Recommender systems are important tools for users to identify their preferred items and for businesses to improve their products and services. In recent years, the use of online services for selection and reservation of hotels have witnessed a booming growth. Customer’ reviews have replaced the word of mouth marketing, but searching hotels based on user priorities is more time-consuming. This s...

Over the last few years, the realm of foreign language learning has witnessed an abundance of research concerning the effectiveness of corrective feedback on the acquisition of grammatical features, with the study of other target language subsystems, such as pronunciation, being few and far between. In order to bridge this gap, the present study intended to investigate and compare the immediate...

Collaborative Filtering(CF) is a widely accepted method of creating recommender systems. CF is based on the similarities among users or items. Measures of similarity including the Pearson Correlation Coefficient and the Cosine Similarity work quite well for explicit ratings, but do not capture real similarity from the ratings derived from implicit feedback. This paper identifies some problems t...

Various studies have confirmed the influential role of corrective feedback (CF) in the development of different linguistic skills and components. However, little, if any, research has been conducted on comparing types of linguistic errors treated by teachers through CF. To bridge this gap, this study sought to investigate the linguistic errors addressed and the types of CF provided by teachers....

Many online shopping malls in which explicit rating information is not available still have difficulty in providing recommendation services using collaborative filtering (CF) techniques for their users. Applying temporal purchase patterns derived from sequential pattern analysis (SPA) for recommendation services also often makes users unhappy with the inaccurate and biased results obtained by n...

The present study investigated EFL teachers’ beliefs about oral corrective feedback (CF), their CF-provision practices across elementary and intermediate levels, and their beliefs-practices correspondence. To this end, the researchers conducted a semi-structured interview with the teachers and went on an overall forty-hour observation of their classrooms across both levels. The findings reveale...

the present study investigated efl teachers’ beliefs about oral corrective feedback (cf), their cf-provision practices across elementary and intermediate levels, and their beliefs-practices correspondence. to this end, the researchers conducted a semi-structured interview with the teachers and went on an overall forty-hour observation of their classrooms across both levels. the findings reveale...

Collaborative filtering (CF)-based recommender systems represent a promising solution for the rapidly growing mobile music market. However, in the mobile Web environment, a traditional CF system that uses explicit ratings to collect user preferences has a limitation: mobile customers find it difficult to rate their tastes directly because of poor interfaces and high telecommunication costs. Imp...

Collaborative filtering (CF) is a widely used method in recommendation systems. Linear models are still the mainstream of collaborative research methods, but non-linear probabilistic beyond limit linear model capacity. For example, variational autoencoders (VAEs) have been extensively CF, and achieved excellent results. Aiming at problem prior distribution for latent codes VAEs traditional CF t...

Collaborative filtering (CF) is widely used in e-commerce recommender systems, which helps the online users to identify the right products to purchase. However, CF-based recommender systems suffer poor quality of recommendation due to the sparsity issue. To address this problem, in this paper we propose an adaptive recommendation method based on small-world implicit trust network. We first pres...