信任与推荐驱动的个性化学习路径探索

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“可靠的同伴与有用的资源：在信任和推荐意识环境中的最佳个性化学习路径” 文章主要探讨了在当前Web 2.0时代和大规模使用点对点网络的背景下，如何在终身学习过程中寻找并筛选出最可靠、最有价值的学习资源。随着互联网的发展，我们能够从分散的数据源获取比以往更多的信息，这极大地提升了终身学习的效果。然而，随之而来的一个关键问题浮出水面：如何确保所使用的资源是可靠的，并且如何判断提供这些资源的同伴是否值得信赖。作者Vincenza Carchiolo、Alessandro Longheu和Michele Malgeri来自意大利卡塔尼亚大学的电子工程和电信工程系，他们提出了一种新的模型，旨在结合信任和推荐机制来搜索个性化且由可信赖同伴推荐的学习路径。这一模型考虑了在社交网络和点对点环境中的信任和声誉因素，以帮助用户更有效地找到适合自己的学习资料。文章提到了几个核心关键词：电子学习（E-learning）、社交网络、信任、声誉以及点对点网络。这些关键词揭示了研究的焦点在于如何利用社交网络的交互性和点对点网络的分布式特性，同时引入信任和声誉评估体系，以增强学习资源推荐的准确性和可靠性。在学术历程方面，文章于2008年12月9日初次提交，2009年10月26日经过修订后再次提交，最终于2009年12月22日被接受发表。这表明了研究经过了多次修改和完善，以确保其学术质量和研究深度。该模型的应用可能包括以下几个方面： 1. **信任评估**：通过分析用户在学习社区中的交互历史和行为模式，建立一个动态的信任评估系统，以判断同伴的可靠性。 2. **声誉系统**：用户的行为和贡献会被记录，形成声誉分数，高声誉的用户提供的资源更可能被推荐。 3. **个性化推荐**：根据用户的学习需求、兴趣和能力，结合同伴的信任度，提供个性化的学习路径建议。 4. **动态更新**：学习路径和资源推荐会随着用户反馈和网络中其他用户的行为动态调整，以保持推荐的时效性和有效性。 5. **点对点网络的利用**：利用P2P网络的分布式特性，实现教育资源的快速分享和访问，同时降低中心化服务的压力。 6. **社区互动**：鼓励用户之间的互动和协作，共同评价和优化学习资源，促进知识的共享和传播。通过这样的模型，教育者和学习者能够在海量信息中找到最相关、最可靠的资源，从而提高学习效率，促进个人和群体的知识成长。这个模型不仅适用于传统的在线教育平台，也适用于任何基于网络的自我学习环境，对于未来教育技术的发展具有重要意义。

The use of the P2P approach in e-learning systems promotes this kind of collaboration and changes the e-learning system

from a static framework, in which users access resources from one or more centralised systems managed by a single author-

ity, to a dynamic framework in which resources are provided by each user of the system.

Ariadne is based on the P2P model inspired by IEEE LOMster [37–39], a project with the goal to support LO sharing and

reuse. Among its facilities, LOMster allows users to add metadata to their learning objects in addition to a system generated

description in order to improve resources sharing. LOMster uses JXTA as a P2P framework adopting the associated publishing

mechanism to locate peers in the network (peers have to previously register). To ﬁnd resources, a peer executes a limited

ﬂooding sending its query to all registered nodes [40].

Edutella [41] is a well-known P2P network designed for distributed environments. The project deﬁnes a framework that

provides a mechanism to manage peer access, a reference model called ‘‘Edutella Common Data Model” allowing the sharing

of queries and messages, but each peer refers only to local data representation and a query model [42].

P2P based e-learning systems allow collaboration, spreading of information and experience [9,43]. Of course, some mech-

anisms – often based on ontologies – must be present in order to integrate the exchanged data allowing users to both query

the system and extract meaningful information. Simple but effective approaches store information about a resource in sev-

eral peers to increase reliability, but each user might also share a managed resource thereby extending the whole system

[44,42].

In traditional distributed environments, service providers and requesters are usually known to each other. Often shared

information in the environment tells which parties can provide what kind of services and which parties are entitled to make

use of those services. Thus, trust between parties is a straightforward question. In spite of this, the above systems adopt dif-

ferent approaches when building learning paths, but only preliminary studies can be found on trust negotiation in e-learning

systems.

Trust was studied in social sciences, business and psychology before it became central to computer science research; the

survey [11] offers a complete overview of most recent issues concerning trust. In [45], the ﬁrst work providing a formal mod-

el of trust, the value of trust is chosen within the range [1,1], covering from complete distrust to full trust. In [46], however,

the use of a range with negative values to model distrust is somehow criticised, mainly due to algorithmic-related issues (not

necessarily real values for trust matrix eigenvector); authors also debate whether a trust score of 0 translates as distrust or as

no opinion. We choose to assign trust values in the range [0,1] since rather than explicitly distrust the peer i (i.e. using values

in the [1,0] range), peers commonly prefer not to express any opinion about i (i.e. a value of 0 is simply indicated).

The paper [21] shows the question of trust in the computer science context as a propagation of a social matter (‘‘in today’s

connected world, it is possible and common to interact with unknown people...”). The metric they deﬁne for trust evaluation is

based on their previous work, MoleTrust, that predicts the trust score of source user on target user by walking the social net-

work starting from the source user and by propagating trust along trust edges; intuitively the trust score of a user depends

on the trust statements of other users on them and their trust scores.

In recent decades, recommender systems have gained an important role in today’s networked worlds because they pro-

vide some tools to support decision making in selecting reliable from unreliable. Reliability is often expressed through a trust

value with which each agent labels its neighbours; [47,48] explore this, but they do not investigate the topic of formation of

trust based on real world social studies. Some recent works have suggested to combine distributed recommendation systems

with trust and reputation mechanisms [47,12].

Ziegler and Golbeck [49,16] believe that trust and user similarity are correlated, in particular that the notion of trust

should reﬂect user similarity.

Therefore, a reputation system is an important tool in any network, and assume a central role in emerging P2P networks

where many people interact with many others in a sort of ‘‘democratic” fashion. Some authors discuss decentralised methods

that approximate ranks according to local history and a notion of a neighbourhood [50] where trust is calculated taking

advantage of small-world properties often emerging in networks that mimic the real world. In the P2P area EigenTrust

[51] distributed implementation of PageRank [52] is proposed that also needs also a distributed structure to store data

and imposes a pre-trust of all nodes belonging to the net thus reducing the ‘‘de-centralisation”.

A preliminary attempt at trust negotiation in e-learning systems is introduced in [53] in which the trust negotiation is

managed using PeerTrust a

policy

language tailored for negotiating and establishing trust in a distributed environment. It

is used in a Edutella-based system [54] to asses the trust of LO.

3. The proposed e-learning framework

The model of the e-learning trust- and recommendation-aware framework we propose is informally introduced in the

following (in Section 3.2 formal deﬁnitions are provided).

Our model is based on the P2P paradigm, the topology of this P2P network is depicted in Fig. 1 where circles represent

peers and squares represent resources. Edges used to build the network can be categorised as follows:

1. The solid directed edges joining peers represent trust relationships, i.e. X had some direct experience with Z and assigned

a trustworthiness to Z, and similarly occurred for peers Z with respect to Y. The trustworthiness is a value in the range

[0,1] used to label the edge (these weights are omitted in the ﬁgure). In addition, several expertise ranks regarding dis-

V. Carchiolo et al. / Information Sciences 180 (2010) 1893–1907

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信任与推荐驱动的个性化学习路径探索

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