Sequential recommendation aims to predict the next item of interest for a user, based on her/his interaction history. In conventional sequential recommenders, a common approach is to learn sequence representations based on ID embeddings of items, which can be leveraged to predict the subsequent items of interest. Clearly, the sequence representations encode user behavioral patterns, which are critical to recommendation. Inspired by recent success in empowering Large Language Models (LLMs) to understand diverse modality (e.g., image, audio), a compelling question arises: “Can LLMs understand and utilize representations from conventional recommenders?” To answer this, we propose RecInterpreter, which examines the capacity of LLMs to decipher the representation space of pretrained recommenders. Specifically, with the multimodal pairs (i.e., interaction sequence representations and text narrations), RecInterpreter first uses a lightweight projector to map the representations into the token embedding space of the LLM, encouraging LLM to generate textual narrations for items within the sequence. Furthermore, upon interpreting recommenders, LLM can enhance its recommendation capabilities through fine-tuning with the projected representations, even without textual description of interaction sequences. Experiments showcase that RecInterpreter enhances LLMs to understand hidden representations from ID-based sequential recommenders and better accomplish recommendation task with the explicit understanding of behavior patterns.

Diffusion models have shown significant potential in generating oracle items that best match user preference with guidance from user historical interaction sequences.However, the quality of guidance is often compromised by unpredictable missing data in observed sequence, leading to suboptimal item generation.Since missing data is uncertain in both occurrence and content, recovering it is impractical and may introduce additional errors.To tackle this challenge, we propose a novel dual-side Thompson sampling-based Diffusion Model (TDM), which simulates extra missing data in the guidance signals and allows diffusion models to handle existing missing data through extrapolation.To preserve user preference evolution in sequences despite extra missing data, we introduce Dual-side Thompson Sampling to implement simulation with two probability models, sampling by exploiting user preference from both item continuity and sequence stability.TDM strategically removes items from sequences based on dual-side Thompson sampling and treats these edited sequences as guidance for diffusion models, enhancing models' robustness to missing data through consistency regularization.Additionally, to enhance the generation efficiency, TDM is implemented under the denoising diffusion implicit models to accelerate the reverse process.Extensive experiments and theoretical analysis validate the effectiveness of TDM in addressing missing data in sequential recommendations.Our data and code is available at https

Sequential recommendation aims to predict users' next interaction with items based on their past engagement sequence. Recently, the advent of Large Language Models (LLMs) has sparked interest in leveraging them for sequential recommendation, viewing it as language modeling. Previous studies represent items within LLMs' input prompts as either ID indices or textual metadata. However, these approaches often fail to either encapsulate comprehensive world knowledge or exhibit sufficient behavioral understanding. To combine the complementary strengths of conventional recommenders in capturing behavioral patterns of users and LLMs in encoding world knowledge about items, we introduce Large Language-Recommendation Assistant (LLaRA). Specifically, it uses a novel hybrid prompting method that integrates ID-based item embeddings learned by traditional recommendation models with textual item features. Treating the "sequential behaviors of users" as a distinct modality beyond texts, we employ a projector to align the traditional recommender's ID embeddings with the LLM's input space. Moreover, rather than directly exposing the hybrid prompt to LLMs, a curriculum learning strategy is adopted to gradually ramp up training complexity. Initially, we warm up the LLM using text-only prompts, which better suit its inherent language modeling ability. Subsequently, we progressively transition to the hybrid prompts, training the model to seamlessly incorporate the behavioral knowledge from the traditional sequential recommender into the LLM. Empirical results validate the effectiveness of our proposed framework. Codes are available at https://github.com/ljy0ustc/LLaRA.

Collaborative Filtering (CF) models, despite their great success, suffer from severe performance drops due to popularity distribution shifts, where these changes are ubiquitous and inevitable in real-world scenarios. Unfortunately, most leading popularity debiasing strategies, rather than tackling the vulnerability of CF models to varying popularity distributions, require prior knowledge of the test distribution to identify the degree of bias and further learn the popularity-entangled representations to mitigate the bias. Consequently, these models result in significant performance benefits in the target test set, while dramatically deviating the recommendation from users’ true interests without knowing the popularity distribution in advance. In this work, we propose a novel learning framework, Invariant Collaborative Filtering (InvCF), to discover disentangled representations that faithfully reveal the latent preference and popularity semantics without making any assumption about the popularity distribution. At its core is the distillation of unbiased preference representations (i.e., user preference on item property), which are invariant to the change of popularity semantics, while filtering out the popularity feature that is unstable or outdated. Extensive experiments on five benchmark datasets and four evaluation settings (i.e., synthetic long-tail, unbiased, temporal split, and out-of-distribution evaluations) demonstrate that InvCF outperforms the state-of-the-art baselines in terms of popularity generalization ability on real recommendations. Visualization studies shed light on the advantages of InvCF for disentangled representation learning. Our codes are available at https://github.com/anzhang314/InvCF.

Knowledge graph (KG) plays an increasingly important role in recommender systems. A recent technical trend is to develop end-to-end models founded on graph neural networks (GNNs). However, existing GNN-based models are coarse-grained in relational modeling, failing to (1) identify user-item relation at a fine-grained level of intents, and (2) exploit relation dependencies to preserve the semantics of long-range connectivity.