近日，博士生张晓堃关于会话推荐(session-based recommendation)的研究成果被Information Processing & Management (IPM) 期刊录用，其为中科院一区期刊，CCF推荐B类期刊。

  题目：Dynamic Intent-aware Iterative Denoising Network for Session-based Recommendation (动态意图感知及智能降噪的会话推荐系统)

  摘要：Session-based recommendation aims to predict items that a user will interact with based on historical behaviors in anonymous sessions. It has long faced two challenges: (1) the dynamic change of user intents which makes user preferences towards items change over time; (2) the uncertainty of user behaviors which adds noise to hinder precise preference learning. They jointly preclude recommender system from capturing real intents of users. Existing methods have not properly solved these problems since they either ignore many useful factors like the temporal information when building item embeddings, or do not explicitly filter out noisy clicks in sessions. To tackle above issues, we propose a novel Dynamic Intent-aware Iterative Denoising Network (DIDN) for session-based recommendation. Specifically, to model the dynamic intents of users, we present a dynamic intent-aware module that incorporates item-aware, user-aware and temporal-aware information to learn dynamic item embeddings. A novel iterative denoising module is then devised to explicitly filter out noisy clicks within a session. In addition, we mine collaborative information to further enrich the session semantics. Extensive experimental results on three real-world datasets demonstrate the effectiveness of the proposed DIDN. Specifically, DIDN obtains improvements over the best baselines by 1.66%, 1.75%, and 7.76% in terms of P@20 and 1.70%, 2.20%, and 10.48% in terms of MRR@20 on all datasets.

  会话推荐系统致力于从匿名的用户行为序列中捕获用户偏好，进而预测用户将来的行为。长期以来，会话推荐系统面临着两个挑战：一是用户意图的动态变化，这使得用户对于物品的偏好随着时间不断变化；二是用户行为的不确定性，这使得用户的行为存在着噪声，导致模型难以学习用户的真正意图。现有的方法在构建商品表示的时候忽略了重要的时序信息，而且没有显式地过滤掉会话中的噪声，导致其无法建模用户的动态意图，也不能过滤掉会话中的噪声。因此，我们提出了一个新的模型DIDN。在DIDN中，我们首先提出了一个动态意图感知模块来建模用户的动态意图，该模块引入了物品，用户及时序信息来构建商品的动态表示。然后，我们设计了一个迭代降噪方法去显式地删除会话中的噪声。最后，我们利用协同信息来丰富会话表示的语义信息，以进一步消除噪声对模型的影响。我们在三个真实的数据集上进行了大量的实验。实验表明，我们提出的模型DIDN在两个常用的评价指标上均优于最先进的模型。