As recommender systems become pervasive in various scenarios, cross-domain recommenders (CDR) are proposed to enhance the performance of one target domain with data from other related source domains. However, irrelevant information from the source domain may instead degrade target domain performance, which is known as the negative transfer problem. Most existing efforts to tackle this issue primarily focus on designing adaptive representations for overlapped users. Whereas, these methods rely on the learned representations of the model, lacking explicit constraints to filter irrelevant source-domain collaborative information for the target domain, which limits their cross-domain transfer capability.

Given a user’s historical interaction sequence, online novel recommendation suggests the next novel the user may be interested in. Online novel recommendation is important but underexplored. In this paper, we concentrate on recommending online novels to new users of an online novel reading platform, whose first visits to the platform occurred in the last seven days. We have two observations about online novel recommendation for new users. First, repeat novel consumption of new users is a common phenomenon. Second, interactions between users and novels are informative. To accurately predict whether a user will reconsume a novel, it is crucial to characterize each interaction at a fine-grained level. Based on these two observations, we propose a neural network for online novel recommendation, called NovelNet. NovelNet can recommend the next novel from both the user’s consumed novels and new novels simultaneously. Specifically, an interaction encoder is used to obtain accurate interaction representation considering fine-grained attributes of interaction, and a pointer network with a pointwise loss is incorporated into NovelNet to recommend previously-consumed novels. Moreover, an online novel recommendation dataset is built from a well-known online novel reading platform and is released for public use as a benchmark. Experimental results on the dataset demonstrate the effectiveness of NovelNet 1.

Within online platforms, it is critical to capture the semantics of sequential user behaviors for accurately modeling user interests. However, dynamic characteristics and sparse behaviors make it difficult to train effective user representations for sequential user behavior modeling.

In industry, customer lifetime value (LTV) prediction is a challenging task, since user consumption data is usually volatile, noisy, or sparse. To address these issues, this paper presents a novel Temporal-Structural User Representation (named TSUR) network to predict LTV. We utilize historical revenue time series and user attributes to learn both temporal and structural user representations, respectively. Specifically, the temporal representation is learned with a temporal trend encoder based on a novel multi-channel Discrete Wavelet Transform~(DWT) module, while the structural representation is derived with Graph Attention Network (GAT) on an attribute similarity graph. Furthermore, a novel cluster-alignment regularization method is employed to align and enhance these two kinds of representations. In essence, such a fusion way can be considered as the association of temporal and structural representations in the low-pass representation space, which is also useful to prevent the data noise from being transferred across different views. To our knowledge, it is the first time that temporal and structural user representations are jointly learned for LTV prediction. Extensive offline experiments on two large-scale real-world datasets and online A/B tests have shown the superiority of our approach over a number of competitive baselines.