Changes In User Preferences Research Articles

A Personalized Navigation Route Recommendation Strategy Based on Differential Perceptron Tracking User's Driving Preference.

With the increasing frequency of autonomous driving, more and more attention is paid to personalized path planning. However, the path selection preferences of users will change with internal or external factors. Therefore, this paper proposes a personalized path recommendation strategy that can track and study user's path preference. First, we collect the data of the system, establish the relationship with the user preference factor, and get the user's initial preference weight vector by dichotomizing the K-means algorithm. The system then determines whether user preferences change based on a set threshold, and when the user's preference changes, the current preference weight vector can be obtained by redefining the preference factor or calling difference perception. Finally, the road network is quantized separately according to the user preference weight vector, and the optimal path is obtained by using Tabu search algorithm. The simulation results of two scenarios show that the proposed strategy can meet the requirements of autopilot even when user preferences change.

Enhancing Ecommerce Recommenders with Dual Transformer Models

The growing complexity and scale of ecommerce platforms have created a need for more advanced and precise recommendation systems. Traditional recommendation approaches, such as collaborative filtering and content-based models, while effective, often struggle to fully capture the evolving preferences and behaviours of users in dynamic online marketplaces. To address these limitations, this paper explores the application of dual transformer models in enhancing ecommerce recommender systems. Transformer models, originally developed for natural language processing, offer powerful attention mechanisms that allow for the processing of sequential and contextual data, providing a more nuanced understanding of user behaviour. This study introduces dual transformer models, which simultaneously process both user interaction history and item features to improve recommendation accuracy. By capturing the intricate relationships between users and items from both dimensions, dual transformer models offer a bidirectional approach that can adapt to changing user preferences in real time. This enables ecommerce platforms to deliver more relevant, personalized, and context-aware product recommendations. The use of dual transformers in recommendation systems presents several benefits, including improved scalability, enhanced personalization, and increased user engagement. By addressing the shortcomings of traditional recommender systems, dual transformer models have the potential to revolutionize the ecommerce industry, leading to better user satisfaction and higher conversion rates. This paper highlights the potential of these models in improving the overall effectiveness of ecommerce recommendation engines, making them a valuable tool for modern online shopping platforms.

Deep learning recommendation algorithm based on semantic mining.

This paper proposes Deep Semantic Mining based Recommendation (DSMR), which can extract user features and item attribute features more accurately by deeply mining the semantic information of review text and item description documents recommend. First, the proposed model uses the BERT pre-training model to process review texts and item description documents, and deeply mine user characteristics and item attributes, which effectively alleviates the problems of data sparseness and item cold start; Then, the forward LSTM is used to pay attention to the changes of user preferences over time, and a more accurate recommendation is obtained; finally, in the model training stage, the experimental data are randomly divided into 1 to 5 points, 1:1:1:1:1. Extraction ensures that the amount of data for each score is equal, so that the results are more accurate and the model is more robust. Experiments are carried out on four commonly used Amazon public data sets, and the results show that with the root mean square error as the evaluation index, the error of DSMR recommendation results is at least 11.95% lower on average than the two classic recommendation models based only on rating data. At the same time, it is better than the three latest recommendation models based on review text, and it is 5.1% lower than the best model on average.

Incorporating Preference Changes through Users’ Input in Collaborative Filtering Movie Recommender System

The usefulness of Collaborative filtering recommender system is affected by its ability to capture users' preference changes on the recommended items during recommendation process. This makes it easy for the system to satisfy users' interest over time providing good and quality recommendations. The Existing system studied fails to solicit for user inputs on the recommended items and it is also unable to incorporate users' preference changes with time which lead to poor quality recommendations. In this work, an Enhanced Movie Recommender system that recommends movies to users is presented to improve the quality of recommendations. The system solicits for users' inputs to create a user profiles. It then incorporates a set of new features (such as age and genre) to be able to predict user's preference changes with time. This enabled it to recommend movies to the users based on users new preferences. The experimental study conducted on Netflix and Movielens datasets demonstrated that, compared to the existing work, the proposed work improved the recommendation results to the users based on the values of Precision and RMSE obtained in this study which in turn returns good recommendations to the users.

An Assistant Teaching System of English Creative Writing Based on Semantic Mining

To enhance students’ English writing ability, English creative writing has become a common course offered by colleges and universities. English reference is an indispensable tool for creative English writing. How to choose appropriate English references is an important guarantee to complete good work. Therefore, this paper proposes a Deep Semantic Mining-based Recommendation (DSMR) algorithm for English writing reference selection and recommendation to assist the completion of high-quality English creative writing works. The model can extract user features and document attributes more accurately by deeply mining semantic information of literature content and user needs, so as to achieve more accurate recommendations. First, the Bidirectional Encoder Representation from Transformers (BERT) pretraining model is adopted to process literature content and user requirement documents. Through in-depth mining of user characteristics and literature attributes, the problems of data sparsity and cold start of items are effectively alleviated. Then, the forward long short-term memory (LSTM) network was used to focus on the changes in user preferences over time, resulting in more accurate recommendations. The experimental results show that the use of heterogeneous information can significantly improve the recommendation performance, and the additional use of user attribute information can also improve the recommendation performance. Compared with other benchmark models, the recommendation quality of this model is greatly improved.

Modeling Information Diffusion With Sequential Interactive Hypergraphs

In online social networks, numerous users generate and distribute tremendous content simultaneously, and how information spreads in online society has attracted critical attention. Recent studies introduce deep learning to help mine the complex diffusion pattern and forecast the diffusion trend. However, there are two limitations of the state-of-art methods. On the one hand, existing models only focus on the internal influence within the diffusion flow but ignore the external influence from the dissemination of other contents. On the other hand, the dynamics of user interest are barely considered, while the changes of user preference for contents also have a considerable impact on future diffusion. To address these issues, we introduce the hypergraph structure and a sequential framework to model complex interactions in social networks. Then, we propose dual-channel hypergraph neural networks to tackle the diffusion prediction problem, denoted by HyperINF. Specifically, in the user channel, we build sequential user interactive hypergraphs and learn the dynamic user representation, and in the diffusion channel, we construct a diffusion interactive graph to capture the cross-diffusion relation. At last, we consider the social relation to help make the prediction. Experimental results on three datasets suggest the effectiveness and practicability of the proposed framework.

DTCRSKG: A Deep Travel Conversational Recommender System Incorporating Knowledge Graph

In the era of information explosion, it is difficult for people to obtain their desired information effectively. In tourism, a travel recommender system based on big travel data has been developing rapidly over the last decade. However, most work focuses on click logs, visit history, or ratings, and dynamic prediction is absent. As a result, there are significant gaps in both dataset and recommender models. To address these gaps, in the first step of this study, we constructed two human-annotated datasets for the travel conversational recommender system. We provided two linked data sets, namely, interaction sequence and dialogue data sets. The usage of the former data set was done to fully explore the static preference characteristics of users based on it, while the latter identified the dynamics changes in user preference from it. Then, we proposed and evaluated BERT-based baseline models for the travel conversational recommender system and compared them with several representative non-conversational and conversational recommender system models. Extensive experiments demonstrated the effectiveness and robustness of our approach regarding conversational recommendation tasks. Our work can extend the scope of the travel conversational recommender system and our annotated data can also facilitate related research.

Simulation-based personalized real-time control of adaptive facades in shared office spaces

High-performance facade is a crucial component in buildings that are characterized by extensive large transparent facades especially in shared office environments to control both energy demand and human comfort. Adaptive façades (AFs) offer a multifunctional potential that can react to short-term changes of environment or user preferences. However, there is no methodological framework for controlling such façades for a shared office space where extra conflicts because of different visual discomfort experiences might arise among users when they want to interact with façade due to social constraints such as hesitation or complex ownership. This research paper introduces a novel simulation-based framework that allows personalized real-time control over façade sections or modules. The methodology utilizes parametric simulation tools and offline control programming using Python language within an integrated workflow. Allowing the users to send their visual discomfort requests to the back-end control algorithm could improve both their visual comfort performance and building thermal energy demand by 61% and 29%, respectively, compared to two typical automatic shading controls. Limitations of the current methodology and potential recommendations for future studies are also discussed in detail.

A power-efficient self-calibrating smart lighting system

Lighting load accounts for a significant portion of overall energy consumption in office buildings. To reduce this load, we have designed and built a smart self-calibrating lighting control system that minimizes power consumption that automatically responds to changes in daylight and occupancy, while simultaneously providing personalized lighting comfort to each occupant. The system measures illuminance and occupancy from sensors located at each work station. Using an unobtrusive self-calibration process, it estimates the relationship between the dimming level of each bulb and the illuminance at each work station. Subsequently, an adaptive control algorithm maintains the desired illuminance at work surfaces despite environmental fluctuations by periodically recalculating the power-efficient and comfort-preserving dimming level for each bulb. Based on a realistic deployment of our system, we find that our system quickly responds to changes in occupancy, daylight and user preferences. We also show, through extensive simulations using 7 months of collected daylight and occupancy data, that our system reduces energy consumption by about 40% compared to conventional LED lighting systems.

Trust Recommendation Based on Deep Deterministic Strategy Gradient Algorithm

Trust recommendation is a vital recommendation system application based on social networks. It can recommend items based on the trust between users, which can alleviate data sparseness and enhance the interpretability of results. A large number of recommendation algorithms have been proposed, but most of them believe that trust is fixed, ignoring the changes of trust in the process of interaction. In addition, deep learning models are good at solving complex tasks and processing high-dimensional data, and they can model recommendation algorithms, but they are insufficient in capturing changes in user preferences timely. Therefore, given the shortcomings of existing researches, we propose a DDPG-TR algorithm based on deep reinforcement learning to capture the changes in user preferences and update the trust between users. The algorithm uses deep deterministic policy gradient algorithm DDPG to model the user-item interaction process. Firstly, we improve a state representative structure to express the user’s state, which is convenient to capture the changes in user preferences. Then, when the user accepts the recommendation, algorithm combines trust and similar information to predict item score, as well as calculates the difference of the score. Finally, Agent gets the score feedback and uses the difference to update trust. Experiments are conducted on three datasets, and they are verified that the DDPG-TR algorithm can provide more accurate recommendation results, compared with other recommendation algorithms.

An Improved Sequential Recommendation Algorithm based on Short‐Sequence Enhancement and Temporal Self‐Attention Mechanism

Sequential recommendation algorithm can predict the next action of a user by modeling the user’s interaction sequence with an item. However, most sequential recommendation models only consider the absolute positions of items in the sequence, ignoring the time interval information between items, and cannot effectively mine user preference changes. In addition, existing models perform poorly on sparse data sets, which make a poor prediction effect for short sequences. To address the above problems, an improved sequential recommendation algorithm based on short‐sequence enhancement and temporal self‐attention mechanism is proposed in this paper. In the proposed algorithm, a backward prediction model is trained first, to predict the prior items in the user sequence. Then, the reverse prediction model is used to generate a batch of pseudo‐historical items before the initial items of the short sequence, to achieve the goal of enhancing the short sequence. Finally, the absolute position information and time interval information of the user sequence are modeled, and a time‐aware self‐attention model is adopted to predict the user’s next action and generate a recommendation list. Various experiments are conducted on two public data sets. The experimental results show that the method proposed in this paper has excellent performance on both dense and sparse data sets, and its effect is better than that of the state of the art.

Enhancement of online education system by using a multi-agent approach

Multi-Agent System (MAS) is popular in the fields where cooperative effort is required to fulfil the purpose of the end product. This study creates a Multi-Agent System as a potential solution for the implementation of an online education system in order to bring about swift and accurate responses from the system and thus a meaningful conversation between the learners and the system. The existing work on online education system employing MAS does not depict the means of flow of information from the browser to the internal MAS and also lacks the adaptivity element with respect to the changing demands of the learners. The study attempts to bridge this gap by employing a system with Event-Condition-Action model and intelligent agents which include a message passing agent to depict the means of message flow from the browser to the MAS and an adaptive course organiser agent which organises adaptive educational content with respect to the changing needs of the learner. The outcome of the study is the design and simulation of the said system as a standalone entity that can be attached to a virtual learning environment (VLE), with the addition of pedagogical agents performing different functionalities to form an adaptive system. The system is evaluated by validating the results generated for various case studies using a validation tool. The expected behaviour is that the resulting course agenda agrees with the learning mode preference of the user provided initially and the results are expected to change according to the changing user preferences through a self-assessment questionnaire. • A Multi-Agent system (MAS) is created as a potential solution for the implementation of an online education system. • Solutions for virtual learning concerns are presented. • System is standalone and can be attached to an existing virtual learning environment (VLE) to make the resulting system more effective in providing appropriate educational resources. • Resulting course agenda agrees with the learning style preference of the user. • Results change according to the changing user preferences through a self-assessment questionnaire.

Formal Specification of Non-Functional Properties of Context-Aware Systems

During the recent years, there is an increasing demand for software systems that dynamically adapt their behavior at run-time in response to changes in user preferences, execution environment, and system requirements, being thus context-aware. Authors are referring here to requirements related to both functional and non-functional aspects of system behavior since changes can also be induced by failures or unavailability of parts of the software system itself. To ensure the coherence and correctness of the proposed model, all relevant properties of system entities are precisely and formally described. This is especially true for non-functional properties, such as performance, availability, and security. This article discusses semantic concepts for the specification of non-functional requirements, taking into account the specific needs of a context-aware system. Based on these semantic concepts, we present a specification language that integrates non-functional requirements design and validation in the development process of context-aware self-adaptive systems.

POI recommendation method using LSTM-attention in LBSN considering privacy protection

Aiming at the problems of traditional point of interest (POI), such as sparse data, lack of negative feedback, and dynamic and periodic changes of user preferences, a POI recommendation method using deep learning in location-based social networks (LBSN) considering privacy protection is proposed. First, the idea of Embedding is used to quantify the user information, friend relationship, POI information, and so on, so as to obtain the internal relationship of the location. Then, based on the user's history and current POI check-in sequence set, the long- and short-term attention mechanism (LSA) is constructed, and the quantified information is used as the input of LSA to better capture the user's long-term and short-term preferences. Finally, the social network information and semantic information are fitted in different input layers, and the time and geographical location information of user's historical behavior are used to recommend the next POI for users. Gowalla and Brightkite datasets are used to demonstrate the proposed method. The results show that the performance of the proposed method is better than other comparison methods under different sparsity, location sequence length, and embedding length. When the number of iterations is 500, the recommended method tends to be stable, and the accuracy is 0.27. Moreover, the recommendation time of the proposed method is less than 130 ms, which is better than other comparative deep learning methods.

CONSTRUCTION OF PATTERNS OF USER PREFERENCES DYNAMICS FOR EXPLANATIONS IN THE RECOMMENDER SYSTEM

The subject of study in the article is the processes of constructing explanations in recommendation systems. Objectives. The goal is to develop a method of constructing patterns that reflect the dynamics of user preferences and provide an opportunity to form an explanation of the recommended list of items, taking into account changes in the user’s requirements of the recommendation system. Construction of explanations taking into account the dynamics of changes in consumer preferences makes it possible to increase user confidence in the results of the intelligent system. Tasks: structuring models of temporal patterns of parallel-alternative and sequential-alternative users’ choice of the recommendation system; development of a method for constructing patterns of changing user preferences using process mining technology; experimental verification of the method for constructing patterns of changing consumer preferences. The approaches used are: temporal logics, which determine the approaches to the description of the temporal ordering of a set of events. The following results are obtained. The structuring of models of temporal patterns of parallel-alternative and sequential-alternative users’ choice of the recommendation system is performed; developed and performed an experimental test of the method of constructing patterns of user preferences dynamics. Conclusions. The scientific novelty of the results is as follows. The method of dynamics patterns construction of users’ preferences for the formation of explanations concerning the recommended list of subjects is offered. The method sequentially generates a set of ordered events, each of which reflects the choice of the subject by a group of users at a certain time interval, and also builds a graph representation of the patterns of user preferences through intellectual analysis of processes. The patterns obtained as a result of the method consist of time-ordered pairs of events that reflect the knowledge of changing user preferences over time. Further use of such dependencies as elements of the knowledge base makes it possible based on probabilistic inference to build a set of alternative explanations for the received recommendation, and then arrange these explanations according to the probability of their implementation for the recommended list of subjects.

Text Refinement Powered by Artificial Intelligence for Tourism

Tourism is one of the leading industries across the globe. Tourists need to get information about the places to visit in their preferred destination. However, it is quite tedious to get individual information about these places. Thus, the proposed work aims in developing a system that analyses users likes and the preferred time period of the user to visit a particular location in the preferred place to form a trained data set and furnishes the results which showcase the path of a user can take to explore a specific city based on the trained data set. The proposed system adopts a haversine algorithm and traveling salesman problem in conjunction with a text refinement framework for extracting the information on recreational sites from media contents, social sites, and other web contents to form a rich corpus of data based on which the user is suggested appropriate places to visit. It is inferred from the experiment that the proposed approach has better time efficiency with changing user preferences, where the observed time is 1.33 ms with 16 user preferences.

Development of a multicriteria decision-making model for selecting optimal aviation fatigue measurement methods

Human fatigue has been proved to directly cause or contribute to a significant share of aviation accidents. Although fatigue measurement is a major input to any solution that targets reduction of fatigue adverse influences on aviation operations, there is a critical lack of evidence on what optimum sets of fatigue measurement strategies could be applied given the vast organizational and operational differences between various industry players. The current study utilizes the published aviation safety literature, the technology acceptance model and expert opinion to propose a new multicriteria decision model, the aviation fatigue measurement selection model, to decide on what fatigue measurement methods are most suitable for each aviation operator. The model addresses the validity, user acceptance, cost and other limitations of each currently acknowledged fatigue measurement method. The model was then applied through comprehensive scenario-based simulations and was found to be sensitive to changes in user preferences and valid.

A Time-Aware Hybrid Approach for Intelligent Recommendation Systems for Individual and Group Users

Although personal and group recommendation systems have been quickly developed recently, challenges and limitations still exist. In particular, users constantly explore new items and change their preferences throughout time, which causes difficulties in building accurate user profiles and providing precise recommendation outcomes. In this context, this study addresses the time awareness of the user preferences and proposes a hybrid recommendation approach for both individual and group recommendations to better meet the user preference changes and thus improve the recommendation performance. The experimental results show that the proposed approach outperforms several baseline algorithms in terms of precision, recall, novelty, and diversity, in both personal and group recommendations. Moreover, it is clear that the recommendation performance can be largely improved by capturing the user preference changes in the study. These findings are beneficial for increasing the understanding of the user dynamic preference changes in building more precise user profiles and expanding the knowledge of developing more effective and efficient recommendation systems.

Modeling user preference dynamics with coupled tensor factorization for social media recommendation

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

Stratified and time-aware sampling based adaptive ensemble learning for streaming recommendations

Recommender systems have played an increasingly important role in providing users with tailored suggestions based on their preferences. However, the conventional offline recommender systems cannot handle the ubiquitous data stream well. To address this issue, Streaming Recommender Systems (SRSs) have emerged in recent years, which incrementally train recommendation models on newly received data for effective real-time recommendations. Focusing on new data only benefits addressing concept drift, i.e., the changing user preferences towards items. However, it impedes capturing long-term user preferences. In addition, the commonly existing underload and overload problems should be well tackled for higher accuracy of streaming recommendations. To address these problems, we propose a S tratified and T ime-aware S ampling based A daptive E nsemble L earning framework, called STS-AEL, to improve the accuracy of streaming recommendations. In STS-AEL, we first devise stratified and time-aware sampling to extract representative data from both new data and historical data to address concept drift while capturing long-term user preferences. Also, incorporating the historical data benefits utilizing the idle resources in the underload scenario more effectively. After that, we propose adaptive ensemble learning to efficiently process the overloaded data in parallel with multiple individual recommendation models, and then effectively fuse the results of these models with a sequential adaptive mechanism. Extensive experiments conducted on three real-world datasets demonstrate that STS-AEL, in all the cases, significantly outperforms the state-of-the-art SRSs.