TiM4Rec: An Efficient Sequential Recommendation Model Based on Time-Aware Structured State Space Duality Model

Sequential recommendation represents a pivotal branch of recommendation systems, centered around dynamically analyzing the sequential dependencies between user preferences and their interactive behaviors. Despite the Transformer architecture-based models achieving commendable performance within this domain, their quadratic computational complexity relative to the sequence dimension impedes efficient modeling. In response, the innovative Mamba architecture, characterized by linear computational complexity, has emerged. Mamba4Rec further pioneers the application of Mamba in sequential recommendation. Nonetheless, Mamba 1's hardware-aware algorithm struggles to efficiently leverage modern matrix computational units, which lead to the proposal of the improved State Space Duality (SSD), also known as Mamba 2. While the SSD4Rec successfully adapts the SSD architecture for sequential recommendation, showing promising results in high-dimensional contexts, it suffers significant performance drops in low-dimensional scenarios crucial for pure ID sequential recommendation tasks. Addressing this challenge, we propose a novel sequential recommendation backbone model, TiM4Rec, which ameliorates the low-dimensional performance loss of the SSD architecture while preserving its computational efficiency. Drawing inspiration from TiSASRec, we develop a time-aware enhancement method tailored for the linear computation demands of the SSD architecture, thereby enhancing its adaptability and achieving state-of-the-art (SOTA) performance in both low and high-dimensional modeling. The code for our model is publicly accessible at https://github.com/AlwaysFHao/TiM4Rec.

A Novel Knowledge-Based Genetic Algorithm for Robot Path Planning in Complex Environments

In this paper, a novel knowledge-based genetic algorithm for path planning of a mobile robot in unstructured complex environments is proposed, where five problem-specific operators are developed for efficient robot path planning. The proposed genetic algorithm incorporates the domain knowledge of robot path planning into its specialized operators, some of which also combine a local search technique. A unique and simple representation of the robot path is proposed and a simple but effective path evaluation method is developed, where the collisions can be accurately detected and the quality of a robot path is well reflected. The proposed algorithm is capable of finding a near-optimal robot path in both static and dynamic complex environments. The effectiveness and efficiency of the proposed algorithm are demonstrated by simulation studies. The irreplaceable role of the specialized genetic operators in the proposed genetic algorithm for solving the robot path planning problem is demonstrated through a comparison study.