DMGIN: How Multimodal LLMs Enhance Large Recommendation Models for Lifelong User Post-click Behaviors

Modeling user interest based on lifelong user behavior sequences is crucial for enhancing Click-Through Rate (CTR) prediction. However, long post-click behavior sequences themselves pose severe performance issues: the sheer volume of data leads to high computational costs and inefficiencies in model training and inference. Traditional methods address this by introducing two-stage approaches, but this compromises model effectiveness due to incomplete utilization of the full sequence context. More importantly, integrating multimodal embeddings into existing large recommendation models (LRM) presents significant challenges: These embeddings often exacerbate computational burdens and mismatch with LRM architectures. To address these issues and enhance the model's efficiency and accuracy, we introduce Deep Multimodal Group Interest Network (DMGIN). Given the observation that user post-click behavior sequences contain a large number of repeated items with varying behaviors and timestamps, DMGIN employs Multimodal LLMs(MLLM) for grouping to reorganize complete lifelong post-click behavior sequences more effectively, with almost no additional computational overhead, as opposed to directly introducing multimodal embeddings. To mitigate the potential information loss from grouping, we have implemented two key strategies. First, we analyze behaviors within each group using both interest statistics and intra-group transformers to capture group traits. Second, apply inter-group transformers to temporally ordered groups to capture the evolution of user group interests. Our extensive experiments on both industrial and public datasets confirm the effectiveness and efficiency of DMGIN. The A/B test in our LBS advertising system shows that DMGIN improves CTR by 4.7% and Revenue per Mile by 2.3%.

Deep Multiple Quantization Network on Long Behavior Sequence for Click-Through Rate Prediction

In Click-Through Rate (CTR) prediction, the long behavior sequence, comprising the user's long period of historical interactions with items has a vital influence on assessing the user's interest in the candidate item. Existing approaches strike efficiency and effectiveness through a two-stage paradigm: first retrieving hundreds of candidate-related items and then extracting interest intensity vector through target attention. However, we argue that the discrepancy in target attention's relevance distribution between the retrieved items and the full long behavior sequence inevitably leads to a performance decline. To alleviate the discrepancy, we propose the Deep Multiple Quantization Network (DMQN) to process long behavior sequence end-to-end through compressing the long behavior sequence. Firstly, the entire spectrum of long behavior sequence will be quantized into multiple codeword sequences based on multiple independent codebooks. Hierarchical Sequential Transduction Unit is incorporated to facilitate the interaction of reduced codeword sequences. Then, attention between the candidate and multiple codeword sequences will output the interest vector. To enable online serving, intermediate representations of the codeword sequences are cached, significantly reducing latency. Our extensive experiments on both industrial and public datasets confirm the effectiveness and efficiency of DMQN. The A/B test in our advertising system shows that DMQN improves CTR by 3.5% and RPM by 2.0%.