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Session-based recommendation aims to predict the next interacted item based on the anonymous user's behavior sequence. The main challenge lies in how to perceive user preference within limited interactions. Recent advances demonstrate the advantage of utilizing intent represented by combining consecutive items in understanding complex user behavior. However, these methods concentrate on the diverse expression of intents enriched by considering consecutive items with different lengths, ignoring the exploration of complex transitions between intents. This limitation makes intent transfer unclear in the user behavior with dynamic change, resulting in sub-optimal performance. To solve this problem, we propose novel collaborative association networks with cross-level attention for session-based recommendation (denoted as CAN4Rec), which simultaneously models intra- and inter-level transitions within hierarchical user intents. Specifically, we first construct two levels of intent, including individual-level and aggregated-level intent, and each level of intent is obtained based on sequential transitions. Then, the cross-level attention mechanism is designed to extract inter-transitions between different levels of intent. The captured inter-transitions are bi-directional, containing from individual-level to aggregated-level intents and from aggregated-level to individual-level intents. Finally, we generate directional session representations and combine them to realize the prediction of the next item. Experimental results on three public benchmark datasets demonstrate that the proposed model achieves state-of-the-art performance. • The complex transitions from hierarchical user intents in related works are ignored. • It leads to the inadequate handling of user behavior sequences with longer lengths. • We solve it based on exploring intra- and inter-transitions in hierarchical intents. • Experiment results show the superiority of our proposed model over three datasets. [ABSTRACT FROM AUTHOR]

A session-based recommender system (SBRS) captures users' evolving behaviors and recommends the next item by profiling users in terms of items in a session. User intent and user preference are two factors affecting his (her) decisions. Specifically, the former narrows the selection scope to some item types, while the latter helps to compare items of the same type. Most SBRSs assume one arbitrary user intent dominates a session when making a recommendation. However, this oversimplifies the reality that a session may involve multiple types of items conforming to different intents. In current SBRSs, items conforming to different user intents have cross-interference in profiling users for whom only one user intent is considered. Explicitly identifying and differentiating items conforming to various user intents can address this issue and model rich contextual information of a session. To this end, we design a framework modeling user intent and preference explicitly, which empowers the two factors to play their distinctive roles. Accordingly, we propose a key-array memory network (KA-MemNN) with a hierarchical intent tree to model coarse-to-fine user intents. The two-layer weighting unit (TLWU) in KA-MemNN detects user intents and generates intent-specific user profiles. Furthermore, the hierarchical semantic component (HSC) integrates multiple sets of intent-specific user profiles along with different user intent distributions to model a multi-intent user profile. The experimental results on real-world datasets demonstrate the superiority of KA-MemNN over selected state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In various scenarios, the motion of a tracked object, for example, a pointing apparatus, pedestrian, animal, vehicle, and others, is driven by achieving a premeditated goal such as reaching a destination. This is albeit the various possible trajectories to this endpoint. This paper presents a generic Bayesian framework that utilizes stochastic models that can capture the influence of intent (viz., destination) on the object behavior. It leads to simple algorithms to infer, as early as possible, the intended endpoint from noisy sensory observations, with relatively low computational and training data requirements. This framework is introduced in the context of the novel predictive touch technology for intelligent user interfaces and touchless interactions. It can determine, early in the interaction task or pointing gesture, the interface item the user intends to select on the display (e.g., touchscreen) and accordingly simplify as well as expedite the selection task. This is shown to significantly improve the usability of displays in vehicles, especially under the influence of perturbations due to road and driving conditions, and enable intuitive contact-free interactions. Data collected in instrumented vehicles are shown to demonstrate the effectiveness of the proposed intent prediction approach.