CRUC: Cold-start Recommendations Using Collaborative Filtering in Internet of Things

Discovering Collaborative Signals for Next POI Recommendation with Iterative Seq2Graph Augmentation

Being an indispensable component in location-based social networks, next point-of-interest (POI) recommendation recommends users unexplored POIs based on their recent visiting histories. However, existing work mainly models check-in data as isolated POI sequences, neglecting the crucial collaborative signals from cross-sequence check-in information. Furthermore, the sparse POI-POI transitions restrict the ability of a model to learn effective sequential patterns for recommendation. In this paper, we propose Sequence-to-Graph (Seq2Graph) augmentation for each POI sequence, allowing collaborativ…

让协同信号发挥作用：面向序列推荐的图感知大语言模型

Making Collaborative Signals Count: Graph-Aware Large Language Models for Sequential Recommendation

针对现有序列推荐方法难以捕捉全局协同模式的问题，提出图感知大语言模型框架GALLM，通过构建协同图建模三类关系并整合至注意力机制，在四个基准上取得最优性能，HR@5较最强基线平均提升9.76%。

大语言模型增强的协同过滤

Large Language Models Enhanced Collaborative Filtering

提出LLM-CF框架，通过上下文学习和思维链推理将大语言模型的世界知识与推理能力蒸馏到协同过滤中，并采用高效指令微调在保持通用性的同时提升推荐性能，实验证明其显著优于竞争基线。

面向社交物联网的情境感知服务推荐系统

Context-Aware Service Recommendation System for the Social Internet of Things

针对社交物联网中设备关系与服务评论情境利用不足的问题，提出聚合设备关系、结合因子分解机建模高阶交互并融合评论的推荐框架，提升了评分预测及服务推荐的准确性与相关性。

Hotel Recommendation System Based on User Profiles and Collaborative Filtering

Nowadays, people start to use online reservation systems to plan their vacations since they have vast amount of choices available. Selecting when and where to go from this large-scale options is getting harder. In addition, sometimes consumers can miss the better options due to the wealth of information to be found on the online reservation systems. In this sense, personalized services such as recommender systems play a crucial role in decision making. Two traditional recommendation techniques are content-based and collaborative filtering. While both methods have their advantages, they also ha…

Continuous-Time Sequential Recommendation with Temporal Graph Collaborative Transformer

In order to model the evolution of user preference, we should learn user/item embeddings based on time-ordered item purchasing sequences, which is defined as Sequential Recommendation (SR) problem. Existing methods leverage sequential patterns to model item transitions. However, most of them ignore crucial temporal collaborative signals, which are latent in evolving user-item interactions and coexist with sequential patterns. Therefore, we propose to unify sequential patterns and temporal collaborative signals to improve the quality of recommendation, which is rather challenging. Firstly, it i…

CoRA：通过大语言模型权重实现协同信息感知的推荐方法

CoRA: Collaborative Information Perception by Large Language Model's Weights for Recommendation

针对LLM推荐中微调损害通用知识、协同特征干扰提示语义的问题，本文提出CoRA，将协同信息经查询生成与低秩权重转换注入LLM参数空间，实现免微调、免额外标记的个性化推荐并提升性能。

COLA: Improving Conversational Recommender Systems by Collaborative Augmentation

Conversational recommender systems (CRS) aim to employ natural language conversations to suggest suitable products to users. Understanding user preferences for prospective items and learning efficient item representations are crucial for CRS. Despite various attempts, earlier studies mostly learned item representations based on individual conversations, ignoring item popularity embodied among all others. Besides, they still need support in efficiently capturing user preferences since the information reflected in a single conversation is limited. Inspired by collaborative filtering, we propose …

安全协同过滤

Safe Collaborative Filtering

针对个性化推荐中低满意度用户流失风险，提出最小化用户损失尾部CVaR的安全协同过滤方法，通过扩展iALS实现可扩展算法，并在真实数据集上验证其优异尾部性能与计算效率。

Relational Collaborative Filtering:Modeling Multiple Item Relations for Recommendation

Existing item-based collaborative filtering (ICF) methods leverage only the relation of collaborative similarity. Nevertheless, there exist multiple relations between items in real-world scenarios. Distinct from the collaborative similarity that implies co-interact patterns from the user perspective, these relations reveal fine-grained knowledge on items from different perspectives of meta-data, functionality, etc. However, how to incorporate multiple item relations is less explored in recommendation research. In this work, we propose Relational Collaborative Filtering (RCF), a general framewo…

Variational Autoencoders for Collaborative Filtering

We extend variational autoencoders (VAEs) to collaborative filtering for implicit feedback. This non-linear probabilistic model enables us to go beyond the limited modeling capacity of linear factor models which still largely dominate collaborative filtering research.We introduce a generative model with multinomial likelihood and use Bayesian inference for parameter estimation. Despite widespread use in language modeling and economics, the multinomial likelihood receives less attention in the recommender systems literature. We introduce a different regularization parameter for the learning obj…