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Social tagging systems leverage social interoperability by facilitating the searching, sharing, and exchanging of tagging resources. A major drawback of existing social tagging systems is that social tags are used as keywords in keyword-based search. They focus on keywords and human interpretability rather than on
engines are keyword-matching mechanism-based, and the existing full-text query search engines are inadequate at retrieving relevant information from various oral queries. With only predefined words and sentence-based recommendations, a social robot may not suggest the correct items, if items retrieved along with the
Nowadays people love travel to escape the grind of regular daily life, for the sake of convenience to web information sourced on internet with most search engines, the journey tours are easily found, but the feedbacks are compared with the keyword you entered, not truly responded to your intention, therefore, diverse
Many e-commerce web sites such as online book retailers or specialized information hubs such as online movie databases make use of recommendation systems where users are directed to items of interests based on past user interactions. While keyword based approaches are naive and do not take content or context into
recommenders in E-Learning system have not been fully figured out. In this paper, a novel personalized semantic recommendation system (PSRS) for E-Learning is designed. The proposed PSRS system employs the Video Structurized Description (VSD) technique to extract the initial keywords description of the learning contents, and then
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.