In this paper, a land mobile satellite (LMS) multiple-input multiple-output (MIMO) is considered, where two satellites simultaneously communicate with a mobile user terminal (UT). Spatial degree of freedom brought by the two satellites is introduced in the channel modeling, aside of other channel parameters including time correlation, shadowing, multipath fading and Doppler effect. Then an algorithm table using Markov multiple-state transition is provided to generate the LMS MIMO channels. Based on the modeled LMS MIMO channels, signal transmission between two satellites and the UT using space-time block coding is considered. Simulation results show that compared to the single satellite communications, the dual-satellite MIMO communications can achieve better bit error rate performance under the same signal-to-noise-ratio condition. In particular, the performance of dual-satellite single-polarization communications is slightly worse than that of single-satellite dual-polarization communications, since the spatial correlation is stronger than the polarization correlation.

Personalized tag recommender systems suggest a list of tags to a user when he or she wants to annotate an item. They utilize users’ preferences and the features of items. Tensorfactorization techniques have been widely used in tag recommendation. Given the user-item pair, although the classic PITF (Pairwise Interaction Tensor Factorization) explicitly models the pairwise interactions among users, items and tags, it overlooks users’ short-term interests and suffers from data sparsity. On the other hand, given the user-item-time triple, time-aware approaches like BLL (Base-Level Learning) utilize the time effect to capture the temporal dynamics and the most popular tags on items to handle cold start situation of new users. However, it works only on individual level and the target resource level, which cannot find users’ potential interests. In this paper, we propose an unified tag recommendation approach by considering both time awareness and personalization aspects, which extends PITF by adding weightsto user-tag interaction and item-tag interaction respectively. Compared to PITF, our proposed model can depict temporal factor by temporal weights and relieve data sparsity problem by referencing the most popular tags on items. Further, our model brings collaborative filtering (CF) to time-aware models, which can mine information from global data and help improving the ability of recommending new tags. Different from the power-form functions used in the existing time aware recommendation models, we use the Hawkes process with the exponential intensity function to improve the model’s efficiency. The experimental results show that our proposed model outperforms the state of the art tag recommendation methods in accuracy and has better ability to recommend new tags.

Item recommendation helps people to discover their potentially interested items among large numbers of items. One most common application is to recommend top-n items on implicit feedback datasets (e.g., listening history, watching history or visiting history). In this paper, we assume that the implicit feedback matrix has local property, where the original matrix is not globally low rank but some sub-matrices are low rank. In this paper, we propose Local Weighted Matrix Factorization (LWMF) for top-n recommendation by employing the kernel function to intensify local property and the weight function to model user preferences. The problem of sparsity can also be relieved by sub-matrix factorization in LWMF, since the density of sub-matrices is much higher than the original matrix. We propose a heuristic method to select sub-matrices which approximate the original matrix well. The greedy algorithm has approximation guarantee of factor $$1-\frac{1}{e}$$ to get a near-optimal solution. The experimental results on two real datasets show that the recommendation precision and recall of LWMF are both improved about 30% comparing with the best case of weighted matrix factorization (WMF).