Information supply: An approach based on demand modelling and information filtering

Abstract

Collaborative team members usually come from diverse disciplines; their demands for information are also different from each other. This paper is mainly concerned with how to capture designers’ demands for information in a collaborative team. From a workflow perspective, designers’ information demand is modelled considering three aspects: members’, roles’, and tasks’ requirements for information. Based on a static model of information demand and information filtering technologies, a dynamic model is proposed so that the designers’ information demand could be derived automatically. In addition, the appropriate volume of information for designers’ needs could also be determined intelligently. With the information demand model, an information supply system could be developed to realize the following objective: the delivery of an appropriate volume of information within an appropriate domain, delivered to the appropriate user in the collaborative team.

Collaborative Recommender System A Review

“…Recommender System (RS) can be defined as systems that guide users toward interesting or useful items in a large space of possible options [4]. It provides a platform to deliver the right knowledge in the right context to the right person in the right volume [5]. The advantages of RS are RS have been used in several domains to support user in decision making and decrease the effect of information overload [6].…”

Distributed recommender for peer-to-peer knowledge sharing

“…The centralized repositories facilitate the knowledge sharing and reusing among members. Furthermore, some intelligent modules in the centralized knowledge servers (e.g., recommender agents) can proactively distribute knowledge to users according to their demands for knowledge [32]. It promotes the efficiency of knowledge sharing in an organization.…”

An inner-enterprise knowledge recommender system

“…Internet/Intranet technologies provide a strong basis and opportunity for knowledge sharing among people with similar interests (Li, Montazemi, & Yuan, 2006). The knowledge recommender also needs to learn users' demands for knowledge from their colleagues with similar interests (Zhen, Jiang, Song, Liu, & Liang, 2008). The knowledge recommendation is a more advanced way for users to acquire knowledge, it will not need users to seek knowledge by themselves; sometimes users may not know what they should seek clearly.…”

“…Yeong et al proposed a new methodology in which customer purchase sequences are used to improve the quality of CF-based recommendations (Yeong, Yoon, & Soung, 2005). Base on demand modeling and information filtering technologies, an information supply model was proposed (Zhen et al, 2008).…”