基于多维信任模型的可信推荐方法研究
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摘要
随着互联网的飞速发展，网络信息量剧增，给用户提出了如何有效利用网络资源的挑战。推荐系统作为一种信息过滤技术，逐渐发展成为解决网络信息过载问题的一个有利工具。在众多的推荐方法中，协同过滤是迄今为止应用最广泛、最成功的技术之一。然而，由于推荐系统的开放性，一些恶意用户向系统提供大量的虚假评分信息从而使推荐结果产生严重偏离，导致用户对推荐系统的满意度下降。因此，面对这种用户概貌注入攻击，如何对用户做出可信推荐成为目前推荐系统中亟待解决的问题。本文从数据源的可信性、邻居选取策略、推荐系统的鲁棒性以及top-N推荐的可信性等几个方面对可信推荐方法进行了深入研究。
首先，针对已有信任计算方法对用户间信任关系度量准确性较差的问题，提出一种融合用户可疑度的多维信任模型。基于用户-项目评分数据，通过引入信息熵理论和基于密度的局部离群因子的思想，对用户的可疑度进行度量；根据信息源可信性理论，从多个视角对用户间的信任属性进行分析和度量，以提高用户间信任度计算的准确性。
其次，针对已有启发式推荐算法推荐质量较差的问题，提出一种基于双重邻居选取策略的协同过滤推荐方法。基于用户相似度计算结果，动态选取目标用户的兴趣相似用户集；采用提出的融合用户可疑度的多维信任模型，计算得到目标用户对每个兴趣相似用户的信任度，并以此作为选取可信邻居用户集的依据；采用双重邻居选取策略，结合常规的协同过滤推荐算法，完成对目标用户的推荐。
再次，针对已有基于模型的推荐算法鲁棒性较差的问题，提出一种融合可信邻居模型的鲁棒协同推荐方法。将提出的融合用户可疑度的多维信任模型与基线估计方法相结合，构建可信邻居模型；在此基础上，引入基于M-估计的矩阵分解方法，完成对目标用户的推荐。
然后，针对已有top-N推荐方法召回率较低和鲁棒性较差的问题，提出一种基于可靠用户的top-N推荐方法。采用提出的融合用户可疑度的多维信任模型度量用户间的信任关系，根据信任度计算结果，选取目标用户的可靠用户集；在此基础上，采用多种方法为目标用户选取候选推荐项目，并利用“均值法”融合策略给出目标用户的top-N推荐结果。
最后，对提出的方法进行了实验验证，和已有的方法进行了对比，并对今后的研究工作进行了展望。
With the rapid development of Internet, there has been a great increase in the amountof information. So how to make use of online resources effectively becomes a challengingtask. Recommender systems, as a kind of information filtering technology, have becomean effective way to solve the information overload problem. Specially, among thenumerous recommendation methods, collaborative filtering is one of the most widely usedand successful recommendation technologies. However, due to the openness ofrecommender systems, some malicious users might try to insert a lot of fake profiles intosystems in order to bias the recommendation results, which results in a decline in users’satisfaction with the recommender systems. Therefore, with the emergence of shillingattacks, how to provide trustworthy recommendation for target user has become a keyissue to be solved. In this thesis, we have made some deep research of trustworthyrecommendation approaches from the following four aspects: the credibility of data source,the strategy of choosing neighbors, the robustness of recommender systems and thecredibility of top-N recommendation.
Firstly, aiming at the problem that the existing computational approaches of trust cannot measure the trust relationship between users accurately, a multidimensional trustmodel incorporating users’ degree of suspicion is proposed. According to users’ ratings onitems, the users’ degree of suspicion are calculated through introducing the theory ofentropy and the idea of density-based local outlier factor. On the basis of that, in order toimprove the calculation accuracy for the degree of trust between users, the trust attributesare analyzed and measured from different angles using the source credibility theory.
Secondly, aiming at the problem that the existing heuristic recommendationalgorithms suffer from lower recommendation quality, a collaborative filteringrecommendation algorithm based on double neighbor choosing strategy is proposed. Onthe basis of the computational result of similarity, the preference similar users of targetuser are chosen dynamically. We can measure the degree of trust between target user andpreference similar user using the proposed multidimensional trust model incorporating users’ degree of suspicion. The trustworthy neighbor set of target user is selected inaccordance with the degree of trust between users. The recommendation for target user isgenerated by incorporating the double neighbor choosing strategy with the conventionalcollaborative filtering recommendation algorithm.
Thirdly, aiming at the problem that the existing model-based recommendationalgorithms have the disadvantage of weaker robustness, a robust collaborativerecommendation algorithm incorporating trustworthy neighborhood model is proposed. Atrustworthy neighborhood model is constructed by incorporating the proposedmultidimensional trust model with baseline estimate approach. On the basis of that, wecan provide recommendation for target user using the M-estimator based matrixfactorization approach.
Fourthly, aiming at the problems that the existing top-N recommendation approachessuffer from lower recall and weaker robustness, a top-N recommendation approach basedon reliable users is proposed. We can measure the degree of trust between users using theproposed multidimensional trust model. And the computational result is used to choosereliable users for target user. Moreover, based on the rating information of reliable users,we can choose candidate recommendation items using some approaches and providetop-N recommendation for target user using the strategy of average aggregation.
Lastly, we have compared the performance between the proposed approaches andothers to demonstrate the effectiveness of the proposed approaches. Also, we propose thefurther research work.
首先，针对已有信任计算方法对用户间信任关系度量准确性较差的问题，提出一种融合用户可疑度的多维信任模型。基于用户-项目评分数据，通过引入信息熵理论和基于密度的局部离群因子的思想，对用户的可疑度进行度量；根据信息源可信性理论，从多个视角对用户间的信任属性进行分析和度量，以提高用户间信任度计算的准确性。
其次，针对已有启发式推荐算法推荐质量较差的问题，提出一种基于双重邻居选取策略的协同过滤推荐方法。基于用户相似度计算结果，动态选取目标用户的兴趣相似用户集；采用提出的融合用户可疑度的多维信任模型，计算得到目标用户对每个兴趣相似用户的信任度，并以此作为选取可信邻居用户集的依据；采用双重邻居选取策略，结合常规的协同过滤推荐算法，完成对目标用户的推荐。
再次，针对已有基于模型的推荐算法鲁棒性较差的问题，提出一种融合可信邻居模型的鲁棒协同推荐方法。将提出的融合用户可疑度的多维信任模型与基线估计方法相结合，构建可信邻居模型；在此基础上，引入基于M-估计的矩阵分解方法，完成对目标用户的推荐。
然后，针对已有top-N推荐方法召回率较低和鲁棒性较差的问题，提出一种基于可靠用户的top-N推荐方法。采用提出的融合用户可疑度的多维信任模型度量用户间的信任关系，根据信任度计算结果，选取目标用户的可靠用户集；在此基础上，采用多种方法为目标用户选取候选推荐项目，并利用“均值法”融合策略给出目标用户的top-N推荐结果。
最后，对提出的方法进行了实验验证，和已有的方法进行了对比，并对今后的研究工作进行了展望。
With the rapid development of Internet, there has been a great increase in the amountof information. So how to make use of online resources effectively becomes a challengingtask. Recommender systems, as a kind of information filtering technology, have becomean effective way to solve the information overload problem. Specially, among thenumerous recommendation methods, collaborative filtering is one of the most widely usedand successful recommendation technologies. However, due to the openness ofrecommender systems, some malicious users might try to insert a lot of fake profiles intosystems in order to bias the recommendation results, which results in a decline in users’satisfaction with the recommender systems. Therefore, with the emergence of shillingattacks, how to provide trustworthy recommendation for target user has become a keyissue to be solved. In this thesis, we have made some deep research of trustworthyrecommendation approaches from the following four aspects: the credibility of data source,the strategy of choosing neighbors, the robustness of recommender systems and thecredibility of top-N recommendation.
Firstly, aiming at the problem that the existing computational approaches of trust cannot measure the trust relationship between users accurately, a multidimensional trustmodel incorporating users’ degree of suspicion is proposed. According to users’ ratings onitems, the users’ degree of suspicion are calculated through introducing the theory ofentropy and the idea of density-based local outlier factor. On the basis of that, in order toimprove the calculation accuracy for the degree of trust between users, the trust attributesare analyzed and measured from different angles using the source credibility theory.
Secondly, aiming at the problem that the existing heuristic recommendationalgorithms suffer from lower recommendation quality, a collaborative filteringrecommendation algorithm based on double neighbor choosing strategy is proposed. Onthe basis of the computational result of similarity, the preference similar users of targetuser are chosen dynamically. We can measure the degree of trust between target user andpreference similar user using the proposed multidimensional trust model incorporating users’ degree of suspicion. The trustworthy neighbor set of target user is selected inaccordance with the degree of trust between users. The recommendation for target user isgenerated by incorporating the double neighbor choosing strategy with the conventionalcollaborative filtering recommendation algorithm.
Thirdly, aiming at the problem that the existing model-based recommendationalgorithms have the disadvantage of weaker robustness, a robust collaborativerecommendation algorithm incorporating trustworthy neighborhood model is proposed. Atrustworthy neighborhood model is constructed by incorporating the proposedmultidimensional trust model with baseline estimate approach. On the basis of that, wecan provide recommendation for target user using the M-estimator based matrixfactorization approach.
Fourthly, aiming at the problems that the existing top-N recommendation approachessuffer from lower recall and weaker robustness, a top-N recommendation approach basedon reliable users is proposed. We can measure the degree of trust between users using theproposed multidimensional trust model. And the computational result is used to choosereliable users for target user. Moreover, based on the rating information of reliable users,we can choose candidate recommendation items using some approaches and providetop-N recommendation for target user using the strategy of average aggregation.
Lastly, we have compared the performance between the proposed approaches andothers to demonstrate the effectiveness of the proposed approaches. Also, we propose thefurther research work.
引文
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