User Preference Data Research Articles

Movie recommendation based on ALS collaborative filtering recommendation algorithm with deep learning model

Development of recommender systems has recently emerged as a prominent study field that has drawn the attention of several scientists and researchers worldwide. Various fields, such as music, movies, books, news, search queries, and commercial goods, employ recommender systems. One of the well-liked and effective RS strategies is the collaborative filtering algorithm, which seeks out users who are quite similar to active one to propose products. This study suggests a unique method for recommending films based on analysis of user preference data that combines ALS collaborative filtering with deep learning techniques. The input in this case is gathered as web data based on previously performed user searches and then processed for noise reduction and normalisation. Convolutional multimodal auto multilayer graph with ALS collaborative filtering (CMAMG_ALSCF) was used to classify this processed data according to user evaluations and interests. Movies that are related to the interests of users are recommended by examining the similarity between users and other users or the similarity between movies and other movies. For several movie recommendation datasets, experimental analysis is done in terms of training accuracy, validation accuracy, RMSE, and average precision.

Laptop Recommender System Using the Hybrid of Ontology-Based and Collaborative Filtering

In the era of ever-evolving information technology, choosing the best laptop can be a complicated task for many users. The increasing complexity of technical specifications is often an obstacle, especially for users who need help understanding them. In addressing this challenge, we propose a solution: a laptop recommendation system that considers users' preferences and functional needs. We designed this system to help users choose a laptop that suits their daily functional needs. This system uses a form of Conversational Recommender System (CRS) by combining Ontology-Based Recommender System Filtering and Collaborative Filtering (CF). Ontology-Based Recommender System Filtering ensures a strong relationship between functional needs and technical specifications of laptops, making it easier for users to identify the right laptop. At the same time, Collaborative Filtering (CF) can provide diversity to the recommended products by using similar user preference data. We evaluate the accuracy of our system by calculating the success rate of recommendation accuracy with the accuracy metric, and the evaluation results show that the success rate of recommendation accuracy reaches 93.33%. Our system is highly effective in assisting users in choosing a laptop that suits their functional needs. With our laptop recommendation system, users can confidently select the correct laptop without being burdened by technical specifications, thus making their lives easier and more productive.

Optimal Marketing Strategy of Private Tutoring: An Application of Game Theory

The pandemic affected private tutorings' decrease in the number of students, which led to a financial crisis for the private tutoring. This research helps analyze and find optimal marketing strategies and their position in competition between private tutorings to increase registrants and income of private tutorings. Game theory is used to analyze marketing strategies using user preference data on the decision to select private tutorings. This study finds the optimal strategy and competitive position for private tutoring X is promotion, and private tutoring Y is a learning module. Recommendations are given to improve the marketing strategy.

User preference optimization for control of ankle exoskeletons using sample efficient active learning.

One challenge to achieving widespread success of augmentative exoskeletons is accurately adjusting the controller to provide cooperative assistance with their wearer. Often, the controller parameters are "tuned" to optimize a physiological or biomechanical objective. However, these approaches are resource intensive, while typically only enabling optimization of a single objective. In reality, the exoskeleton user experience is likely derived from many factors, including comfort, fatigue, and stability, among others. This work introduces an approach to conveniently tune the four parameters of an exoskeleton controller to maximize user preference. Our overarching strategy is to leverage the wearer to internally balance the experiential factors of wearing the system. We used an evolutionary algorithm to recommend potential parameters, which were ranked by a neural network that was pretrained with previously collected user preference data. The controller parameters that had the highest preference ranking were provided to the exoskeleton, and the wearer responded with real-time feedback as a forced-choice comparison. Our approach was able to converge on controller parameters preferred by the wearer with an accuracy of 88% on average when compared with randomly generated parameters. User-preferred settings stabilized in 43 ± 7 queries. This work demonstrates that user preference can be leveraged to tune a partial-assist ankle exoskeleton in real time using a simple, intuitive interface, highlighting the potential for translating lower-limb wearable technologies into our daily lives.

Non-Rating Recommender System for Choosing Tourist Destinations Using Artificial Neural Network

The development of tourist destinations in Batu City makes it hard for the tourists to decide their destinations. The recommender system is a solution that provides a lot of information or tourist attraction data. Collaborative filtering is often used in recommender systems. However, it has drawbacks; one of which is the cold-start problem, where the system cannot recommend items to new users. It was caused by the new user who had no history of rating on any item, or the system had no information. This study aims to apply a non-rated travel destination recommendation system to address the cold-start problem for new users. We use a multi-layer perceptron or artificial neural networks to overcome the problem by training user preference data to produce high training accuracy. Based on four experiments in the training data, the network architecture shows 5 – 7 – 5 – 3 –14, which is the highest accuracy. The architecture uses five variables as inputs and three hidden layers, with each layer was activated using the ReLU activation function. The output layer produces 14 binary outputs and is activated using the sigmoid activation function. The system can give recommendations to new users using feedforward from test data with updated values in weights and biases. The test results from 46 test data showed an accuracy of 67.235%.

Retracted: Game User Preference Data Analysis and Market Guidance Based on Dynamic Attention GRU

Retracted: Game User Preference Data Analysis and Market Guidance Based on Dynamic Attention GRU

The Movie Recommendation System using Content Based Filtering with TF-IDF¬¬-Vectorization and Levenshtein Distance

In this busy life people like to do things to make their mind calm and watching movies is one of the thing but due to large data of a movie exist in the world it is very difficult for the user to select a movie. They have to spend a lot of time in searching and selecting movie. This procedure is time consuming and difficult. So recommendation system make the things easy. Recommendation engines are trained to produce fast and accurate suggestions to users. This paper describes a movie recommendation system using content based filtering and data is processed using Term-frequency Inverse document frequency technique (TF-IDF) for vectorization. Cosine similarity method is used for similarity measure. The system is presented to the user through a web-hosted user-interface which offers a system architecture by considering the initial problem usually faced by recommendation systems, namely the cold start problem. The problem of lack of user preferences data is trying to be overcome by utilizing movies data. The raw data is processed using the TF-IDF algorithm and Vector Space Model to generate a data model. Then levenshtien distance with cosine similarity will improve the performance of existing system. Advantages of the system include efficient recommendations, correct suggestions. Future enhancements include user profiling, documentations and data acquirement through web scraping.

OP-K-Means: Optimized Algorithm for Recommendation System Based on User Preferences

For a recommender system (RS), it is difficult to capture all the user’s interest lists simultaneously, which leads to the problem of insufficient performance of the existing joint RS based on the K-Means clustering algorithm. In this paper (1), we introduce a cluster optimization method OP -K-means for user preference data. This method starts with propagation from the center of the user preference data. By selecting relatively distant positions between each initial center, the distance between them is increased as much as possible. (2) Finally, we validate the effectiveness of our algorithm on a dataset from Facebook and compare our algorithm with original K-means. Our experimental results justify the validity of our OP -K-means algorithm.

Retracted] Application of Mobile Technology in College English Vocabulary Teaching

In order to improve the quality of college English vocabulary teaching, this paper studies the application of mobile technology in college English vocabulary teaching. The advantages of mobile technology are analyzed, and different mobile technology communication deployment schemes are determined according to different needs and environments of English vocabulary learning, so as to build a college English vocabulary teaching system, which is mainly composed of the user layer, mobile network layer, and English vocabulary data layer. Among them, the main functions of the user layer are user login, system information setting, English vocabulary resource browsing, uploading English vocabulary homework, etc., and determine user preference data through user logs. The mobile network layer is designed using mobile technology to create the delay control technique of the system mobile communication, and the system mobile network layer’s LAN is set up to increase data transmission speed. Finally, set up the database according to the English vocabulary of English words field, calculating Euclidean distance to determine the relationship between them, and then through random set classification vocabulary data types, calculate the similarity degree of the data extraction vocabulary data characteristics and remove high similarity of the English vocabulary data to reduce the difficulty of system storage. The experimental findings suggest that the mobile technology‐based system has high performance and dependability and may be widely used in practice.

Game User Preference Data Analysis and Market Guidance Based on Dynamic Attention GRU

With the development of Internet technology and social model, game products have become an important product of people’s life for entertainment and recreation, and the precise marketing of game products has become a winning means for enterprises to improve competitiveness and reduce labor cost consumption, and major game companies are also paying more and more attention to the data-based marketing model. How to dig out the effective information from the existing market behavior data is a powerful means to implement precise marketing. Achieving precise positioning and marketing of gaming market is the guarantee of innovative development of game companies. For the research on the above problem, based on the SEMAS process of data mining, this paper proposes a mining model based on recurrent neural network, which is named as Dynamic Attention GRU (DAGRU) with multiple dynamic attention mechanisms, and evaluates it on two self-built data sets of user behavior samples. The results demonstrate that the mining method can effectively analyze and predict the player behavior goals. The game marketing system based on data mining can indeed provide more accurate and automated marketing services, which greatly reduces the cost investment under the traditional marketing model and achieves accurate targeting marketing services and has certain application value.

A Technique of Recursive Reliability-Based Missing Data Imputation for Collaborative Filtering

Collaborative filtering (CF) is a recommendation technique that analyzes the behavior of various users and recommends the items preferred by users with similar preferences. However, CF methods suffer from poor recommendation accuracy when the user preference data used in the recommendation process is sparse. Data imputation can alleviate the data sparsity problem by substituting a virtual part of the missing user preferences. In this paper, we propose a k-recursive reliability-based imputation (k-RRI) that first selects data with high reliability and then recursively imputes data with additional selection while gradually lowering the reliability criterion. We also propose a new similarity measure that weights common interests and indifferences between users and items. The proposed method can overcome disregarding the importance of missing data and resolve the problem of poor data imputation of existing methods. The experimental results demonstrate that the proposed approach significantly improves recommendation accuracy compared to those resulting from the state-of-the-art methods while demanding less computational complexity.

Personalized Recommendation Algorithm for Mobile Based on Federated Matrix Factorization

There is a problem that the amount of users’ preference data on the mobile is small, and users are unwilling to disclose the preference data for the recommendation system about mobile users, so the server can’t centrally train a large amount of users’ preference data for a personalized recommendation. This paper proposes a personalized federated matrix factorization algorithm by introducing a federated matrix factorization model. The algorithm introduces users’ and items’ biases to modify the predictive rating model on each mobile; At the same time, conformity is introduced to give different weights to the preference data. In the case that the preference data does not leave the mobile, but the user preference data of the multiparty mobiles is shared, the multi-party mobiles and the server jointly train personalized matrix factorization model. The experimental results show that the algorithm in this paper still has high recommendation accuracy under the premise of correct updating in the federated matrix factorization model that uses bias and conformity.

Female-Type Android\u2019s Drive to Quickly Understand a User\u2019s Concept of Preferences Stimulates Dialogue Satisfaction: Dialogue Strategies for Modeling User\u2019s Concept of Preferences

This research develops a conversational robot that stimulates users’ dialogue satisfaction and motivation in non-task-oriented dialogues that include opinion and/or preference exchanges. One way to improve user satisfaction and motivation is by demonstrating the robot’s ability to understand user opinions. In this paper, we explore a method that efficiently obtains the concept of user preferences: likes and dislikes. The concept is acquired by complementing a small amount of user preference data observed in dialogues. As a method for efficient collection, we propose a dialogue strategy that creates utterances with the largest expected complementation. Our experimental results with a female-type android robot suggest that the proposed strategy efficiently obtained user preferences and enhanced dialogue satisfaction. In addition, the strength of user motivation (i.e., long-term willingness to communicate with the android) is only positively correlated with the android’s willingness to understand. Our results not only show the effectiveness of our proposed strategy but also suggest a design theory for dialogue robots to stimulate dialogue motivation, although the current results are derived only from a female-type android.

Red-Green Tactical Lighting Is Preferred for Suturing Wounds in a Simulated Night Environment.

Delivering medical care in nighttime conditions is challenging, as 25% of Special Operations medical Operators have reported that problems with lighting contributed to poor casualty outcomes. Red light is often used in nighttime operations but makes blood detection difficult and diminishes depth perception and visual acuity. Red-green combination lighting may be superior for differentiating blood from tissue and other fluids but had not been tested versus red-only or green-only lighting for combat-related medical procedures, such as wound suturing. Dark-adapted medical resident physicians (N = 24) sutured 6cm long, 3cm deep, full-thickness lacerations in deceased swine under red-only, green-only, and red-green lighting provided by a tactical flashlight using a randomized within-subjects design. Time to suture completion, suture quality, user ratings, and user preference data were contrasted at p < .05. This study was approved by Naval Medical Center Portsmouth IRB. Suture completion time and suture quality were similar across all lighting conditions. Participants rated red-green lighting as significantly easier for identifying blood, identifying instruments, and performing suturing (p < .01). Red-green lighting was preferred by 83% of participants compared to 8% each for red-only and green-only (p < .001). Pending further study under tactical conditions, red-green lighting is tentatively recommended for treating battlefield wounds in low-light environments.

IASSIST

The iASSIST is an iPhone-based assistive sensor solution for independent and safe travel for people who are blind or visually impaired, or those who simply face challenges in navigating an unfamiliar indoor environment. The solution integrates information of Bluetooth beacons, data connectivity, visual models, and user preferences. Hybrid models of interiors are created in a modeling stage with these multimodal data, collected, and mapped to the floor plan as the modeler walks through the building. Client-server architecture allows scaling to large areas by lazy-loading models according to beacon signals and/or adjacent region proximity. During the navigation stage, a user with the navigation app is localized within the floor plan, using visual, connectivity, and user preference data, along an optimal route to their destination. User interfaces for both modeling and navigation use multimedia channels, including visual, audio, and haptic feedback for targeted users. The design of human subject test experiments is also described, in addition to some preliminary experimental results.

Content-based recommender system architecture for similar e-commerce products

Recommendation systems are quite famous and are increasingly being used on e-commerce platforms for a variety of purposes. The recommendation system technique used also varies greatly depending on the scope and Item of recommendation. Content-based filtering, for example, is used to recommend related product items based on user preferences. However, how the recommendation system architecture should be built starts by creating a data model for bringing up related product items. This paper offers a system architecture by considering the initial problem usually faced by recommendation systems, namely the cold start problem. The problem of lack of user preferences data is trying to be overcome by utilizing product item documents. Product item documents are processed using the TF-IDF algorithm and Vector Space Model to generate a data model. Then a query can be applied to find similarities to items that the user has seen. In the end, the recommendation system architecture that was built produced excellent Precision using Recall and Precision testing. Tests are carried out for data using the weighting of product names and product labels. The result obtained 0.84 for the average value of Recall and 0.78 for the average value of Precision.

Inferring linear feasible regions using inverse optimization

Consider a problem where a set of feasible observations are provided by an expert, and a cost function exists that characterizes which of the observations dominate the others and are hence, preferred. Assume the expert has an implicit optimization model in mind to identify the feasible observations, but the explicit constraints of this underlying model are unknown. Our goal is to infer the feasible region of such an optimization model that would render these observations feasible while making the best ones optimal for the cost (objective) function. Such feasible regions (i) build a baseline for a systematic categorization of future observations, and (ii) allow for using sensitivity analysis to discern changes in optimal solutions if the objective function changes in the future.In this paper, we propose a general inverse optimization methodology that recovers the complete constraint matrix of a linear model and then introduce a tractable equivalent reformulation. Furthermore, we provide and discuss several generalized loss functions to inform the desirable properties of the feasible region based on user preference and historical data. We demonstrate our approach using numerical examples and a realistic diet recommendation case study for imputing personalized diets. Our numerical examples verify the validity of our approach and emphasize the differences among the proposed loss functions. The diet recommendation case study shows that the proposed models can improve the palatability of the recommended diets for each user, and it provides further intuition for large-scale implementations of the proposed methodology.

Multi-Task Travel Route Planning With a Flexible Deep Learning Framework

Travel route planning aims to map out a feasible sightseeing itinerary for a traveler covering famous attractions and meeting the tourist's desire. It is very useful for tourists to plan their travel routes when they want to travel at unfamiliar scenic cities. Existing methods for travel route planning mainly concentrate on a single planning problem for a special task, but is not capable of being applied to other tasks. For example, previous must-visit planning methods cannot be applied to the next-point recommendation, despite these two tasks are closely related to each other in travel route planning. Besides, most of the existing work do not consider the important auxiliary information such as Point of Interests (POI) attributes, user preference, and historical route data in their approaches. In this paper, we propose a flexible Multi-task Deep Travel Route Planning framework named MDTRP to integrate rich auxiliary information for more effective planning. Specifically, we first construct a heterogeneous network through the relations between users and POIs and employ a heterogeneous network embedding method to learn the features of users and POIs. Then we present an attention-based deep model to integrate the auxiliary information and focus on important visited points for the prediction of next POIs. Finally, a beam search algorithm is introduced to flexibly generate multiple feasible route candidates for three types of planning tasks (next-point recommendation, general route planning, and must-visit planning). We introduce six public datasets to conduct extensive experiments, of which the results demonstrate the flexibility and superiority of the proposed approach in travel route planning.

Exploring innovation diffusion of two-sided mobile payment platforms: A system dynamics approach

Mobile payment (m-payment) can be considered as a two-sided platform that delivers payment services for mobile devices. Its penetration still faces high barriers in some countries, such as existing consumer payment behavior on traditional payment methods and fragmented merchant acceptance. This research aims to develop an empirical data-driven simulation methodology that integrates system dynamics (SD) and choice-based conjoint (CBC) analysis for analyzing diffusion dynamics of several competing m-payment platforms. Choice-based conjoint analysis is used to collect the multi-attribute preference data on different platform features from each side of user groups. A SD simulation model with the empirical user preference data is developed to evaluate how platform design strategies affect diffusion dynamics of various competing platforms under different scenarios. The methodology is applied to Taiwan's m-payment market. The research findings can help firms understand potential evolution of the m-payment market and formulate appropriate platform design strategies proactively to improve competitive advantages.

Knowledge transfer by domain-independent user latent factor for cross-domain recommender systems

Collaborative filtering is one of the most promising used techniques in recommender systems to provide personalized recommendations. However, it suffers from the data sparsity problem due to insufficient user preference data. The promising solution to this problem is knowledge exploitation from other related source domains through transfer learning mechanism. Although several works have been proposed to enhance prediction performance of the target domain by leveraging the knowledge of source domains, extracting domain-independent knowledge from source domains and preventing the negative knowledge both are challenging and open problems for cross-domain recommender systems framework.In this paper, we propose a method namely Knowledge Transfer by Domain-independent User Latent Factor for Cross-domain Recommender Systems. The propose method uses matrix tr-factorization by which a rating matrix decomposes into three sub-matrices. Advantage of tri-factorization is to be able to prevent domain effect knowledge transfer. This allows to enhance the overall performance of prediction in the target domain. We collectively factorize both domain rating matrices by matrix tri-factorization with the constraint of shared user latent factor across domains. The constraint holds to be true due to users fully overlap across domains. The loss functions of matrix tri-factorization are combined with a tuning parameter that is able to prevent negative transferring of a source domain. The combined loss function is optimized by stochastic gradient descent algorithm. The superiority of our proposed method over existing state-of-the-art methods is demonstrated by experimental results which have been done on real-world Amazon co-purchasing metadata.