Divide and Conquer: Towards Better Embedding-based Retrieval for Recommender Systems From a Multi-task Perspective

Embedding-based retrieval (EBR) methods are widely used in modern recommender systems thanks to its simplicity and effectiveness. However, along the journey of deploying and iterating on EBR in production, we still identify some fundamental issues in existing methods. First, when dealing with large corpus of candidate items, EBR models often have difficulties in balancing the performance on distinguishing highly relevant items (positives) from both irrelevant ones (easy negatives) and from somewhat related yet not competitive ones (hard negatives). Also, we have little control in the diversity and fairness of the retrieval results because of the ``greedy'' nature of nearest vector search. These issues compromise the performance of EBR methods in large-scale industrial scenarios. This paper introduces a simple and proven-in-production solution to overcome these issues. The proposed solution takes a divide-and-conquer approach: the whole set of candidate items are divided into multiple clusters and we run EBR to retrieve relevant candidates from each cluster in parallel; top candidates from each cluster are then combined by some controllable merging strategies. This approach allows our EBR models to only concentrate on discriminating positives from mostly hard negatives. It also enables further improvement from a multi-tasking learning (MTL) perspective: retrieval problems within each cluster can be regarded as individual tasks; inspired by recent successes in prompting and prefix-tuning, we propose an efficient task adaption technique further boosting the retrieval performance within each cluster with negligible overheads.

Latent Evolution Model for Change Point Detection in Time-varying Networks

Graph-based change point detection (CPD) play an irreplaceable role in discovering anomalous graphs in the time-varying network. While several techniques have been proposed to detect change points by identifying whether there is a significant difference between the target network and successive previous ones, they neglect the natural evolution of the network. In practice, real-world graphs such as social networks, traffic networks, and rating networks are constantly evolving over time. Considering this problem, we treat the problem as a prediction task and propose a novel CPD method for dynamic graphs via a latent evolution model. Our method focuses on learning the low-dimensional representations of networks and capturing the evolving patterns of these learned latent representations simultaneously. After having the evolving patterns, a prediction of the target network can be achieved. Then, we can detect the change points by comparing the prediction and the actual network by leveraging a trade-off strategy, which balances the importance between the prediction network and the normal graph pattern extracted from previous networks. Intensive experiments conducted on both synthetic and real-world datasets show the effectiveness and superiority of our model.

Dual Preference Distribution Learning for Item Recommendation

Recommender systems can automatically recommend users items that they probably like, for which the goal is to represent the user and item as well as model their interaction. Existing methods have primarily learned the user's preferences and item's features with vectorized representations, and modeled the user-item interaction by the similarity of their representations. In fact, the user's different preferences are related and capturing such relations could better understand the user's preferences for a better recommendation. Toward this end, we propose to represent the user's preference with multi-variant Gaussian distribution, and model the user-item interaction by calculating the probability density at the item in the user's preference distribution. In this manner, the mean vector of the Gaussian distribution is able to capture the center of the user's preferences, while its covariance matrix captures the relations of these preferences. In particular, in this work, we propose a dual preference distribution learning framework (DUPLE), which captures the user's preferences to both the items and attributes by a Gaussian distribution, respectively. As a byproduct, identifying the user's preference to specific attributes enables us to provide the explanation of recommending an item to the user. Extensive quantitative and qualitative experiments on six public datasets show that DUPLE achieves the best performance over all state-of-the-art recommendation methods.