As online learning platforms continue to expand, the integration of large language models (LLMs) into course recommendation systems has shown significant potential, leveraging their semantic reasoning capabilities and inherent world knowledge. Current approaches typically adopt a small-model retrieval followed by an LLM-based re-ranking strategy. However, these methods face notable limitations. They often do not fully utilize the built-in knowledge of LLMs to enhance the initial retrieval phase and lack sufficient adaptation to the unique characteristics of educational contexts, limiting their effectiveness in personalized course recommendations. In addition, issues such as hallucinations and inconsistencies in the decoding of LLM output pose challenges to the reliability of these systems.To address these challenges, we propose CourseLRec, an innovative course recommendation framework that combines the semantic reasoning capabilities of LLMs with the efficiency of sequence modeling techniques. CourseLRec introduces three key innovations: LLM-Embedding for data augmentation of course titles and types, combined with Course-Aware Fusion to dynamically balance user interaction patterns and course content semantics; an Education-CoT Prompt to effectively integrate domain knowledge with learners’ progressive learning trajectories; and LoRA fine-tuning alongside a Context-Enhanced Token-to-Course Mapping module to enhance computational efficiency and semantic modeling. Comprehensive experiments on MOOCCourse and MOOCCube datasets demonstrate that CourseLRec outperforms state-of-the-art models across multiple evaluation metrics, highlighting its effectiveness in course recommendation tasks.

Incorporating a critiquing component into recommender applications facilitates the enhancement of user perception. Typically, critique-able recommender systems adapt the model parameters and update the recommendation list in real-time through the analysis of user critiquing keyphrases in the inference phase. The current critiquing methods necessitate the designation of a dedicated recommendation model to estimate user relevance to the critiquing keyphrase during the training phase preceding the recommendations update. This paradigm restricts the applicable scenarios and reduces the potential for keyphrase exploitation. Furthermore, these approaches ignore the issue of catastrophic forgetting caused by continuous modification of model parameters in multi-step critiquing. Thus, we present a general Representative Items Sampling Framework for Critiquing on Knowledge Graph Recommendation (RISC) implemented as a plug-in, which offers a new paradigm for critiquing in mainstream recommendation scenarios. RISC leverages the knowledge graph to sample important representative items as a hinge to expand and convey information from user critiquing, indirectly estimating the relevance of the user to the critiquing keyphrase. Consequently, the necessity for specialized user-keyphrase correlation modules is eliminated with respect to a variety of knowledge graph recommendation models. Moreover, we propose a Weight Experience Replay (WER) approach based on KG to mitigate catastrophic forgetting by reinforcing the user's prior preferences during the inference phase. Our extensive experimental findings on three real-world datasets and three knowledge graph recommendation methods illustrate that RISC with WER can be effectively integrated into knowledge graph recommendation models to efficiently utilize user critiquing for refining recommendations and mitigate catastrophic forgetting.

Recommendation systems are the primary means of information filtering in the current information age. They utilize user historical behaviors, personal preferences, and item attributes to predict user interests and deliver personalized recommendations. Existing research has predominantly focused on improving the accuracy of recommendation systems. However, false correlation and insufficient personalization are issues that have received limited attention. This paper introduces the Causal Feature-Enhanced Collaborative Filtering recommendation algorithm (CFECF) which addresses concerns that significantly impact the generalization capability of recommendation algorithms and user satisfaction. CFECF adopts a causal relationship perspective and introduces a latent outcome model framework to assess the true causal relationships between user and item features. This approach addresses the problem of false correlation in recommendation systems and enhances the model’s generalization capability. To improve personalization, user interaction histories are analyzed to derive a prior indicator based on statistical distribution, indicating user interest directions. The model’s training incorporates this indicator to enhance user satisfaction. Comprehensive experiments conducted on four real-world datasets affirm that the proposed CFECF markedly enhances recommendation performance when compared to state-of-the-art collaborative filtering methods, effectively addresses false correlation and insufficient personalization issues in recommendation systems.

Automatic recommendation has become an increasingly relevant problem to industries, which allows users to discover new items that match their tastes and enables the system to target items to the right users. In this article, we have proposed a deep learning based fully Bayesian treatment recommendation framework, DVMF, which has high-quality performance and ability to integrate any kinds of side information handily and efficiently. In DVMF, the variational inference technique and the reparameterization tricks are introduced to make DVMF possible to be optimized by the stochastic gradient-based methods, in addition, two novel deep neural networks have been constructed to infer the hyper-parameters of the distributions of latent factors from the knowledge of user and item, which are represented as low-dimensional real-valued vectors retaining primary features. Experimental results on five public databases indicate that the proposed method performs better than the state-of-the-art recommendation algorithms on prediction accuracy in terms of quantitative assessments.

Automatic recommendation has become an increasingly relevant problem to industries, which allows users to discover new items that match their tastes and enables the system to target items to the right users. In this paper, we propose a deep learning (DL) based collaborative filtering framework, namely, deep matrix factorization (DMF), which can integrate any kind of side information effectively and handily. In DMF, two feature transforming functions are built to directly generate latent factors of users and items from various input information. As for the implicit feedback that is commonly used as input of recommendation algorithms, implicit feedback embedding (IFE) is proposed. IFE converts the high-dimensional and sparse implicit feedback information into a low-dimensional real-valued vector retaining primary features. Using IFE could reduce the scale of model parameters conspicuously and increase model training efficiency. Experimental results on five public databases indicate that the proposed method performs better than the state-of-the-art DL-based recommendation algorithms on both accuracy and training efficiency in terms of quantitative assessments.