درخت حوزه‌های تخصصی

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 study is aimed at designing a recommender system based on the explicit and implicit preferences of the customers in order to increase prediction’s accuracy. In this study, we have combined sentiment analysis with the Collaborative Filtering (CF) based on deep learning for user groups in order to increase system accuracy. The proposed system uses Natural Language Processing (NLP) and supervised classification approach to analyze sentiments and extract implicit features. In order to design the recommender system, the Singular Value Decomposition (SVD) was used to improve scalability. The results show that our proposed method improves CF performance.

Nowadays with the ever-increasing growth in social media platforms and the creation of different forms of online activism, the word known as “Campaign” has become a familiar and useful term in people’s everyday lives. Campaigns with all kinds of social aims especially using Hashtags are run on social media platforms by individuals, charities, NGOs, governments, municipalities and brand companies and with peoples' participation and value co-creation, appropriate results are achieved and the desired changes are made in people’s attitudes and the behavior of individuals, businesses, or governments. Therefore, there seems to be a new form of a virtual public sphere where there is action, dialog and social mobilization, which leads to the formation of public opinion and social change in the digital era. In this paper, using a critical review method, we are going to investigate a public sphere's literature and study its core features (1. access, 2. dialog, 3. action, and 4. public opinion) in digital campaigns and online platforms. Then, in the last part, we are going to map those in a real case study in order to further clarify the arguments and show how social campaigns on online platforms contribute to the formation of a public sphere in the digital era.

The focus of this study is on lecturer’s use of online technology in the higher education context. Precisely, this study aims to understand the effect of personal innovativeness in IT (PI) in determining technology adoption behavior of lecturers in the higher education institutes in Sri Lanka. In this study, the variable of personal innovativeness in IT is integrated with the UTAUT framework and thereby the causal paths which effects VLE adoption intention of individuals is examined. Literature suggests that domain-specific innovativeness is a crucial factor in determining an individual’s adoption of technological innovations. Therefore, understanding the multifaceted effects of this factor along with other significant factors can help higher education institutes to effectively endorse online technology among lectures, generating productive payoffs in the long run. The quantitative method was used for data collection, which yielded # 1253 responses through the Question Pro online survey tool. The targeted respondents were the registered lecturers in higher education institutes of Sri Lanka, selected based on simple random sampling method. Structural equation modelling (SEM) procedure was employed for data analysis using IBM SPSS (ver.21) and AMOS (Ver.26). The structural path analysis resulted in partial mediation, confirming that “lecturer’s innovativeness in IT” exerts its influence on VLE adoption intention by altering the mediators set in the study. Further, the study validated a unique set of factors that determine lecturer’s acceptance of VLE in a higher education setting.