Abstract: The traditional category-driven approaches only consider the association between categories or organize them into flat or hierarchical structure, but the relationships between items and categories are complex, making other information be ignored. Aiming at this problem, a random walk recommendation algorithm based on combinational category space was proposed to better organize the category information of items and alleviate data sparsity. Firstly, a combinational category space of items represented by Hasse diagrams was constructed to map the one-to-many relationship between items and categories into one-to-one simple relationships, and represent the user's jumps between items in higher and lower levels, the same level and the cross-levels. Then the semantic relationships and two types of semantic distances - the links and the preferences were defined to better describe the changes of the user's dynamic preferences qualitatively and quantitatively. Afterwards,the user personalized category preference model was constructed based on random walking and combination of the semantic relationship, semantic distance, user behavior jumping, jumping times, time sequence and scores of the user's browsing graph in the combinatorial category space. Finally, the items were recommended to users by collaborative filtering based on the user's personalized category preference. Experimental results on MovieLens dataset show that compared with User-based Collaborative Filtering (UCF) model and category-based recommendation models (UBGC and GENC), the recommended F1-score was improved by 6 to 9 percentage points, the Mean Absolute Error (MAE) was reduced by 20% to 30%; compared with Category Hierarchy Latent Factor (CHLF) model, the recommended F1-score was improved by 10%. Therefore, the proposed algorithm has advantage in ranking recommendation and is superior to other category-based recommendation algorithms.

Key words: preference similarity, gradient descent, random walk, collaborative filtering, recommendation algorithm

摘要： 传统的类别驱动方法只考虑类别间的关联或是将其组织成扁平或层次结构，而项目和类别对应关系复杂，其他信息容易被忽略。针对这个问题提出基于组合类别空间的随机游走推荐算法，更好地组织了项目类别信息、缓解了数据稀疏。首先，建立一个用哈斯图表示的项目组合类别空间，将项目和类别复杂的一对多关系映射成一对一的简单关系，并表示用户上下层次、同层次及跨层次的项目类别间的跳转；接着，定义组合类别空间的语义关系及链接、偏好两种语义距离，更好地定性、定量描述用户动态偏好的变化；然后，结合组合类别空间上用户浏览图的语义关系、语义距离、用户行为跳转、跳转次数、时序、评分等各种信息，利用随机游走建立用户个性化类别偏好模型；最后，根据用户个性化偏好完成基于用户的协同过滤项目推荐。在MovieLens数据集上的实验显示，与基于用户的协同过滤（UCF）、基于类别关联的推荐模型（UBGC和GENC）相比，所提算法推荐的F1-score提高了6~9个百分点，平均绝对误差（MAE）减小了20%~30%；与基于类别层次潜在因子模型（CHLF）相比，所提算法推荐的F1-score提高了10%。实验结果表明，所提算法在排序推荐上优于传统基于类别的推荐算法。

关键词: 偏好相似度, 梯度下降, 随机游走, 协同过滤, 推荐算法