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.

The actual relationship between users cannot be captured by the existing data imputation methods because they only consider the rating information and traditional similarity. To alleviate data sparsity and improve recommendation accuracy, a data imputation method was proposed. Firstly, the defects of traditional similarity were analyzed and a new session-based temporal similarity based on tempoaral similarity and dissimilarity was defined, which integrated time context into rating patterns to better identify neighbors for active user. Additionally, the rating sub-matrix of key item set was extracted from similar users and their consumption items which can mine the potential influence factors of users and items, and it was imputed by using matrix factorization. Then the user probabilistic topic distribution for each stage was obtained by using Latent Dirichlet Allocation (LDA) and the user dynamic profile was built with the temporal penalty weights. Finally, the items were recommended based on the correlation of probabilistic topic distribution between users and user-based collaborative filtering. Experimental results show that compared with other imputation-based methods, the proposed method can reduce the Mean Absolute Error (MAE) and improve the recommendation performance under different sparsity.

In session-based Collaborative Filtering (CF), a user interaction history is divided into sessions using fixed time window and user preference is expressed by sequences of them.But in tourism data, there is no interaction in some sessions and it is difficult to select neighbors because of high sparsity. To alleviate data sparsity and better use the characteristics of the tourism data, a new tourism route recommendation method based on dynamic clustering was proposed. Firstly, the different characteristics of tourism data and other standard data were analyzed. Secondly, a user interaction history was divided into sessions by variable time window using dynamic clustering and user preference model was built by combining probabilistic topic distribution obtained by Latent Dirichlet Allocation (LDA) from each session and time penalty weights. Then, the set of neighbors and candidate routes were obtained through the feature vector of users, which reflected the characteristics of tourist age, route season and price. Finally, routes were recommended according to the relevance of probabilistic topic distribution between candidate routes and tourists. It not only alleviates data sparsity by using variable time window, but also generates the optimal number of time windows which is automatically obtained from data. User feature vector was used instead of similarity of tourism data to select neighbors, so as to the avoid the computational difficulty caused by data sparsity. The experimental results on real tourism data indicate that the proposed method not only adapts to the characteristics of tourism data, but also improves the recommendation accuracy.