Sequential Recommendation with Diffusion Models

Generative models, such as Variational Auto-Encoder (VAE) and Generative Adversarial Network (GAN), have been successfully applied in sequential recommendation. These methods require sampling from probability distributions and adopt auxiliary loss functions to optimize the model, which can capture the uncertainty of user behaviors and alleviate exposure bias. However, existing generative models still suffer from the posterior collapse problem or the model collapse problem, thus limiting their applications in sequential recommendation. To tackle the challenges mentioned above, we leverage a new paradigm of the generative models, i.e., diffusion models, and present sequential recommendation with diffusion models (DiffRec), which can avoid the issues of VAE- and GAN-based models and show better performance. While diffusion models are originally proposed to process continuous image data, we design an additional transition in the forward process together with a transition in the reverse process to enable the processing of the discrete recommendation data. We also design a different noising strategy that only noises the target item instead of the whole sequence, which is more suitable for sequential recommendation. Based on the modified diffusion process, we derive the objective function of our framework using a simplification technique and design a denoise sequential recommender to fulfill the objective function. As the lengthened diffusion steps substantially increase the time complexity, we propose an efficient training strategy and an efficient inference strategy to reduce training and inference cost and improve recommendation diversity. Extensive experiment results on three public benchmark datasets verify the effectiveness of our approach and show that DiffRec outperforms the state-of-the-art sequential recommendation models

L1-ORF1p and Ago2 are involved in a siRNA-mediated regulation for promoter activity of L1-5’UTR

Introduction. Long interspersed nuclear elements-1 (L1), as the only one self-active retrotransposon of the mobile element, was found to be generally activated in tumor cells. The 5‘UTR of L1 (L1-5’UTR) contains both sense and antisense bidirectional promoters, transcription products of which can generate double-strand RNA (dsRNA). In addition, L1-ORF1p, a dsRNA binding protein encoded by L1, is considered to engage in some RNA-protein (RNP) formation. Ago2, one of the RISC components, can bind to dsRNA to form RISC complex, but its role in L1 regulation still remains unclear. Due that the 5‘UTR of L1 (L1-5’UTR) contains both sense and antisense bidirectional promoters, so the activities in both string were identified. A dsRNA-mediated regulation of L1-5’UTR, with the feedback regulation of L1-ORF1p as well as other key molecules engaged (Ago1–4) in this process, was also investigated. Material and methods. Genomic DNA was extracted from HEK293 cells and subjected to L1-5’UTR prepa­ration by PCR. Report gene system pIRESneo with SV40 promoter was employed. The promoter activities of different regions in L1-5’UTR were identified by constructing these regions into pIRESneo, which SV40 region was removed prior, to generate different recombinant plasmids. The promoter activities in recombinant plasmids were detected by the luciferase expression assay. Western blot and co-immunoprecipitation were employed to identify proteins expression and protein-protein interaction respectively. Results. Ago2 is a member of Agos family, which usually forms a RISC complex with si/miRNA and is involved in post- transcriptional regulation of many genes. Here L1-ORF1p and Ago2 conducts a regulation as a negative feedback for L1-5'UTR sense promoter. L1-ORF1p could form the immune complexes with Ago1, Ago2 and Ago4, respectively. Conclusions. L1-5’UTR harbors both sense and antisense promoter activity and a dsRNA-mediated regulation is responsible for L1-5’UTR regulation. Agos proteins and L1-ORF1p were engaged in this process

Performance Characteristics and Temperature Compensation Method of Fluid Property Sensor Based on Tuning-Fork Technology

Fluid property sensor (FPS) based on tuning-fork technology is applied to the measurement of the contaminant level of lubricant oil. The measuring principle of FPS sensor is derived and proved together with its resolution. The performance characteristics of the FPS sensor, such as sensitivity coefficient, resolution, and quality factor, are analyzed. A temperature compensation method is proposed to eliminate the temperature-dependence of the measuring parameters, and its validity is investigated by numerical simulation of sensitivity, oscillating frequency, and dielectric constant. The values of purification efficiency obtained using microwave and without microwave are compared experimentally

Frequency Enhanced Hybrid Attention Network for Sequential Recommendation

The self-attention mechanism, which equips with a strong capability of modeling long-range dependencies, is one of the extensively used techniques in the sequential recommendation field. However, many recent studies represent that current self-attention based models are low-pass filters and are inadequate to capture high-frequency information. Furthermore, since the items in the user behaviors are intertwined with each other, these models are incomplete to distinguish the inherent periodicity obscured in the time domain. In this work, we shift the perspective to the frequency domain, and propose a novel Frequency Enhanced Hybrid Attention Network for Sequential Recommendation, namely FEARec. In this model, we firstly improve the original time domain self-attention in the frequency domain with a ramp structure to make both low-frequency and high-frequency information could be explicitly learned in our approach. Moreover, we additionally design a similar attention mechanism via auto-correlation in the frequency domain to capture the periodic characteristics and fuse the time and frequency level attention in a union model. Finally, both contrastive learning and frequency regularization are utilized to ensure that multiple views are aligned in both the time domain and frequency domain. Extensive experiments conducted on four widely used benchmark datasets demonstrate that the proposed model performs significantly better than the state-of-the-art approaches.Comment: 11 pages, 7 figures, The 46th International ACM SIGIR Conference on Research and Development in Information Retrieva

Intent Contrastive Learning with Cross Subsequences for Sequential Recommendation

The user purchase behaviors are mainly influenced by their intentions (e.g., buying clothes for decoration, buying brushes for painting, etc.). Modeling a user's latent intention can significantly improve the performance of recommendations. Previous works model users' intentions by considering the predefined label in auxiliary information or introducing stochastic data augmentation to learn purposes in the latent space. However, the auxiliary information is sparse and not always available for recommender systems, and introducing stochastic data augmentation may introduce noise and thus change the intentions hidden in the sequence. Therefore, leveraging user intentions for sequential recommendation (SR) can be challenging because they are frequently varied and unobserved. In this paper, Intent contrastive learning with Cross Subsequences for sequential Recommendation (ICSRec) is proposed to model users' latent intentions. Specifically, ICSRec first segments a user's sequential behaviors into multiple subsequences by using a dynamic sliding operation and takes these subsequences into the encoder to generate the representations for the user's intentions. To tackle the problem of no explicit labels for purposes, ICSRec assumes different subsequences with the same target item may represent the same intention and proposes a coarse-grain intent contrastive learning to push these subsequences closer. Then, fine-grain intent contrastive learning is mentioned to capture the fine-grain intentions of subsequences in sequential behaviors. Extensive experiments conducted on four real-world datasets demonstrate the superior performance of the proposed ICSRec model compared with baseline methods.Comment: 10pages, 5figures, WSDM2024. arXiv admin note: text overlap with arXiv:2304.0776

Development of a SCAR Marker for Rapid Identification of New Kentucky Bluegrass Breeding Lines

As a commonly used turfgrass, Kentucky bluegrass (Poa pratensis L.) (KBG) has many commercially available cultivars for production. After several years of screening, two new lines were obtained (‘KBG03’ and ‘KBG04’), which have high tolerance to summer. The study showed that the two lines revealed similar morphological characteristics, with light green leaf color, narrow leaf blade, high plant height and light 1,000-grain weight. A total of 400 random amplified polymorphic DNA (RAPD) primers and 256 sequence-related amplified polymorphism (SRAP) primer combinations were screened among the two lines and other 4 imported commercial cultivars. The percentages of polymorphic sites were 65.5% (RAPD) and 22.6% (SRAP) respectively. By cluster analysis of RAPD and SRAP data, the dendrogram at a similarity of 0.29 gave two main clusters, of which one group had 4 commercial cultivars, and the other had the two new breeding lines. Furthermore, one specific band of ‘KBG04’ was successfully converted into a dominant sequence characterized amplified region marker (SCAR196). Then the SCAR marker was verified by 39 KBG DNA samples, including imported varieties, domestic varieties and self-breeding lines of our laboratory, and it exhibited high consistency with the original RAPD polymorphic amplification. The results showed that the SCAR marker can be used to distinguish the new line ‘KBG04’ from numerous KBG germplasms, which would be useful for cultivar identification and property rights protection in the future