{'fraction_non_alphanumeric': 0.02318840579710145, 'fraction_numerical': 0.0, 'mean_word_length': 5.974747474747475, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Hierarchical Transition-Aware Graph Attention Network for Session-based Recommendation

Recent session-based recommendation research leverages graph neural networks to capture user intents from items transitions. Due to the limited transition information carried by the pairwise relation, the existing methods often face the dilemma of generating inaccurate item representations. To alleviate this issue, the paper proposes a novel hierarchical transition-aware graph attention network, HTA-Graph, with three unique features. First, the multi-relationship graphs that describe transitions with relevant attributes are adopted as the pairwise source. An order-irrelevance hierarchical relation aggregator is proposed to represent the pairwise at four levels of granularity. In addition, a transition-aware interaction network is built to effectively integrate the transition context and the sequence context into a unified session representation. Second, a hierarchical transition attribute attention mechanism is developed to access the pairwise information at different levels of granularity. And last, a fusion strategy is designed to allow the session representation to select valid combinations from three patterns. All experiments exceeded the state-of-the-art results in typical session-based recommendation tasks, showed HTA-Graph's powerful item representation capability.