiment analysis to capture and respond to nuanced emotional feedback from users. Utilizing Bidirectional Encoder Representations from Transformers for sentiment analysis, our system not only understands but also respects user privacy by processing feedback without revealing sensitive information. The adaptive learning rate, inspired by AdaGrad, allows our model to adjust its learning trajectory based on the sentiment scores associated with user feedback, ensuring a dynamic response to both positive and negative sentiments. This dual approach enhances the system’s adaptability to changing user preferences and improves its contentment understanding. Our methodology involves a comprehensive analysis of both the content of learning materials and the behaviors and preferences of learners, facilitating a more personalized learning experience. By dynamically adjusting recommendations based on real-time user data and behavioral analysis, our system leverages the collective insights of similar users and relevant content. We validated our approach against three datasets—MovieLens, Amazon, and a proprietary TEL dataset—and saw significant improvements in recommendation precision, F-score, and mean absolute error. The results indicate the potential of integrating sentiment analysis and adaptive learning rates into TEL recommender systems, marking a step forward in developing more responsive and user-centric educational technologies. This study paves the way for future advancements in TEL systems, emphasizing the importance of emotional intelligence and adaptability in enhancing the learning experience.

End-to-end AI pipeline optimization: Benchmarking and performance enhancement techniques for recommendation systems

End-to-end AI pipeline optimization is critical for improving the efficiency and performance of recommendation systems, which play a pivotal role in personalizing user experiences across various domains. This review explores benchmarking and performance enhancement techniques tailored to recommendation systems within AI pipelines. The objective is to streamline the processes involved in data ingestion, feature engineering, model training, and deployment to achieve optimal system performance and user satisfaction. Recommendation systems typically involve complex workflows that require continuous optimization. Benchmarking serves as a foundational step, enabling the identification of bottlenecks and inefficiencies within the pipeline. By establishing clear performance metrics, such as precision, recall, and latency, benchmarking allows for the comparative analysis of different algorithms, data processing methods, and system configurations. These metrics guide the selection of the most suitable models and techniques, thereby enhancing overall system effectiveness. Performance enhancement techniques are then applied to various stages of the AI pipeline. Advanced methods in feature engineering, such as automated feature selection and dimensionality reduction, can significantly improve model accuracy while reducing computational overhead. In the model training phase, techniques like hyperparameter tuning, gradient-based optimization, and distributed training are employed to accelerate convergence and improve model generalization. Additionally, optimization strategies at the deployment stage, including model compression, quantization, and the use of specialized hardware, are crucial for minimizing latency and resource consumption. This review also highlights the importance of continuous monitoring and feedback loops to maintain the effectiveness of recommendation systems in dynamic environments. By integrating real-time analytics and adaptive algorithms, systems can adjust to changing user behaviors and preferences, ensuring sustained performance improvements. In conclusion, optimizing end-to-end AI pipelines for recommendation systems involves a multifaceted approach that includes benchmarking, feature engineering, model training, and deployment enhancements. These efforts collectively contribute to more efficient, scalable, and accurate recommendation systems, ultimately leading to better user experiences and operational efficiencies. This paper will focus on optimizing AI pipelines for recommendation systems, covering the entire process from data extraction and feature engineering to model deployment and performance benchmarking. It will discuss techniques for identifying and mitigating performance bottlenecks in different computing environments, providing valuable insights for enhancing the efficiency of recommendation systems, which are crucial for various applications.

Dynamic Playlist Generation using Machine Learning

This study investigates the application of machine learning algorithms for dynamic playlist generation in the context of music recommendation systems. The traditional method of creating playlists frequently depends on immutable standards, like artist or genre, which might not adequately represent the unique and dynamic character of personal preferences. On the other hand, our suggested system makes use of machine learning methods to examine user behavior, preferences, and contextual elements in order to create playlists that are dynamically created and customized to each user’s individual preferences. In order to train machine learning models, the study collects user interaction data, such as listening history, skip patterns, and user feedback. To extract patterns and relationships from the data, a variety of algorithms are used, including hybrid models, content-based filtering, and collaborative filtering. After that, the models are incorporated into a dynamic playlist creation system that can eventually adjust to changing user preferences.Our test findings show how well the suggested strategy works to improve user experience by offering more interesting and customized playlists. With its ability to adjust to changing user preferences and contextual cues, the dynamic playlist generation system provides a smooth and pleasurable music discovery experience. We also talk about possible enhancements, implementation difficulties, and deployment considerations in the real world.This work adds to the ongoing efforts to improve music recommendation systems by demonstrating how machine learning can be used to develop more responsive and intelligent playlist generation systems. The results highlight how crucial customized experiences are in the constantly changing world of digital music consumption. Index Terms—hybrid mode, content-based filtering, intelligent playlist, music

A DQN-based traffic classification method for mobile application recommendation with continual learning

With the popularity and development of smartphones, many mobile applications of various types have emerged. How to recommend mobile applications that match the user’s preferences and usage habits among the massive applications is a problem that needs to be solved. Traditional mobile application recommendation methods cannot dynamically track user behavior and preference changes in time and cannot timely correct the recommendation model, resulting in poor recommendation effects. The continual update of mobile applications will also invalidate the recommendation model based on traffic classification. To solve these problems, this paper proposes A Deep Q-Network (DQN) based traffic classification method for mobile application recommendation with continual learning, which embeds a DQN-based traffic classification model in the mobile terminal and sets up a reward and punishment mechanism to achieve self-supervised learning. By continuously adjusting and optimizing the model, the effectiveness of the traffic classification model is ensured, and the recommendation model is provided with accurate and reliable user behavior data support. Experiments on the ISCX and private datasets show that the proposed method performs better and can effectively guarantee the accuracy of the classification model.

Onboard autonomous mission generation method based on user preference

This paper presents a method for autonomous onboard mission generation of Earth observation imaging satellites based on user preferences. The paper develops a planning database to store the previous user mission requirements, and proposes a database updating algorithm to track the changes in user preferences. The paper also proposes a mission generation method based on the planning database, which predicts target priorities and imaging methods. Finally, a mission allocation strategy is used to assign newly generated missions to the appropriate satellites. Simulations results show that the proposed method can effectively improve the utilization of satellite resources and thereby obtain higher mission return.

Reliable sleep temperature regulation based on PMV model and dynamic thermal parameters

Quality of nighttime sleep is crucial. The common practice of air conditioners' sleep mode is to recommend a fixed sleep curve or suggest a pre-trained temperature curve based on geologic location. However, these models neglect the user preference and dynamic changes in the room's thermal environment, making it difficult to satisfy people of different ages and genders and resulting in discomfort during sleep. Demanding user to input their preferences or private data causes privacy issues, which leads to a bottleneck in solving this problem. Therefore, the paper proposes a new dynamic thermal parameter model for sleep, which learns the users' metabolic rate and thermal resistance of clothing and bedding through the user's undetected setting before sleep and simple feedback after waking up. These parameters fully reflect the inherent preferences of users and the thermal environment of rooms. After 1–2 rounds of feedback, the model can simulate the needs of users pretty well. In addition, we have optimized the PMV sleep model based on the dynamic thermal parameter model to regulate the temperature of air conditioner. In the controlled experiment, 84% of the participants expressed satisfaction with the regulated sleep temperature environment using the new model, which is 17% higher than that of the fixed curve. This model establishes a personalized model for users without obtaining their privacy information. In future research, wind speed control and humidity control can be introduced, and other sleep monitoring devices can be added to achieve more precise air conditioning temperature control.

Metropolitan Marvels : To Forge Seamless Possibilities for Urban Discoverability and Connectivity

Embodies a particular approach with the devising set out to enrich the quality of the city living experience incorporating technology and real human relationships in applying information in the city. The research journey is going to trace dynamic pathways underpinning in software development through powerful tools like Android Studio among others that helps in designing simple and user-friendly interfaces that easily adjust themselves to the diversity of the residents who live in cities and visitors come to the city. It now focuses to build proper links of implementation not on the technological strides but only by increasing the spotlight. Reliability as well as availability is ensuring that hustling has a strong back-end that would handle the data smoothly by the use of applications like Firebase and Room Persistence. Added to it, the approach makes use of offline capabilities as well part of validation of strict security precursors for performing through real-user trials while designing the likes of the application in conformance to the changing user preferences of the urban communities. This methodology visualizes transformative fostering of people-centric cities in successfully intricacy technology by richness of human experiences within innovative city information application, and elevated urbanlifestyles.

Efficient management of HVAC systems through coordinated operation of parallel chiller units: An economic predictive control approach

In developed countries, air conditioning systems have become major contributors to energy consumption in buildings. Cooling installations made up of independent chiller units connected in parallel pose a challenge in finding the most energy-efficient management strategy. This article proposes a novel economic model predictive control strategy to optimize the operation of multiple-chiller HVAC systems according to an economic cost comprising energy consumption as well as a thermal comfort index. Provided that the gradient of the economic cost function can be calculated or estimated, the proposed controller only entails the solution of a single quadratic programming (QP) problem at each sampling period, reducing computational requirements and thus facilitating the deployment on commercial embedded HVAC management platforms. The proposed controller improves economic performance. As our numerical analysis shows, optimal sequencing is achieved for the case of dual-chiller plants. Moreover, the controller adapts to possible variations in the economic criteria, enabling the system to respond to changes in electricity price and user preferences. A realistic case study, using a high fidelity model of a building simulated in TRNSYS, demonstrates the effectiveness of the proposed methodology. In particular, the proposed controller demonstrates to be more efficient than state-of-the-art QP-based economic predictive controllers in achieving a reduction of the energy consumption up to 5.19%, which is in line with the targeted reduction of 7.9% by 2030 in the EU Green Deal's ‘Fit for 55’ package.

Personalized product ranking system for enhanced user experience

Businesses looking to engage and satisfy their online audience must prioritize the user experience in the quickly changing digital landscape. In order to address this challenge, this project proposes and implements a sophisticated algorithmic solution designed to produce extremely accurate and user-centric product rankings. This represents a groundbreaking approach. The system attempts to predict and present the most relevant product suggestions by meticulously considering a wide range of factors such as user preferences, historical interactions, product popularity trends, and user similarity metrics. Our methodology is distinguished by the use of a dynamic simulation environment in which user profiles, product categories, and interaction pat- terns are manipulated to replicate authentic real-world scenarios. The dynamic framework facilitates the comprehensive testing and optimization of customized ranking algorithms, guaranteeing their flexibility in response to changing user preferences and behaviors. The project’s effectiveness is evaluated using precise evaluation metrics that offer quantitative information about how well the system understands and responds to each unique user’s preference, ultimately resulting in a more fulfilling and rich online shopping or content consumption experience.

A Service Recommendation System Based on Dynamic User Groups and Reinforcement Learning

Recently, advancements in machine-learning technology have enabled platforms such as short video applications and e-commerce websites to accurately predict user behavior and cater to their interests. However, the limited nature of user data may compromise the accuracy of these recommendation systems. To address personalized recommendation challenges and adapt to changes in user preferences, reinforcement-learning algorithms have been developed. These algorithms strike a balance between exploring new items and exploiting existing ones, thereby enhancing recommendation accuracy. Nevertheless, the cold-start problem and data sparsity continue to impede the development of these recommendation systems. Hence, we proposed a joint-training algorithm that combined deep reinforcement learning with dynamic user groups. The goal was to capture user preferences for precise recommendations while addressing the challenges of data sparsity and cold-start. We used embedding layers to capture representations and make decisions before the reinforcement-learning process, executing this approach cyclically. Through this method, we dynamically obtained more accurate user and item representations and provide precise recommendations. Additionally, to address data sparsity, we introduced a dynamic user grouping algorithm that collectively enhanced the recommendations using group parameters. We evaluated our model using movie-rating and e-commerce datasets. As compared to other baseline algorithms, our algorithm not only improved recommendation accuracy but also enhanced diversity by uncovering recommendations across more categories.

Enhancing User Engagement through Hybrid Algorithms in Movie Recommendation Systems

The Netflix Movie Recommendation System is a sophisticated algorithmic solution designed to enhance user experience by providing personalized movie suggestions. This system utilizes a combination of collaborative filtering, content-based filtering, and hybrid approaches to predict and recommend movies that users are likely to enjoy based on their viewing history and preferences. Collaborative filtering leverages the collective behavior of users, identifying patterns and similarities in viewing habits to suggest films. Content-based filtering, on the other hand, analyzes the attributes of movies, such as genre, director, and cast, to recommend titles with similar characteristics. The hybrid approach integrates both methods to overcome individual limitations and improve recommendation accuracy. Netflix employs advanced machine learning techniques, including deep learning and matrix factorization, to handle vast amounts of data and continuously refine the recommendation process. The system also incorporates user feedback, ratings, and implicit signals such as viewing time and interaction patterns to dynamically adapt to changing user preferences. This multi-faceted recommendation engine is critical in retaining user engagement, reducing churn, and enhancing the overall viewing experience on the platform. Through constant innovation and optimization, Netflix aims to deliver highly relevant and enjoyable content to its diverse global audience.

How policies and actor strategies affect electric vehicle diffusion and wider sustainability transitions.

Implementing electromobility is a central component in the de-carbonization of personal mobility. In recent years, the absolute number of electric vehicles (EVs) and their market share has increased sharply in many countries. This paper focuses on Norway, a pioneer market for EVs that other countries can learn from. The analysis highlights how a combination of local and national policies over a 30-y period, which targeted both industry development and vehicle demand, were important drivers of this development. It also highlights the importance of advocacy groups and strong networks in promoting EVs, as well as changes in user preferences. The paper demonstrates how the EV diffusion has been driven by alignments of multiple processes across different levels, involving interactions between multiple actors and social groups with different interests and views about desirable futures as described by the multi-level perspective (MLP). Building on the MLP, the study of EV diffusion in Norway illustrates how niches are often sustained through demonstrations, experimentation, strategic alliances, and actors securing favorable political and economic conditions. Further, it shows how local or national niches may depend on international regime actors, such as the car manufacturing industry and policies developed abroad. The paper also explores how the introduction of EVs has opened for wider effects, including innovation within production-consumption systems beyond mobility. Based on this analysis, we argue for a nuanced perspective on the relationship between incremental, regime-internal innovation, and wider transformative changes, where the merits of societal learning and experience with battery electricity for transportation are highlighted.

Movie recommendation system using taymon optimized deep learning network

The increased usage of the internet and social networks generates a large volume of information. Exploring through the large collection is time-consuming and hard to find the required one, so there is a serious need for a recommendation system. Based on this context several movie recommendation (MR) systems have been recently established. In addition, they have poor data analytics capability and cannot handle changing user preferences. As a result, there are many movies listed on the recommendation page, which provides for a poor user experience is the major issue. Therefore, in this work, a novel Taymon Optimized Deep Learning network (TODL net) for recommending top best movies based on their past choices, behaviour and movie contents. The deep neural network is a combination of Dilated CNN with Bi-directional LSTM. The DiCNN-BiLSTM model eliminates the functionality pooling operations and uses a dilated convolution layer to address the issue of information loss. The DiCNN is employed to learn the movie contents by mining user behavioral pattern attributes. The BiLSTM is applied to recommend the best movies on basis of the extracted features of the movie rating sequences of users in other social mediums. Moreover, for providing better results the DiCNN-BiLSTM is optimized with Taymon optimization algorithm to recommend best movies for the users. The proposed TODL net obtains the overall accuracy of 97.24% for best movies recommendation by using TMDB and MovieLens datasets.

A reinforcement learning recommender system using bi-clustering and Markov Decision Process

Collaborative filtering (CF) recommender systems are static in nature and does not adapt well with changing user preferences. User preferences may change after interaction with a system or after buying a product. Conventional CF clustering algorithms only identifies the distribution of patterns and hidden correlations globally. However, the impossibility of discovering local patterns by these algorithms, headed to the popularization of bi-clustering algorithms. Bi-clustering algorithms can analyze all dataset dimensions simultaneously and consequently, discover local patterns that deliver a better understanding of the underlying hidden correlations. In this paper, we modelled the recommendation problem as a sequential decision-making problem using Markov Decision Processes (MDP). To perform state representation for MDP, we first converted user-item votings matrix to a binary matrix. Then we performed bi-clustering on this binary matrix to determine a subset of similar rows and columns. A bi-cluster merging algorithm is designed to merge similar and overlapping bi-clusters. These bi-clusters are then mapped to a squared grid (SG). RL is applied on this SG to determine best policy to give recommendation to users. Start state is determined using Improved Triangle Similarity (ITR similarity measure. Reward function is computed as grid state overlapping in terms of users and items in current and prospective next state. A thorough comparative analysis was conducted, encompassing a diverse array of methodologies, including RL-based, pure Collaborative Filtering (CF), and clustering methods. The results demonstrate that our proposed method outperforms its competitors in terms of precision, recall, and optimal policy learning.

Modeling Long and Short Term User Preferences by Leveraging Multi-Dimensional Auxiliary Information for Next POI Recommendation

Next Point-of-Interest (POI) recommendation has shown great value for both users and providers in location-based services. Existing methods mainly rely on partial information in users’ check-in sequences, and are brittle to users with few interactions. Moreover, they ignore the impact of multi-dimensional auxiliary information such as user check-in frequency, POI category on user preferences modeling and the impact of dynamic changes in user preferences over different time periods on recommendation performance. To address the above limitations, we propose a novel method for next POI recommendation by modeling long and short term user preferences with multi-dimensional auxiliary information. In particular, the proposed model includes a static LSTM module to capture users’ multi-dimensional long term static preferences and a dynamic meta-learning module to capture users’ multi-dimensional dynamic preferences. Furthermore, we incorporate a POI category filter into our model to comprehensively simulate users’ preferences. Experimental results on two real-world datasets demonstrate that our model outperforms the state-of-the-art baseline methods in two commonly used evaluation metrics.

Dynamic recommendation algorithms for a COVID-19 restrictions scenario in the restaurant industry

研究目的利用关于不同国家餐厅及其顾客反馈的数据, 我们探索了以下问题：(i) 在餐饮行业, 用户行为和偏好在COVID-19限制期间如何改变, (ii) 这些变化如何影响推荐算法的性能, 以及 (iii) 可以提出哪些方法来改进封锁情景下的餐厅推荐质量。研究方法为了评估用户行为和偏好的变化, 本研究进行了定量和定性数据分析, 对比了COVID-19限制期前后的情况。为了评估非稳态环境中餐厅推荐系统的性能, 我们测试了最先进的协同过滤算法。我们提出并研究了一种基于过滤的方法, 以提高封锁情景下推荐算法的质量。研究发现研究发现, 在COVID-19限制期间, 平均评分和评论数量发生了变化。实验研究证实：(i) 在COVID-19限制期间, 所有最先进的餐厅行业推荐系统的性能显著下降; (ii) 使用滑动窗口和后过滤方法可以改进非稳态环境下餐厅推荐的准确性和稳定性。实践意义我们提出了两种新方法：基于CatBoost分类模型的关闭餐厅后过滤和滑动窗口方法。这些方法可以应用于经典的协同过滤推荐算法, 并在非稳态条件下提高指标值。这些方法对于推荐系统的开发者和大规模餐厅和酒店聚合平台都有帮助。因此, 这对于应用的最终用户和企业主都有好处, 因为当用户得到良好的推荐时, 他们会诚实地对餐厅进行评价, 而不会因为外部因素降低评分。研究创新本文首次提供了COVID-19限制对不同国家餐厅推荐系统有效性影响的广泛多方面的实验研究, 并提出和验证了两种解决餐厅推荐性能下降问题的新方法。

RSS: Effective and Efficient Training for Sequential Recommendation using Recency Sampling

Many modern sequential recommender systems use deep neural networks, which can effectively estimate the relevance of items, but require a lot of time to train. Slow training increases the costs of training, hinders product development timescales and prevents the model from being regularly updated to adapt to changing user preferences. The training of such sequential models involves appropriately sampling past user interactions to create a realistic training objective. The existing training objectives have limitations. For instance, next item prediction never uses the beginning of the sequence as a learning target, thereby potentially discarding valuable data. On the other hand, the item masking used by the state-of-the-art BERT4Rec recommender model is only weakly related to the goal of the sequential recommendation; therefore, it requires much more time to obtain an effective model. Hence, we propose a novel Recency-based Sampling of Sequences (RSS) training objective (which is parameterized by a choice of recency importance function) that addresses both limitations. We apply our method to various recent and state-of-the-art model architectures – such as GRU4Rec, Caser, and SASRec. We show that the models enhanced with our method can achieve performances exceeding or very close to the effective BERT4Rec, but with much less training time. For example, on the MovieLens-20M dataset, RSS applied to the SASRec model can result in a 60% improvement in NDCG over a vanilla SASRec, and a 16% improvement over a fully-trained BERT4Rec model, despite taking 93% less training time than BERT4Rec. We also experiment with two families of recency importance functions and show that they perform similarly. We further empirically demonstrate that RSS-enhanced SASRec successfully learns to distinguish differences between recent and older interactions – a property that the original SASRec model does not exhibit. Overall, we show that RSS is a viable (and frequently better) alternative to the existing training objectives, which is both effective and efficient for training sequential recommender model when the computational resources for training are limited.

Service selection model based on user intention and context

The internet interconnects billions of objects and services. Real-world services are offered by entities with various functions communicating with one another. The selection of services based on their functionality is a complex process because as the number of services rises, so does the number of services that offer the same functionality. Quality of service (QoS) can be a criterion for selecting a suitable service. However, QoS' relative significance fluctuates due to changing user preferences, and users may exhibit various behaviors depending on their contexts and intentions. Defining a user's preference based on user intentions and context is similarly challenging; nonetheless, scholars have paid little attention to this topic. This study provides a new model for service selection based on user intentions and context. The model dynamically selects the appropriate set of QoS with their importance to specify a user preference for various behaviors. The issue of assessing user preference to select the desired service is resolved by calculating the QoS importance based on the user's behavior history and context. The study proposed a dynamic K-Skyline method to optimize a search space and a multi-criteria decision-making technique to select and rank services efficiently. A case study and an experiment demonstrating the proposed model are presented, in which real-world datasets are utilized. The experimental results of the proposed model validate the model's efficiency and robustness.

Generalized temporal similarity-based nonnegative tensor decomposition for modeling transition matrix of dynamic collaborative filtering

In the real world, user preferences change dynamically. Therefore, time-aware recommendation systems have attracted more attention in both academia and industry. In the literature, tensor decomposition-based models and matrix factorization-based models can handle large-scale sparse data well. However, to the best of our knowledge, there is no work that provides an explanation of the latent time factor embedded in the models. Moreover, conventional Frobenius norm-based models cannot well describe the dynamic changes in user preferences over time. To capture the dynamic changes in user preferences, we interpret the time latent factor vector as a transition matrix of user preferences. In addition, a novel temporal similarity measure is proposed accordingly, which considers dynamic user and item changes between two adjacent time slices. Moreover, we propose a generalized temporal similarity-based nonnegative tensor decomposition (GTS-NTD) model and provide the corresponding solution method. Experiments on three datasets suggest that our proposed method can improve recommendation performance under dynamic changes in user preferences.

Mobility-as-a-Service as a Catalyst for Urban Transport Integration in Conditions of Uncertainty

Sudden events are being observed more often (pandemics, armed conflicts, high inflation, etc.). Urban transport systems are especially susceptible to sudden disruptions as manifested by sharp changes in user preferences and demand for transport services. Mobility as a service (MaaS) aims to improve the efficiency of urban transport systems and support the integration of various forms of transport. The main goal of the research is to assess the level of MaaS use and check what characteristics could influence using MaaS by different groups of users. A case study of the Tri-City, the largest urban centre in northern Poland, was used to examine the possibility of implementing MaaS. We assumed that the use of IT applications would help to reduce the challenges faced by mobility in modern cities. We hypothesised that MaaS is conducive to integrating transport in the city. Knowledge of MaaS acceptance is essential in implementing this concept and can help local authorities respond to sudden disruptions by increasing flexibility in shaping urban mobility. Poland still lacks a universal multimodal solution that would help optimise travel within the cities and actions should be undertaken to popularise digital solutions that improve the efficiency and organisation of PT.