摘要
社交网络如Facebook, Twitter,新浪微博等已成为当今世界最为流行的信息分享平台。随着社交网络的快速发展,用户规模的不断扩大,信息更新的不断加快,社交网络中的用户找到自己感兴趣的信息变得越发困难。如何帮助社交网络用户过滤掉不感兴趣的信息,提供符合用户兴趣需求的个性化推荐服务成为目前学术界和工业界关注的研究热点。基于协同过滤技术的传统推荐方法虽然在个性化推荐研究领域中取得了一定成果,但由于社交网络自身的复杂性,使传统个性化推荐方法在社交网络应用场景下的推荐质量不高。
针对上述问题,本论文重点研究社交网络的社会关系、兴趣传播、信任传递和时间因素对个性化推荐方法产生的影响,提出新的社交网络个性化推荐方法,满足社交网络用户的个性化信息需求,实现高质量的社交网络个性化推荐。本论文的主要研究内容和研究成果总结如下：
1.通过分析社交网络中朋友关系和用户—项目历史数据,建立基于用户朋友关系的社交网络项目推荐模型。该模型结合用户兴趣和社交网络中用户朋友的兴趣构建概率图模型进行推理和预测,将用户和用户朋友的共同兴趣映射为潜在因子空间中的潜在因子,进行潜在因子分析,利用目标用户和朋友用户的共同兴趣预测目标用户的兴趣,实现基于社交网络朋友关系的个性化推荐。通过对实际应用场景中推荐结果的评估,检验社交网络项目推荐模型的有效性和可扩展性。
2.为了研究社交兴趣信息传播对个性化推荐方法的影响,扩大社交网络中朋友关系的影响范围,建立社交网络用户兴趣模型。该模型从两方面刻画用户兴趣：一方面利用用户—项目点击的历史数据刻画目标用户的个人兴趣；另一方面通过定义兴趣传播刻画社交网络其他用户的个人兴趣对目标用户个人兴趣的影响。社交网络用户兴趣模型结合用户的个人兴趣和兴趣传播两方面信息对目标用户进行个性化推荐,准确地反映社交网络对个人兴趣的影响。实验结果表明基于社交网络用户兴趣模型的个性化推荐方法提高了推荐质量。
3.将推荐信任引入到个性化推荐方法,提出一种基于社交网络推荐信任的的个性化推荇方法。该方法首先根据社交网络中用户朋友关系之间的相似度计算直接信任,用直接信息度量朋友之间的隐含信任关系。定义两种社交网络的信任传递方式,计算社交网络用户间的信任传递。根据信任传递距离对间接信任加权,刻画社交网络的网络结构对用户之间推荐信任的影响,根据推荐信仃为用户匹配到信任用户。对选择到的信任用户与目标用户的共同兴趣进行潜在因子分析,构建基于推荐信任潜在因子模型的个性化推荐方法。
4.将社交网络的动态性和用户反馈信息融入到社交网络个性化推荐方法中,提出基于时间感知和用户反馈的个性化推荐方法。该方法利用时间衰减因子对带有时间加权的动态社交网络进行兴趣衰减分析,使时间间隔较近用户的选择行为对资源对象的推荐作用获得较高的贡献度,体现用户兴趣的时间效应特性。通过扩展相似度计算方法,将用户反馈表示为正反馈信息和负反馈信息,考虑用户反馈信息对推荐的影响。
本论文深入研究影响社交网络个性化推荐方法推荐质量的主要因素,建立能够准确预测用户兴趣的推荐模型与学习算法,形成真实反映社交网络用户兴趣的个性化推荐方法。将提出的推荐模型和方法应用到实际社交网络推荐场景中,实现为社交网络用户提供高质量个性化推荐的研究目标,为进一步研究社交网络个性化推荐方法提供有益参考。
Online social networks are becoming the most popular platforms of information exchange, where users build friendship connections and share their interest information. Some popular social network sites such as Facebook, Twitter, and Sina Weibo, are attracting thousands of new users each day. The amount of user-generated information is increasing far more quickly that users can't handle the information overload without the support of recommendation methods.
Collaborative filtering based recommendation methods are one of the most successful solutions to the information overload issue, which have been widely used in real world recommendation services and systems. Generally, collaborative filtering based recommendation methods build on the user-item similarity measures. The basic idea underlying collaborative filtering methods is that if two users have historically had similar interests on some items, they are likely to be interested in other items similarly. However, collaborative filtering based recommendation methods treat user-item information equally and ignore the context information in social networks, such as social relationship, social influence, information propagation over the connections between users and temporal factor, which affect the qulity of recommendation methods.
Aiming at improving the quality of recommendation methods in social networks, this thesis addresses a number of important questions regarding the performance of recommendation, and then proposes the recommendation methods. The main contributions of this thesis are following.
1. It proposes a social item recommendation (SIR) model based on neighborhood model and latent variable model. The SIR model encodes both the interest and friendship information, associates the latent variables with the interest similarities between the pair of the active user and his followers. Then extend the SIR model to SIR+for considering the social features during the inference of social item recommendation. The experimental results demonstrate that both SIR and SIR+outperform the traditional collaborative filtering methods, and SIR+achieves a better performance than SIR.
2. In order to address the information propagation impact on recommendations in social networks, it applies a user interest model (UInRec), which characterizes the user interests and interest propagation in online social networks. UInRec fuses both social network features and user-item click information for recommendations. Firstly, UInRec uses collaborative filtering techniques to model the user interest from user-item click information. Secondly, UInRec models the strength of followship by social action features. UInRec combines both the user interest model and interest propagation model for social recommendation and improves the performance of recommendation in social networks.
3. Considering the trust relationship in social networks for recommendation, it presents a trust metric to quantitatively measure the recommendation trust between pairs of users by aggregating the implicit trust and trust propagation values, and then selects the neighbours based on the trust metric. After that, it proposes a trust-based latent factor model, which allows us to incorporate pairwise trust values into the latent factor model for top-k item recommendation. Finally, the experiments are conducted on Sina Weibo and the results show that the proposed method leads to a substantial increase in the performance of top-k item recommendation.
4. Exploiting the context information such as time, relationship and user feedback information in social network, it presents a time-aware social recommendation method based on user feedback for top-k item recommendation. This method incorporates the temporal factors by introducing a time weight function, which models the decay of user interest. Moreover, the method considers the user positive feedback and negative feedback information, as well as the social relationship information for recommendation, Experimental results and analysis show that the proposed method outperforms the collaborative filtering method for top-k item recommendation in social networks.
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