【摘要】 根据在线社区中群体的历史行为进行物品(或信息)推荐是当前研究热点之一,传统推荐算法都面临数据稀疏性问题的挑战.针对传统推荐算法知识表示的局限性进行了研究,提出了一种基于标签系统的用户行为知识表示法,把用户在物品上历史行为的统计,转化为对用户在物品标签上的统计,从而缓解数据稀疏的情况.为了降低标签维度过高导致的计算复杂性问题,提出了采用因子分析法,抽取出潜在重要且稳定的特征因子向量来最终表示用户的历史行为,并据此度量用户行为在特征因子向量上的相似性.最后采用协同过滤的思想给出了一种新的协同推荐方法.通过在真实数据集上的大量对比实验,表明该方法在处理具有稀疏性的数据集时,总是能保持更高且更稳定的推荐准确率.更多还原

【Abstract】 Item( or information) recommendation is one of hot research topics currently. However the issue of sparseness in dataset challenges all traditional recommendation algorithms. Limitations of knowledge representation in traditional recommendation algorithms were studied. The tag-system-based knowledge to represent information of each user’s behavior was proposed. That it the account on user’s behavior on items is transferred to an account on a user’s behavior on tags. To decrease the computation complexity on high dimensional tag-based datasets,a factor analysis method was taken to extract those most important latent factors to represent users’ behaviors. Based on each user’s representing vector of latent factors,a new way was given to compute similarities among users. By incorporating this similarity measure,a new collaborative recommendation method with low sensitivity to sparseness was built to meet the need of practical and dynamic datasets. Experiments were carried on real-world datasets to compare the proposed method with other state-of-the-art collaborative filtering and matrix factorization based recommendation methods. It is shown the proposed method can achieve better prediction accuracy while keeps a lower sensitivity to sparseness.更多还原