基于类别转移加权张量分解模型的兴趣点分区推荐

基于位置社交网络的兴趣点(POI)推荐是人们发现有趣位置的重要途径,然而,现实中用户在不同区域的地点偏好侧重的差异,加之高维度的历史签到信息,使得精准而又个性化的POI推荐极富挑战性.对此,该文提出一种新型的基于类别转移加权张量分解模型的兴趣点分区推荐算法(WTD-PR).通过结合用户连续行为和时间特征,来充分利用用户...

A Recommendation Method for Point-of-Interest Partition Based on Category Transfer Weighted Tensor Decomposition Model

Point-Of-Interest (POI) recommendation in location-based social networks is an important way for people to find interesting locations. However, in reality, both the various user preference of locations in different regions and the high-dimensional historical check-in information make accurate and personalized POI recommendations extremely challenging. In this regard, a new type of recommendation algorithm for point-of-interest Partition Recommendation based on a category transfer Weighted Tensor Decomposition (WTD-PR) model is proposed. The proposed algorithm makes full use of the user’s historical visit information by combining the user’s continuous behavior and time characteristics to obtain the category transfer weight factor; Then, by improving the user-time-category tensor model and adding the category transfer weight to the tensor to predict the user’s preference category; Finally, the local and remote locations are divided according to the user’s historical access area, and the recommended areas are found based on the user’s current location. After that, location and social factors are introduced and combined with the candidate categories to make the recommendation of points of interest. Through comparative experiments on real data sets, the proposed algorithm is proved not only to be universal, but also superior to other comparison algorithms in terms of recommendation performance.