在当今大数据时代下，信息过载问题日益严重。会话序列推荐系统是缓解该问题的有效工具之一，能通过用户与物品交互的行为序列来捕获用户的兴趣偏好，并为用户推荐合适的物品。而由于会话序列数据存在匿名性及信息缺失等问题，如何充分利用有限的会话序列数据来提高推荐准确率已成为会话序列推荐的重要研究任务。

针对已有结合自注意力的会话推荐方法存在无法准确衡量物品重要性及自注意力计算复杂度高的问题，提出一种多兴趣感知的自适应自注意力网络会话推荐模型。模型设计了一个自适应自注意力网络，通过引入一类高稀疏性的计算函数替换softmax函数，提高了输出物品重要性分数的稀疏性；同时考虑到每条序列的差异性，设计了一个序列自适应因子模块，使每条序列能基于上下文动态地选择合适的计算函数，从而加强了模型能准确衡量物品重要性的能力。针对自注意力计算复杂度高的问题，提出一个兴趣聚合层去捕获常数个用户兴趣，从而通过将物品两两之间的关联性转换成计算物品与常数个兴趣之间的关联性，降低了计算复杂度。此外，模型采用了一种解耦位置编码方案降低物品与位置信息的编码耦合度，避免引入噪声信息。最后，为了充分考虑用户特征，设计了用户总体偏好模块以捕获用户的偏好。在多个公开数据集上的实验结果表明所提模型的有效性及可行性。与基线方法相比，所提模型在HR@10和NDCG@10上分别获得了6.54%和5.71%的最大增益百分比。

在电商场景中，针对应用图神经网络的会话推荐方法采用信息有损的会话图构建方式，以及未能充分考虑特征建模的影响因素等问题，提出一种价格感知的信息无损会话推荐模型。模型采用信息无损的会话图构建方式以便充分编码会话信息，降低构图时的信息损失。为了探索影响用户偏好的新因素，设计了一个价格容忍因子模块，建模用户对各类物品的价格容忍度。此外，提出了一种新的用户意图编码方案，从物品类别层面捕获用户意图，以提高对用户意图捕获的准确性。最后，为了全面考虑用户特征的建模，设计了一个动态兴趣模块去捕获用户随时间演化的动态兴趣。实验结果表明，所提模型与多个代表性基线相比整体上取得较好的性能，在HR@20和MRR@20上分别获得了1.52%和1.58%的最大增益百分比。

In today's big data era, the problem of information overload is becoming more and more serious. Session sequence recommendation system is one of the effective tools to alleviate this problem. It captures users' interests and preferences through the interaction sequence between users and items, and recommends appropriate items for them. However, due to the anonymity and lack of information in session sequence data, how to make full use of limited session sequence data to improve the accuracy of recommendation has become an important research task of session sequence recommendation.

Aiming at the problems of the high computational complexity of self-attention and unable to accurately measure the importance of items in the existing session-based recommendation methods combined with self-attention, a multi-interest aware adaptive self-attention network model is proposed for session-based recommendation. In the model, an adaptive self-attention network is designed to improve the sparsity of the output item importance score by introducing a class of highly sparse computing functions to replace the softmax function. At the same time, considering the differences of each sequence, a sequence adaptive factor module is designed to enable each sequence to dynamically select the appropriate computing function based on the context, thus enhancing the ability of the model to accurately measure the importance of items. To solve the problem of the high computational complexity of self-attention, an interest aggregation layer is proposed to capture constant user's interests, which reduces the computational complexity by converting the correlation between items into the correlation between items and constant interests. In addition, the model adopts a decoupled position coding scheme to reduce the coding coupling between items and position information, to avoid the introduction of noise information. Finally, to fully consider the user characteristics, the user global preference module is designed to capture the user preferences. Experimental results on several public datasets show the effectiveness and feasibility of the proposed model. Compared to the baseline methods, the maximum gain percentage of the proposed model is 6.54% on HR@10 and 5.71% on NDCG@10, respectively.

Aiming at the problems that the session-based recommendation method combined with graph neural network in e-commerce scene adopts the construction method of session graph with information loss, and fails to fully consider the influencing factors of feature modeling, a price-aware information lossless model is proposed for session-based recommendation. The model adopts a method of constructing a session graph with information lossless to fully encode the session information and reduce the information loss during construction. To explore the new factors that affect users' preferences, a price tolerance factor module is designed to model users' price tolerance for various items. In addition, a new user intention coding scheme is proposed to capture user intention from the item category level to improve the accuracy of capturing user intention. Finally, to fully consider the modeling of user characteristics, a dynamic interest module is designed to capture the dynamic interest of users over time. Experimental results show that the proposed model achieves better performance compared with multiple representative baselines. The maximum gain percentage of the proposed model is 1.52% on HR@20 and 1.58% on MRR@20, respectively.

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