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A personalized recommendation strategy based on trusted social community

机译：基于可信社会界的个性化推荐战略

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Personalized recommendation technology has become a very effective approach to cope with the problem of information overload in E-commerce. Currently, there are three recommendation strategies: content-based recommender strategy, collaborative filtering recommender strategy, and hybrid strategy. Since characterized as simple, easy to implement, and with high accuracy, collaborative filtering recommender algorithm is widely used. But typical problems still exist in traditional collaborative filtering algorithms, for example, data sparsity, cold start, easy to be attack, and poor ability for migration of user preference, etc, which lead to decreasing of recommendation accuracy and decline of user confidence for recommendation system. In this paper, a novel personalized recommendation strategy is proposed based on traditional collaborative filtering technology, aimed at the issues of poor interest migration and easy to be attack. We call it trusted social network community based recommendation strategy Trusted communities in which users all with similar preference are been detected from user social network. And then recommendation is been given based on these trusted communities. Nodes in the trusted community all have trust relationship between them, so it can effectively avoid attacks from malicious nodes. Meanwhile, a mechanism used for trust relationship feedback has been introduced. Users can submit some feedbacks for their trust relationship after every transaction, which means while the quality of item they received surpass their expectation, they could enhance the relationship, and while fall behind their expectation, they can weaken the relationship. This mechanism provide a good approach to solve change of preference. Experiments show that the algorithm can effectively improve the accuracy of recommendation, and enhance customer satisfaction.
机译：个性化推荐技术已成为应对电子商务过载问题的非常有效的方法。目前，有三种推荐策略：基于内容的推荐战略，协作过滤推荐战略和混合策略。由于表征为简单，易于实现，并且具有高精度，因此广泛使用协同过滤推荐算法。但传统的协作过滤算法中仍然存在典型的问题，例如数据稀疏，冷启动，易于攻击，迁移用户偏好等差，这导致推荐准确性和用户信心的衰落减少系统。本文提出了一种基于传统的协作过滤技术的新颖的个性化推荐战略，旨在涉及贫困利息迁移和易于攻击的问题。我们称之为信赖的社交网络社区基于建议的推荐战略，可信社区，其中从用户社交网络中检测到所有具有相似偏好的用户。然后基于这些值得信赖的社区给出了建议。信任社区中的节点都有他们之间的信任关系，因此它可以有效地避免攻击恶意节点。同时，介绍了用于信任关系反馈的机制。用户可以在每次交易后提交一些反馈，以获得他们的信任关系，这意味着他们获得的物品质量超过他们的期望，他们可以提高关系，而虽然落后于他们的期望，但它们可以削弱这种关系。这种机制提供了解决偏好变化的良好方法。实验表明，该算法可以有效提高推荐的准确性，提高客户满意度。

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