Document recommendation based on the analysis of group trust and user weightings

Abstract

Collaborative filtering (CF) has been applied in various domains to resolve problems related to information overload. In a knowledge-intensive environment, most works are processed through teamwork. A user on a team can reference task-related documents from other trusted members to support work on the task. However, the traditional personalised recommender systems no longer meet the demand of teams or groups. Therefore, this work proposes a novel document recommendation method based on a group-based trust model. Our method will analyse the degrees of trust among users in a group and then identify the trustworthy users. The proposed group trust consists of a hybrid personal trust (HPT) model and users’ importance (i.e. users’ activity, similarity and reputation) in a group. Group-based trust is then integrated with the user-based CF to recommend documents to users. The experiments demonstrate that the proposed method can provide better performance than other trust-based recommendation methods; it not only obtains reliable trust values to increase the accuracy of predictions but also enhances the recommendation quality.

Development of College Students' Innovation and Entrepreneurship Information Platform Based on Computer Technology

“…The software architecture of the innovation and entrepreneurship platform for college students is divided into two parts: server and client [6]. The server interacts with Internet data information and the system disk in the cloud server, and the client interacts with the system disk and data disk in the cloud server.…”

A context-aware diversity-oriented knowledge recommendation approach for smart engineering solution design

“…However, these items are usually not comprehensive and sometimes already known, thus performing poorly in covering users' knowledge needs in the solution design process. Another common approach, CF [16][17][18][19], can rapidly supply knowledge items according to similar users' behaviors, but it lacks a pertinence to the facing context. As a result, the generated design solution tends to be generic and sometimes it becomes infeasible due to special constraints in particular contexts.…”

A Collaborative Filtering Probabilistic Approach for Recommendation to Large Homogeneous and Automatically Detected Groups

“…Visualization of group recommendations is also a challenging task; authors in [72] provide visual presentations and intuitive explanations. Finally, using social trust information, [73] identifies trustworthy users and it analyses the degrees of trust among users in a group.…”