基于商品属性的电子商务推荐系统研究
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摘要
推荐系统是解决信息过载问题的有效手段，已经引起了企业界和学术界的广泛关注。为了减少稀疏矩阵、冷启动等问题对推荐过程的影响，改善推荐系统的推荐效果和解释能力，本研究在对现有的电子商务推荐系统研究成果分别从信息技术和商务应用的角度进行了梳理和分类的基础上，从信息粒度和信息来源的角度研究了商品的属性知识和用户对商品属性的偏好信息在推荐系统中的作用机理。
论文首先针对用户经常消费的商品提出了基于商品属性的推荐算法和流程。参考信息检索领域的TFIDF算法，提出了在不增加消费者反馈工作量的前提下，将传统的用户商品评分矩阵转化为用户商品属性评分矩阵的方法，并以此评分矩阵为基础提出了基于商品属性效用叠加、基于神经网络和基于属性的协同过滤三种推荐方法。通过对国际互联网上公开的数据集的计算表明本文提出的矩阵转化方法能够在一定程度上提升矩阵中数据元素的密度，提出的三种推荐方法也能在不同程度上解决推荐系统的稀疏矩阵和冷启动的问题。
论文接着针对用户不经常消费的商品提出基于定性的用户购买目标和定量的专家商品领域知识的交互式推荐算法和流程。该交互式推荐算法以手段目标链理论模型为理论依据，以商品属性为中介。论文在理论分析的基础上根据设计科学的研究范式，选用计算机商品为研究对象，设计了包括专家商品领域知识获取和推荐交互功能的原型系统。实验研究结果表明提出的交互式推荐过程能够明显降低用户对选购类商品消费过程的感知复杂程度，显著提高用户消费过程中的决策效率。
论文最后提出在线评论中用户商品属性偏好信息的挖掘方法以及这些信息在商品推荐过程中的作用机理。借鉴现有的自然语言处理技术和数据挖掘方法，给出了在一定的人工参入下，从用户在线评论中挖掘用户商品属性偏好信息、评论权重信息、用户商品评分信息的算法和流程，同时分析了获取的这些信息作为商品领域知识或用户商品偏好模型信息来源应用于推荐过程的不同作用机理。针对用户对计算机商品的在线评论的实验研究结果证明了提出的算法和流程的可行性和有效性。
Recommender system is an efficacious tool that helps the user find interesting itemsand overcome information overload problem. Based on a survey of its up-to-datedevelopment from technical and managerial perspective, this thesis delves down into therecommender process using the knowledge from the users' interest and the attributes of theitems. This research is implemented from the perspective of information granularity andinformation source.
The algorithm and flow of the recommender system for frequent purchased productbased on the attributes of the product is discussed firstly. On the premise of not increasingthe workload of the user, the algorithm that converts the user-item matrix into user-item'sattributes is proposed referring to the TFIDF algorithm in information retrieval research.Using this matrix, three recommendation approaches which are separately summing thepartial utilities of attributes, artificial neural network and attribute-based collaborativefilter are proposed. Experiments are conducted on the public data sets from the Internet.The results show that the proposed algorithm and approaches can increase accuracy ofprediction and help reduce the data sparsity and cold-start problems of recommendersystem.
Conversational recommender system for infrequent purchased product is discussedsecondly, which is based on the users' goals and the items' attributes, and in whichadditional domain expert knowledge is encoded. Based on the means-end model, thisthesis describes a conversational recommender process that supports for users in findingsatisfying shopping products, for which the system lacks of users' history shopping dataand the users lack of product knowledge, according to the users' qualitative goals and thedomain experts' quantitative product knowledge. The domain expert knowledge includesspecific names for the purpose, the functions and the attributes of the products and theirquantitative relation. It is inputted by the domain experts with the help of analyticalhierarchy process. According to the research method in design science, a prototype systemis designed, which can obtain the domain knowledge of computer experts and recommendappropriate computers to the users based on their goals. Experimental results show that the conversational recommender process proposed in this thesis abates the users' perceivedcomplexity and improves their decision efficiency when they choose and buy shoppingproducts.
Finally the algorithm and process of mining user preference for the product attributesfrom the online product reviews are discussed. This algorithm and process make use oftechnologies of natural language processing and data mining and some artificiality. It canobtain three type of information including user preference for the product attributes,weight for the online product reviews and scores for the product attributes. Themechanism of the preference for recommender systems is discussed also. It may be usedto build the user preference model or as a source of product domain knowledge.Experimental results aimed at computers show that there are no significant differencesbetween the scores for products get by the method proposed in this thesis and given by theexperts. This demonstrates the feasibility and effectiveness of the proposed algorithm andprocess.
论文首先针对用户经常消费的商品提出了基于商品属性的推荐算法和流程。参考信息检索领域的TFIDF算法，提出了在不增加消费者反馈工作量的前提下，将传统的用户商品评分矩阵转化为用户商品属性评分矩阵的方法，并以此评分矩阵为基础提出了基于商品属性效用叠加、基于神经网络和基于属性的协同过滤三种推荐方法。通过对国际互联网上公开的数据集的计算表明本文提出的矩阵转化方法能够在一定程度上提升矩阵中数据元素的密度，提出的三种推荐方法也能在不同程度上解决推荐系统的稀疏矩阵和冷启动的问题。
论文接着针对用户不经常消费的商品提出基于定性的用户购买目标和定量的专家商品领域知识的交互式推荐算法和流程。该交互式推荐算法以手段目标链理论模型为理论依据，以商品属性为中介。论文在理论分析的基础上根据设计科学的研究范式，选用计算机商品为研究对象，设计了包括专家商品领域知识获取和推荐交互功能的原型系统。实验研究结果表明提出的交互式推荐过程能够明显降低用户对选购类商品消费过程的感知复杂程度，显著提高用户消费过程中的决策效率。
论文最后提出在线评论中用户商品属性偏好信息的挖掘方法以及这些信息在商品推荐过程中的作用机理。借鉴现有的自然语言处理技术和数据挖掘方法，给出了在一定的人工参入下，从用户在线评论中挖掘用户商品属性偏好信息、评论权重信息、用户商品评分信息的算法和流程，同时分析了获取的这些信息作为商品领域知识或用户商品偏好模型信息来源应用于推荐过程的不同作用机理。针对用户对计算机商品的在线评论的实验研究结果证明了提出的算法和流程的可行性和有效性。
Recommender system is an efficacious tool that helps the user find interesting itemsand overcome information overload problem. Based on a survey of its up-to-datedevelopment from technical and managerial perspective, this thesis delves down into therecommender process using the knowledge from the users' interest and the attributes of theitems. This research is implemented from the perspective of information granularity andinformation source.
The algorithm and flow of the recommender system for frequent purchased productbased on the attributes of the product is discussed firstly. On the premise of not increasingthe workload of the user, the algorithm that converts the user-item matrix into user-item'sattributes is proposed referring to the TFIDF algorithm in information retrieval research.Using this matrix, three recommendation approaches which are separately summing thepartial utilities of attributes, artificial neural network and attribute-based collaborativefilter are proposed. Experiments are conducted on the public data sets from the Internet.The results show that the proposed algorithm and approaches can increase accuracy ofprediction and help reduce the data sparsity and cold-start problems of recommendersystem.
Conversational recommender system for infrequent purchased product is discussedsecondly, which is based on the users' goals and the items' attributes, and in whichadditional domain expert knowledge is encoded. Based on the means-end model, thisthesis describes a conversational recommender process that supports for users in findingsatisfying shopping products, for which the system lacks of users' history shopping dataand the users lack of product knowledge, according to the users' qualitative goals and thedomain experts' quantitative product knowledge. The domain expert knowledge includesspecific names for the purpose, the functions and the attributes of the products and theirquantitative relation. It is inputted by the domain experts with the help of analyticalhierarchy process. According to the research method in design science, a prototype systemis designed, which can obtain the domain knowledge of computer experts and recommendappropriate computers to the users based on their goals. Experimental results show that the conversational recommender process proposed in this thesis abates the users' perceivedcomplexity and improves their decision efficiency when they choose and buy shoppingproducts.
Finally the algorithm and process of mining user preference for the product attributesfrom the online product reviews are discussed. This algorithm and process make use oftechnologies of natural language processing and data mining and some artificiality. It canobtain three type of information including user preference for the product attributes,weight for the online product reviews and scores for the product attributes. Themechanism of the preference for recommender systems is discussed also. It may be usedto build the user preference model or as a source of product domain knowledge.Experimental results aimed at computers show that there are no significant differencesbetween the scores for products get by the method proposed in this thesis and given by theexperts. This demonstrates the feasibility and effectiveness of the proposed algorithm andprocess.
引文
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