Session-based recommendation with quaternion-enhanced attention calculation

A sound session-based Recommendation system is an important thing for users and enterprises. Session data from anonymous users is used to infer subsequent behavior and make high-quality recommendations. Graph neural networks have been extensively used to represent and learn information about the graph structure of session data in many existing studies, which have achieved significant progress. However, certain limitations still exist. This part of the model mainly focuses on how to enhance the expressiveness of the model while ignoring the quality of embedding. And previous studies have shown that complex deep learning models do not always have an edge over relatively simple algorithms in terms of prediction accuracy. To complement this aspect, this paper proposes a model for session recommendation based on quaternion-enhanced attention computation. Distinct from Euclidean space, quaternions perform calculations and analogy deductions in a hyper-complex vector space. The Hamilton product employed by quaternions provides a highly significant and meaningful computational method for enhancing session representation and reducing model parameters. A quaternion weight calculation fusion mechanism was designed, which uses quaternion calculations in key calculation steps, such as calculating relevant learnable weights for item representation and session representation. Experimental results on the benchmark database show that QEAC-SR outperforms some of the existing state-of-the-art methods, with Precision scores improving over the best-performing baseline method by <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mn>1.70</mml:mn> <mml:mi mathvariant="normal">%</mml:mi> </mml:math> to <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mn>1.80</mml:mn> <mml:mi mathvariant="normal">%</mml:mi> </mml:math> on Diginetica and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mn>21.51</mml:mn> <mml:mi mathvariant="normal">%</mml:mi> </mml:math> to <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mn>23.49</mml:mn> <mml:mi mathvariant="normal">%</mml:mi> </mml:math> on Tmall; MRR scores improve over the best-performing baseline method by <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mn>2.31</mml:mn> <mml:mi mathvariant="normal">%</mml:mi> </mml:math> to <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mn>2.36</mml:mn> <mml:mi mathvariant="normal">%</mml:mi> </mml:math> on Diginetica and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mn>19.56</mml:mn> <mml:mi mathvariant="normal">%</mml:mi> </mml:math> to <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mn>19.65</mml:mn> <mml:mi mathvariant="normal">%</mml:mi> </mml:math> on Tmall.