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

Time-aware topic recommendation based on micro-blogs

Topic recommendation can help users deal with the information overload issue in micro-blogging communities. This paper proposes to use the implicit information network formed by the multiple relationships among users, topics and micro-blogs, and the temporal information of micro-blogs to find semantically and temporally relevant topics of each topic, and to profile users' time-drifting topic interests. The Content based, Nearest Neighborhood based and Matrix Factorization models are used to make personalized recommendations. The effectiveness of the proposed approaches is demonstrated in the experiments conducted on a real world dataset that collected from Twitter.com

Introduction to the special issue on Graph and Network Analysis

International audienceThis fifth issue of the Journal of Interdisciplinary Methodologies and Issues in Science (JIMIS) is dedicated to methods designed for the analysis of graphs and networks, as well as to applied works relying on the analysis of graphs and networks in specific domains. It can be considered as a follow-up of the second issue of JIMIS, which focused on the modeling of social systems through graphs. Like before, it includes strongly interdisciplinary works. In addition, this issue widens the scope of the considered problems and systems, as the focus is not only on social systems anymore

Preference fusion and Condorcet's Paradox under uncertainty

Facing an unknown situation, a person may not be able to firmly elicit his/her preferences over different alternatives, so he/she tends to express uncertain preferences. Given a community of different persons expressing their preferences over certain alternatives under uncertainty, to get a collective representative opinion of the whole community, a preference fusion process is required. The aim of this work is to propose a preference fusion method that copes with uncertainty and escape from the Condorcet paradox. To model preferences under uncertainty, we propose to develop a model of preferences based on belief function theory that accurately describes and captures the uncertainty associated with individual or collective preferences. This work improves and extends the previous results. This work improves and extends the contribution presented in a previous work. The benefits of our contribution are twofold. On the one hand, we propose a qualitative and expressive preference modeling strategy based on belief-function theory which scales better with the number of sources. On the other hand, we propose an incremental distance-based algorithm (using Jousselme distance) for the construction of the collective preference order to avoid the Condorcet Paradox.Comment: International Conference on Information Fusion, Jul 2017, Xi'an, Chin

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