An Extended Collaborative Filtering-based Recommendation Procedure for Multimedia Contents in M-Commerce

As mobile market grows more and more fast, the mobile contents market, especially music contents for mobile phones have record remarkable growth. In spite of this rapid growth, mobile web users experience high levels of frustration to search the desired music. And new musics are very profitable to the content providers, but the existing CF system can’t recommend them. To solve this problem, we propose an extended CF system to reflect the user’s real preference by representing users in the feature space. We represent the musics using the music’s content based acoustic feature like timbral, MFCCs, rhythmic, and pitch contents in multi-dimensional feature space, and then select a neighborhood with distance based function. And for new music recommendation, we match the new music with other users’ preference. To verify the performance of the proposed system, the simulation imitating the real user’s decision-making and context in conducted. Through comparison with the pure CF, we validate our system’s performanc

Hypoxia alters the transcriptomic and epigenetic landscape of TNF-?? response in human macrophages

Department of Biological Sciencesclos

A Deep Learning-Based Course Recommender System for Sustainable Development in Education

Recently, the worldwide COVID-19 pandemic has led to an increasing demand for online education platforms. However, it is challenging to correctly choose course content from among many online education resources due to the differences in users’ knowledge structures. Therefore, a course recommender system has the essential role of improving the learning efficiency of users. At present, many online education platforms have built diverse recommender systems that utilize traditional data mining methods, such as Collaborative Filtering (CF). Despite the development and contributions of many recommender systems based on CF, diverse deep learning models for personalized recommendation are being studied because of problems such as sparsity and scalability. Therefore, to solve traditional recommendation problems, this study proposes a novel deep learning-based course recommender system (DECOR), which elaborately captures high-level user behaviors and course attribute features. The DECOR model can reduce information overload, solve high-dimensional data sparsity problems, and achieve high feature information extraction performance. We perform several experiments utilizing real-world datasets to evaluate the DECOR model’s performance compared with that of traditional recommendation approaches. The experimental results indicate that the DECOR model offers better and more robust recommendation performance than the traditional methods

A Review Helpfulness Modeling Mechanism for Online E-commerce: Multi-Channel CNN End‑to‑End Approach

Review helpfulness prediction aims to provide helpful reviews for customers to make purchase decisions. Although many studies have proposed prediction mechanisms, few have introduced consistency between the review text and star rating information in the review helpfulness prediction task. Moreover, previous studies that have reflected such a consistency still have limitations, including the star rating facing information loss, and the interaction between review text and star rating not extracted effectively. This study proposes the CNN-TRI model to overcome these limitations. Specifically, this study applies a multi-channel CNN model to extract semantic features in the review text and convert star ratings into a high-dimensional feature vector to avoid information loss. Next, element-wise operation and multilayer perception are applied to extract linear and nonlinear interactions to learn interaction effectively. Results measured by real world online reviews collected from Amazon.com show that CNN-TRI significantly outperforms the state-of-the-art. This study helps e-commerce websites with marketing efforts to attract more customers by providing more helpful reviews and thus, increasing sales. Moreover, this study can enhance customers’ attitudes and purchase decision-making by reducing information overload and customers’ search costs

Elasto-Inertial Particle Focusing in Microchannel with T-Shaped Cross-Section

Recently, particle manipulation in non-Newtonian fluids has attracted increasing attention because of a good particle focusing toward the mid-plane of a channel. In this research, we proposed a simple and robust fabrication method to make a microchannel with various T-shaped cross-sections for particle focusing and separation in a viscoelastic solution. SU-8-based soft lithography was used to form three different types of microchannels with T-shaped cross-sections, which enabled self-alignment and plasma bonding between two PDMS molds. The effects of the flow rate and geometric shape of the cross-sections on particle focusing were evaluated in straight microchannels with T-shaped cross-sections. Moreover, by taking images from the top and side part of the channels, it was possible to confirm the position of the particles three-dimensionally. The effects of the corner angle of the channel and the aspect ratio of the height to width of the T shape on the elasto-inertial focusing phenomenon were evaluated and compared with each other using numerical simulation. Simulation results for the particle focusing agreed well with the experimental results both in qualitatively and quantitatively. Furthermore, the numerical study showed a potential implication for particle separation depending on its size when the aspect ratio of the T-shaped microchannel and the flow rate were appropriately leveraged

A CNN-Based Advertisement Recommendation through Real-Time User Face Recognition

The advertising market’s use of smartphones and kiosks for non-face-to-face ordering is growing. An advertising video recommender system is needed that continuously shows advertising videos that match a user’s taste and displays other advertising videos quickly for unwanted advertisements. However, it is difficult to make a recommender system to identify users’ dynamic preferences in real time. In this study, we propose an advertising video recommendation procedure based on computer vision and deep learning, which uses changes in users’ facial expressions captured at every moment. Facial expressions represent a user’s emotions toward advertisements. We can utilize facial expressions to find a user’s dynamic preferences. For such a purpose, a CNN-based prediction model was developed to predict ratings, and a SIFT algorithm-based similarity model was developed to search for users with similar preferences in real time. To evaluate the proposed recommendation procedure, we experimented with food advertising videos. The experimental results show that the proposed procedure is superior to benchmark systems such as a random recommendation, an average rating approach, and a typical collaborative filtering approach in recommending advertising videos to both existing users and new users. From these results, we conclude that facial expressions are a critical factor for advertising video recommendations and are helpful in properly addressing the new user problem in existing recommender systems

Analyzing the Impact of Components of Yelp.com on Recommender System Performance: Case of Austin

As people’s demand for eating out is steadily increasing, the number of restaurants is continuously increasing, and catering industry platforms such as Yelp, Open Table, and Zomato provide basic information and evaluation information of restaurants and restaurant recommendation services suitable for users. Existing research on recommending restaurants mainly uses only evaluation information to find neighbors, and the use of user and restaurant information is still in its infancy. In addition, there is little study on how various types of input information affect the performance of the recommender system. This study examines the influence of three component information provided by Yelp.com on the performance of the recommender system using various real restaurants, reviews, and users dataset provided by Yelp.com. For this purpose, Two Phase Experiment was designed, and restaurant data located in Austin, Texas, USA, which has the largest number of review data, was collected. As a result of the experiment, elite status, the cumulative number of reviews, price range and average rating of restaurants could improve the recommendation performance

A Hybrid CNN-Based Review Helpfulness Filtering Model for Improving E-Commerce Recommendation Service

As the e-commerce market grows worldwide, personalized recommendation services have become essential to users’ personalized items or services. They can decrease the cost of user information exploration and have a positive impact on corporate sales growth. Recently, many studies have been actively conducted using reviews written by users to address traditional recommender system research problems. However, reviews can include content that is not conducive to purchasing decisions, such as advertising, false reviews, or fake reviews. Using such reviews to provide recommendation services can lower the recommendation performance as well as a trust in the company. This study proposes a novel review of the helpfulness-based recommendation methodology (RHRM) framework to support users’ purchasing decisions in personalized recommendation services. The core of our framework is a review semantics extractor and a user/item recommendation generator. The review semantics extractor learns reviews representations in a convolutional neural network and bidirectional long short-term memory hybrid neural network for review helpfulness classification. The user/item recommendation generator models the user’s preference on items based on their past interactions. Here, past interactions indicate only records in which the user-written reviews of items are helpful. Since many reviews do not have helpfulness scores, we first propose a helpfulness classification model to reflect the review helpfulness that significantly impacts users’ purchasing decisions in personalized recommendation services. The helpfulness classification model is trained about limited reviews utilizing helpfulness scores. Several experiments with the Amazon dataset show that if review helpfulness information is used in the recommender system, performance such as the accuracy of personalized recommendation service can be further improved, thereby enhancing user satisfaction and further increasing trust in the company

Analyzing Determinants of Job Satisfaction Based on Two-Factor Theory

For one company to have a competitive advantage and sustainability over others, its human resource management is of the utmost importance to secure competent employees. As job satisfaction plays a critical role in securing excellent manpower and enhancing corporate performance, it is essential to identify factors that would affect employees’ job satisfaction. Recently, writing reviews with integrity on job portal sites by former and current employees has become prevalent as such websites have guaranteed the reviewers’ anonymity. For this reason, we collected a vast amount of review data over nine industries, such as IT web communication, from one of the representative job portal sites in South Korea, Job Planet, and investigated factors that affect one’s job satisfaction based on the two-factor theory. As a result, it was found that (1) both motivation and hygiene factors had a substantial effect on job satisfaction over all industries; (2) the moderating effect between former and current employees was different for each industry; and (3) there was no moderating effect on job satisfaction between motivation and hygiene factors