The goal of sequential recommendation (SR) is to predict a user's potential interested items based on her/his historical interaction sequences. Most existing sequential recommenders are developed based on ID features, which, despite their widespread use, often underperform with sparse IDs and struggle with the cold-start problem. Besides, inconsistent ID mappings hinder the model's transferability, isolating similar recommendation domains that could have been co-optimized. This paper aims to address these issues by exploring the potential of multi-modal information in learning robust and generalizable sequence representations. We propose MISSRec, a multi-modal pre-training and transfer learning framework for SR. On the user side, we design a Transformer-based encoder-decoder model, where the contextual encoder learns to capture the sequence-level multi-modal synergy while a novel interest-aware decoder is developed to grasp item-modality-interest relations for better sequence representation. On the candidate item side, we adopt a dynamic fusion module to produce user-adaptive item representation, providing more precise matching between users and items. We pre-train the model with contrastive learning objectives and fine-tune it in an efficient manner. Extensive experiments demonstrate the effectiveness and flexibility of MISSRec, promising an practical solution for real-world recommendation scenarios.

As an emerging privacy-preserving approach to leveraging cross-platform user interactions, vertical federated learning (VFL) has been increasingly applied in recommender systems. However, vanilla VFL is only applicable to overlapped users, ignoring potential universal interest patterns hidden among non-overlapped users and suffers from limited user group benefits, which hinders its application in real-world recommenders. In this paper, we extend the traditional vertical federated recommendation problem (VFR) to a more realistic Fully-Vertical federated recommendation setting (Fully-VFR) which aims to utilize all available data and serve full user groups. To tackle challenges in implementing Fully-VFR, we propose a Retrieval-enhanced Vertical Federated recommender (ReFer), a groundbreaking initiative that explores retrieval-enhanced machine learning approaches in VFL. Specifically, we establish a general "retrieval-and-utilization" algorithm to enhance the quality of representations across all parties. We design a flexible federated retrieval augmentation (RA) mechanism for VFL: (i) Cross-RA to complement field missing and (ii) Local-RA to promote mutual understanding between user groups. We conduct extensive experiments on both public and industry datasets. Results on both sequential and non-sequential CTR prediction tasks demonstrate that our method achieves significant performance improvements over baselines and is beneficial for all user groups.

Self-supervised video hashing (SSVH) is a practical task in video indexing and retrieval. Although Transformers are predominant in SSVH for their impressive temporal modeling capabilities, they often suffer from computational and memory inefficiencies. Drawing inspiration from Mamba, an advanced state-space model, we explore its potential in SSVH to achieve a better balance between efficacy and efficiency. We introduce S5VH, a Mamba-based video hashing model with an improved self-supervised learning paradigm. Specifically, we design bidirectional Mamba layers for both the encoder and decoder, which are effective and efficient in capturing temporal relationships thanks to the data-dependent selective scanning mechanism with linear complexity. In our learning strategy, we transform global semantics in the feature space into semantically consistent and discriminative hash centers, followed by a center alignment loss as a global learning signal. Our self-local-global (SLG) paradigm significantly improves learning efficiency, leading to faster and better convergence. Extensive experiments demonstrate S5VH's improvements over state-of-the-art methods, superior transferability, and scalable advantages in inference efficiency.