融合多种上下文的协同过滤推荐算法研究
发布时间：2018-10-09 13:48
【摘要】：现在我们处在信息急速爆炸的时代,这时候很难做到为用户提供符合心意的有用信息。因为搜索引擎的出现,用户减少了部分信息过载压力,但存在结果单一性问题,无法提供差异性的可以满足用户偏好的服务。具体的,推荐系统通过探究其核心的相关信息,即用户的行为、偏好和环境上下文等因素,筛选掉与用户喜好无关的信息,从而为用户推荐满足个性化需求的服务。协同过滤是目前众多推荐方法中应用范围最广的。它的基本思想是挖掘用户行为背后信息,筛选到相似用户,依据相似用户对某一具体资源的偏好来推断目标用户对具体资源的喜好程度,依照其值顺序推荐。实践证明,此算法可提高电子商务领域中用户由网页浏览者到物品选购者的转化率。尽管协同过滤算法取得了不错的成绩,但传统协同过滤算法仅通过单一评分来挖掘相似用户(物品),推荐的效果并不占优势。不少学者将时间、地点、标签等上下文信息融合到协同过滤推荐算法中,以期提高个性化推荐的质量。通过大量的与协同过滤算法相关的文献阅读、资料总结与内容讨论,本文在经典的算法基础上进行了创新和改进,大量的模拟实验结果证明了新算法的可行性与优越性。具体的工作总结为以下几部分:(1)将时间上下文信息加入到协同过滤推荐算法中。利用用户先后购买同一物品的时间关系来衡量用户间相似度,得到用户特征向量;利用物品先后被同一用户购买的时间关系来衡量物品间相似度,可以计算得到该物品的特征向量;最后,将前面得到的特征向量融合到概率矩阵分解模型中并不断的对其进行优化来降低误差。(2)将标签上下文信息加入到协同过滤推荐算法中。利用标签信息来丰富用户(物品)信息,提出了一种基于用户(物品)标签特征向量的建模方法。通过用户-标签、物品-标签二部图求出用户间相似度和物品间的相似度。将用户评分的时间上下文因素考虑进来,对最近邻模型进行优化,动态发现对当前用户(物品)影响最大的邻居集合。(3)提出一种融合时间上下文和标签上下文的协同过滤推荐算法。通过时间上下文来计算用户相似度,通过标签上下文来计算物品相似度,最后融合到矩阵分解模型中。(4)提出融合多种上下文的推荐系统框架,并给出上下文数据采集、用户兴趣偏好提取,上下文感知推荐生成的具体方法。
[Abstract]:Now we are in the era of information explosion, it is difficult to provide users with the right useful information. Because of the emergence of search engines, users reduce the pressure of partial information overload, but there is a problem of single results, which can not provide different services that can meet users' preferences. Specifically, the recommendation system through exploring its core information, that is, user behavior, preferences and environmental context and other factors, screening out information independent of user preferences, so as to recommend users to meet the needs of personalized services. Collaborative filtering is the most widely used recommendation method. Its basic idea is to mine the information behind the user's behavior, filter out the similar user, infer the target user's preference for a specific resource according to the preference of the similar user to a specific resource, and recommend it according to its value order. It is proved that this algorithm can improve the conversion rate from page viewer to item buyer in the field of electronic commerce. Although the collaborative filtering algorithm has achieved good results, the traditional collaborative filtering algorithm only uses a single score to mine similar users (items), and the recommended results are not dominant. In order to improve the quality of personalized recommendation, many scholars fuse the contextual information such as time, place and label into collaborative filtering recommendation algorithm. By reading a lot of literatures, summarizing the data and discussing the contents of the collaborative filtering algorithm, this paper innovates and improves on the basis of the classical algorithm, and a large number of simulation results prove the feasibility and superiority of the new algorithm. The specific work is summarized as follows: (1) time context information is added to collaborative filtering recommendation algorithm. The similarity between users is measured by the time relationship between the same items, and the user feature vector is obtained, and the similarity between the items is measured by the time relationship between the items purchased by the same user. The eigenvector of the item can be calculated. Finally, the former eigenvector is fused into the probabilistic matrix decomposition model and optimized continuously to reduce the error. (2) the label context information is added to the collaborative filtering recommendation algorithm. This paper presents a modeling method based on user (item) label feature vector to enrich user (object) information by label information. The similarity between user and item is obtained by user-label, item-label two-part graph. Taking into account the time context of the user rating, the nearest neighbor model is optimized. (3) A collaborative filtering recommendation algorithm combining time context and label context is proposed. The similarity of users is calculated by time context, and the similarity of items is calculated by label context. Finally, it is fused into matrix decomposition model. (4) the framework of recommendation system is proposed, and the context data collection is given. User interest preference extraction, context-aware recommendation generation of specific methods.
【学位授予单位】：山东师范大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TP391.3
，
本文编号：2259565
[Abstract]:Now we are in the era of information explosion, it is difficult to provide users with the right useful information. Because of the emergence of search engines, users reduce the pressure of partial information overload, but there is a problem of single results, which can not provide different services that can meet users' preferences. Specifically, the recommendation system through exploring its core information, that is, user behavior, preferences and environmental context and other factors, screening out information independent of user preferences, so as to recommend users to meet the needs of personalized services. Collaborative filtering is the most widely used recommendation method. Its basic idea is to mine the information behind the user's behavior, filter out the similar user, infer the target user's preference for a specific resource according to the preference of the similar user to a specific resource, and recommend it according to its value order. It is proved that this algorithm can improve the conversion rate from page viewer to item buyer in the field of electronic commerce. Although the collaborative filtering algorithm has achieved good results, the traditional collaborative filtering algorithm only uses a single score to mine similar users (items), and the recommended results are not dominant. In order to improve the quality of personalized recommendation, many scholars fuse the contextual information such as time, place and label into collaborative filtering recommendation algorithm. By reading a lot of literatures, summarizing the data and discussing the contents of the collaborative filtering algorithm, this paper innovates and improves on the basis of the classical algorithm, and a large number of simulation results prove the feasibility and superiority of the new algorithm. The specific work is summarized as follows: (1) time context information is added to collaborative filtering recommendation algorithm. The similarity between users is measured by the time relationship between the same items, and the user feature vector is obtained, and the similarity between the items is measured by the time relationship between the items purchased by the same user. The eigenvector of the item can be calculated. Finally, the former eigenvector is fused into the probabilistic matrix decomposition model and optimized continuously to reduce the error. (2) the label context information is added to the collaborative filtering recommendation algorithm. This paper presents a modeling method based on user (item) label feature vector to enrich user (object) information by label information. The similarity between user and item is obtained by user-label, item-label two-part graph. Taking into account the time context of the user rating, the nearest neighbor model is optimized. (3) A collaborative filtering recommendation algorithm combining time context and label context is proposed. The similarity of users is calculated by time context, and the similarity of items is calculated by label context. Finally, it is fused into matrix decomposition model. (4) the framework of recommendation system is proposed, and the context data collection is given. User interest preference extraction, context-aware recommendation generation of specific methods.
【学位授予单位】：山东师范大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TP391.3
，
本文编号：2259565
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