Sequential recommendation aims to predict users' future interactions by modeling collaborative filtering (CF) signals from historical behaviors of similar users or items. Traditional sequential recommenders predominantly rely on ID-based embeddings, which capture CF signals through high-order co-occurrence patterns. However, these embeddings depend solely on past interactions, lacking transferable knowledge to generalize to unseen domains. Recent advances in large language models (LLMs) have motivated text-based recommendation approaches that derive item representations from textual descriptions. While these methods enhance generalization, they fail to encode CF signals-i.e., latent item correlations and preference patterns-crucial for effective recommendation. We argue that an ideal embedding model should seamlessly integrate CF signals with rich semantic representations to improve both in-domain and out-of-domain recommendation performance. To this end, we propose LLM2Rec, a novel embedding model tailored for sequential recommendation, integrating the rich semantic understanding of LLMs with CF awareness. Our approach follows a two-stage training framework: (1) Collaborative Supervised Fine-tuning, which adapts LLMs to infer item relationships based on historical interactions, and (2) Item-level Embedding Modeling, which refines these specialized LLMs into structured item embedding models that encode both semantic and collaborative information. Extensive experiments on real-world datasets demonstrate that LLM2Rec effectively improves recommendation quality across both in-domain and out-of-domain settings. Our findings highlight the potential of leveraging LLMs to build more robust, generalizable embedding models for sequential recommendation. Our codes are available at: https://github.com/HappyPointer/LLM2Rec.

Product bundling has been a prevailing marketing strategy that is beneficial in the online shopping scenario. Effective product bundling methods depend on high-quality item representations capturing both the individual items' semantics and cross-item relations. However, previous item representation learning methods, either feature fusion or graph learning, suffer from inadequate cross-modal alignment and struggle to capture the cross-item relations for cold-start items. Multimodal pre-train models could be the potential solutions given their promising performance on various multimodal downstream tasks. However, the cross-item relations have been under-explored in the current multimodal pre-train models.

Bundle recommendation seeks to recommend a bundle of related items to users to improve both user experience and the profits of platform. Existing bundle recommendation models have progressed from capturing only user-bundle interactions to the modeling of multiple relations among users, bundles, and items. CrossCBR, in particular, incorporates cross-view contrastive learning into a two-view preference learning framework, significantly improving SOTA performance. It does, however, have two limitations: (1) the two-view formulation does not fully exploit all the heterogeneous relations among users, bundles, and items; and (2) the “early contrast and late fusion” framework is less effective in capturing user preference and difficult to generalize to multiple views. In this article, we present MultiCBR, a novel Multi -view C ontrastive learning framework for B undle R ecommendation. First, we devise a multi-view representation learning framework capable of capturing all the user-bundle, user-item, and bundle-item relations, especially better utilizing the bundle-item affiliations to enhance sparse bundles’ representations. Second, we innovatively adopt an “early fusion and late contrast” design that first fuses the multi-view representations before performing self-supervised contrastive learning. In comparison to existing approaches, our framework reverses the order of fusion and contrast, introducing the following advantages: (1) Our framework is capable of modeling both cross-view and ego-view preferences, allowing us to achieve enhanced user preference modeling; and (2) instead of requiring quadratic number of cross-view contrastive losses, we only require two self-supervised contrastive losses, resulting in minimal extra costs. Experimental results on three public datasets indicate that our method outperforms SOTA methods. The code and dataset can be found in the github repo https://github.com/HappyPointer/MultiCBR .

Bundle recommendation aims to recommend a bundle of related items to users, which can satisfy the users' various needs with one-stop convenience. Recent methods usually take advantage of both user-bundle and user-item interactions information to obtain informative representations for users and bundles, corresponding to bundle view and item view, respectively. However, they either use a unified view without differentiation or loosely combine the predictions of two separate views, while the crucial cooperative association between the two views' representations is overlooked.

Particle Swarm Optimization (PSO) is a swarm intelligence method which is employed frequently for solving real-world problems. After its inception, many variants of PSO devote to improving its performance by modifying the behavior of each particle, in which the population topologies of the particle swarm may alter. This paper investigates how population topology influences the performance of PSO. A random topology generation algorithm that adopts both the greedy strategy and randomized algorithm is proposed in the paper. The randomly generated topologies are applied in PSO-w, which introduces no modification to the population topology of the original PSO. Experimental results demonstrate that algorithms using topologies with more edges tend to converge faster and generally obtain a more accurate solution. Another major result in this paper is that how clustering coefficient affects PSO largely depends on the sparsity of the topology. A lower clustering coefficient in sparse topology conduces to faster convergence and a more precise result, but a higher clustering coefficient is preferred when the topology is dense.