The state-of-the-art in personalized recommender systems for social networking

With the explosion of Web 2.0 application such as blogs, social and professional networks, and various other types of social media, the rich online information and various new sources of knowledge flood users and hence pose a great challenge in terms of information overload. It is critical to use intelligent agent software systems to assist users in finding the right information from an abundance of Web data. Recommender systems can help users deal with information overload problem efficiently by suggesting items (e.g., information and products) that match users’ personal interests. The recommender technology has been successfully employed in many applications such as recommending films, music, books, etc. The purpose of this report is to give an overview of existing technologies for building personalized recommender systems in social networking environment, to propose a research direction for addressing user profiling and cold start problems by exploiting user-generated content newly available in Web 2.0

The contribution of data mining to information science

The information explosion is a serious challenge for current information institutions. On the other hand, data mining, which is the search for valuable information in large volumes of data, is one of the solutions to face this challenge. In the past several years, data mining has made a significant contribution to the field of information science. This paper examines the impact of data mining by reviewing existing applications, including personalized environments, electronic commerce, and search engines. For these three types of application, how data mining can enhance their functions is discussed. The reader of this paper is expected to get an overview of the state of the art research associated with these applications. Furthermore, we identify the limitations of current work and raise several directions for future research

The Method of Constructing Recommendations Online on the Temporal Dynamics of User Interests Using Multilayer Graph

The problem of the online construction of a rating list of objects in the recommender system is considered. A method for constructing recommendations online using the presentation of input data in the form of a multi-layer graph based on changes in user interests over time is proposed. The method is used for constructing recommendations in a situation with implicit feedback from the user. Input data are represented by a sequence of user choice records with a time stamp for each choice. The method includes the phases of pre-filtering of data and building recommendations by collaborative filtering of selected data. At pre-filtering of the input data, the subset of data is split into a sequence of fixed-length non-overlapping time intervals. Users with similar interests and records with objects of interest to these users are selected on a finite continuous subset of time intervals. In the second phase, the pre-filtered subset of data is used, which allows reducing the computational costs of generating recommendations. The method allows increasing the efficiency of building a rating list offered to the target user by taking into account changes in the interests of the user over time

Detection of Trending Topic Communities: Bridging Content Creators and Distributors

The rise of a trending topic on Twitter or Facebook leads to the temporal emergence of a set of users currently interested in that topic. Given the temporary nature of the links between these users, being able to dynamically identify communities of users related to this trending topic would allow for a rapid spread of information. Indeed, individual users inside a community might receive recommendations of content generated by the other users, or the community as a whole could receive group recommendations, with new content related to that trending topic. In this paper, we tackle this challenge, by identifying coherent topic-dependent user groups, linking those who generate the content (creators) and those who spread this content, e.g., by retweeting/reposting it (distributors). This is a novel problem on group-to-group interactions in the context of recommender systems. Analysis on real-world Twitter data compare our proposal with a baseline approach that considers the retweeting activity, and validate it with standard metrics. Results show the effectiveness of our approach to identify communities interested in a topic where each includes content creators and content distributors, facilitating users' interactions and the spread of new information.Comment: 9 pages, 4 figures, 2 tables, Hypertext 2017 conferenc

Processing and Linking Audio Events in Large Multimedia Archives: The EU inEvent Project

In the inEvent EU project [1], we aim at structuring, retrieving, and sharing large archives of networked, and dynamically changing, multimedia recordings, mainly consisting of meetings, videoconferences, and lectures. More specifically, we are developing an integrated system that performs audiovisual processing of multimedia recordings, and labels them in terms of interconnected “hyper-events ” (a notion inspired from hyper-texts). Each hyper-event is composed of simpler facets, including audio-video recordings and metadata, which are then easier to search, retrieve and share. In the present paper, we mainly cover the audio processing aspects of the system, including speech recognition, speaker diarization and linking (across recordings), the use of these features for hyper-event indexing and recommendation, and the search portal. We present initial results for feature extraction from lecture recordings using the TED talks. Index Terms: Networked multimedia events; audio processing: speech recognition; speaker diarization and linking; multimedia indexing and searching; hyper-events. 1