Multi-modal contents have proven to be the powerful knowledge for recommendation tasks. Most state-of-the-art multi-modal recommendation methods mainly focus on aligning the semantic spaces of different modalities to enhance the item representations and do not pay much attention on the relevant knowledge in the multi-modalities for recommendation, resulting in that the positive effects of the relevant knowledge is reduced and the improvement of recommendation performance is limited. In this paper, we propose a multi-modal correction network termed MMCN to enhance the item representation with the important semantic knowledge in each modality by a residual structure with attention mechanisms and a hierarchical contrastive learning framework. The residual information is obtained through self-attention and cross-attention, which can learn the relevant knowledge across different modalities effectively. While hierarchical contrastive learning further captures the relevant knowledge not only at the feature level but also at the element-wise level with a matrix. Extensive experiments on three large-scale real-world datasets show the superiority of MMCN over state-of-the-art multi-modal recommendation methods.

In recent years, uplift modeling, also known as individual treatment effect (ITE) estimation, has seen wide applications in online marketing, such as delivering one-time issuance of coupons or discounts to motivate users' purchases. However, complex yet more realistic scenarios involving multiple interventions over time on users are still rarely explored. The challenges include handling the bias from time-varying confounders, determining optimal treatment timing, and selecting among numerous treatments. In this paper, to tackle the aforementioned challenges, we present a temporal point process-based uplift model (TPPUM) that utilizes users' temporal event sequences to estimate treatment effects via counterfactual analysis and temporal point processes. In this model, marketing actions are considered as treatments, user purchases as outcome events, and how treatments alter the future conditional intensity function of generating outcome events as the uplift. Empirical evaluations demonstrate that our method outperforms existing baselines on both real-world and synthetic datasets. In the online experiment conducted in a discounted bundle recommendation scenario involving an average of 3 to 4 interventions per day and hundreds of treatment candidates, we demonstrate how our model outperforms current state-of-the-art methods in selecting the appropriate treatment and timing of treatment, resulting in a 3.6% increase in application-level revenue.

CTR (Click-Through Rate) prediction plays an essential role in online advertising systems. Most existing works attempt to capture users’ interests from sessions by assuming that behaviors within a session are homogeneous. However, user interest may change frequently. Thus it is hard to guarantee that behaviors in a session are homogeneous, resulting in users’ interests extracted from sessions being biased. In this paper, we propose a model named Deep Session Heterogeneity-aware Network (DSHN) by learning the relationships of behaviors within sessions and the relevance between the session and target item to alleviate the influence of irrelevant or heterogeneous sessions. We design a heterogeneity-aware mechanism to learn the heterogeneity of items within a session. Then we further design two modules: the Session Heterogeneity Learning module and the Relevance Inference module. The Session Heterogeneity Learning module weighs each session by summarizing the variation of session interest with and without any behavior. The relevance Inference module learns the relevance between the target item and each session in a similar way by learning session interest with and without the target item. Extensive experiments on four datasets demonstrate that our proposed DSHN achieves better results compared to the state-of-the-art.

Click-through rate (CTR) prediction is the core task in an online advertising system. How to capture the users’ dynamic interests through the behavior sequences to predict CTR is a challenging problem in the real-world applications. To address this challenge, we propose a deep dynamic interest learning by learning the local sessions and global sessions within sequences for CTR prediction. The local sessions and global sessions are used to capture the short-term dynamic interests and the long-term interests of users, respectively. To explore the heterogeneous behaviors within the global session efficiently, the interests in global sessions are forced to be consistent with that in local sessions. Then, the bi-LSTM network is adopted to capture the dynamic interests with the behaviors across global sessions effectively. Experimental results on various datasets show that the proposed method outperforms several state-of-the-art CTR prediction methods.