Enabling user to user interactions in web lectures with history-aware user awareness
نویسندگان
چکیده
User awareness has become a popular feature in many social web applications. In classic text-based web-systems, user awareness features show how many users are online in a web application or how many users are accessing the same web page. When time-based media like web lectures are concerned this approach comes to its limits since time-based media are inherently different from classic text-based media. The main difference in these two types of media is that text-based media can easily be skimmed at a glance while videoor audio-objects have to be fully replayed when users want to grasp their content. This paper presents an approach that employs time-based usage statistics for all users in a web lecture system in order to provide users with a means to communicate with other users who are online and have watched those parts of a media object that the user is interested in. The work is implemented as a prototype application in the context of the Opencast Matterhorn project – an open source based project for producing, managing and distributing academic video content.
منابع مشابه
Use of Semantic Similarity and Web Usage Mining to Alleviate the Drawbacks of User-Based Collaborative Filtering Recommender Systems
One of the most famous methods for recommendation is user-based Collaborative Filtering (CF). This system compares active user’s items rating with historical rating records of other users to find similar users and recommending items which seems interesting to these similar users and have not been rated by the active user. As a way of computing recommendations, the ultimate goal of the user-ba...
متن کاملWeb pages ranking algorithm based on reinforcement learning and user feedback
The main challenge of a search engine is ranking web documents to provide the best response to a user`s query. Despite the huge number of the extracted results for user`s query, only a small number of the first results are examined by users; therefore, the insertion of the related results in the first ranks is of great importance. In this paper, a ranking algorithm based on the reinforcement le...
متن کاملEvolutionary User Clustering Based on Time-Aware Interest Changes in the Recommender System
The plenty of data on the Internet has created problems for users and has caused confusion in finding the proper information. Also, users' tastes and preferences change over time. Recommender systems can help users find useful information. Due to changing interests, systems must be able to evolve. In order to solve this problem, users are clustered that determine the most desirable users, it pa...
متن کاملContext-Aware Recommender Systems: A Review of the Structure Research
Recommender systems are a branch of retrieval systems and information matching, which through identifying the interests and requires of the user, help the users achieve the desired information or service through a massive selection of choices. In recent years, the recommender systems apply describing information in the terms of the user, such as location, time, and task, in order to produce re...
متن کاملRRLUFF: Ranking function based on Reinforcement Learning using User Feedback and Web Document Features
Principal aim of a search engine is to provide the sorted results according to user’s requirements. To achieve this aim, it employs ranking methods to rank the web documents based on their significance and relevance to user query. The novelty of this paper is to provide user feedback-based ranking algorithm using reinforcement learning. The proposed algorithm is called RRLUFF, in which the rank...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- Interact. Techn. Smart Edu.
دوره 8 شماره
صفحات -
تاریخ انتشار 2011