Lightweight Modality Adaptation to Sequential Recommendation via Correlation Supervision

In Sequential Recommenders (SR), encoding and utilizing modalities in an end-to-end manner is costly in terms of modality encoder sizes. Two-stage approaches can mitigate such concerns, but they suffer from poor performance due to modality forgetting, where the sequential objective overshadows modality representation. We propose a lightweight knowledge distillation solution that preserves both merits: retaining modality information and maintaining high efficiency. Specifically, we introduce a novel method that enhances the learning of embeddings in SR through the supervision of modality correlations. The supervision signals are distilled from the original modality representations, including both (1) holistic correlations, which quantify their overall associations, and (2) dissected correlation types, which refine their relationship facets (honing in on specific aspects like color or shape consistency). To further address the issue of modality forgetting, we propose an asynchronous learning step, allowing the original information to be retained longer for training the representation learning module. Our approach is compatible with various backbone architectures and outperforms the top baselines by 6.8% on average. We empirically demonstrate that preserving original feature associations from modality encoders significantly boosts task-specific recommendation adaptation. Additionally, we find that larger modality encoders (e.g., Large Language Models) contain richer feature sets which necessitate more fine-grained modeling to reach their full performance potential.Comment: Accepted by ECIR 202

A Conversation is Worth A Thousand Recommendations: A Survey of Holistic Conversational Recommender Systems

Conversational recommender systems (CRS) generate recommendations through an interactive process. However, not all CRS approaches use human conversations as their source of interaction data; the majority of prior CRS work simulates interactions by exchanging entity-level information. As a result, claims of prior CRS work do not generalise to real-world settings where conversations take unexpected turns, or where conversational and intent understanding is not perfect. To tackle this challenge, the research community has started to examine holistic CRS, which are trained using conversational data collected from real-world scenarios. Despite their emergence, such holistic approaches are under-explored. We present a comprehensive survey of holistic CRS methods by summarizing the literature in a structured manner. Our survey recognises holistic CRS approaches as having three components: 1) a backbone language model, the optional use of 2) external knowledge, and/or 3) external guidance. We also give a detailed analysis of CRS datasets and evaluation methods in real application scenarios. We offer our insight as to the current challenges of holistic CRS and possible future trends.Comment: Accepted by 5th KaRS Workshop @ ACM RecSys 2023, 8 page

TF-DCon: Leveraging Large Language Models (LLMs) to Empower Training-Free Dataset Condensation for Content-Based Recommendation

Modern techniques in Content-based Recommendation (CBR) leverage item content information to provide personalized services to users, but suffer from resource-intensive training on large datasets. To address this issue, we explore the dataset condensation for textual CBR in this paper. The goal of dataset condensation is to synthesize a small yet informative dataset, upon which models can achieve performance comparable to those trained on large datasets. While existing condensation approaches are tailored to classification tasks for continuous data like images or embeddings, direct application of them to CBR has limitations. To bridge this gap, we investigate efficient dataset condensation for content-based recommendation. Inspired by the remarkable abilities of large language models (LLMs) in text comprehension and generation, we leverage LLMs to empower the generation of textual content during condensation. To handle the interaction data involving both users and items, we devise a dual-level condensation method: content-level and user-level. At content-level, we utilize LLMs to condense all contents of an item into a new informative title. At user-level, we design a clustering-based synthesis module, where we first utilize LLMs to extract user interests. Then, the user interests and user embeddings are incorporated to condense users and generate interactions for condensed users. Notably, the condensation paradigm of this method is forward and free from iterative optimization on the synthesized dataset. Extensive empirical findings from our study, conducted on three authentic datasets, substantiate the efficacy of the proposed method. Particularly, we are able to approximate up to 97% of the original performance while reducing the dataset size by 95% (i.e., on dataset MIND)

A systematic review and meta-analysis of Danshen combined with mesalazine for the treatment of ulcerative colitis

Purpose: Current pharmacological treatments for Ulcerative Colitis (UC) have limitations. Therefore, it is important to elucidate any available alternative or complementary treatment, and Chinese herbal medicine shows the potential for such treatment. As a traditional Chinese herbal medicine, Danshen-related preparations have been reported to be beneficial for UC by improving coagulation function and inhibiting inflammatory responses. In spite of this, the credibility and safety of this practice are incomplete. Therefore, in order to investigate whether Danshen preparation (DSP) is effective and safe in the treatment of UC, we conducted a systematic review and meta-analysis.Methods: PubMed, Embase, Cochrane Library, Web of Science, China National Knowledge Infrastructure, Wanfang Database and CQVIP Database were searched for this review.The main observation indexes were the effect of DSP combined with mesalazine or DSP on the effective rate, platelet count (PLT), mean platelet volume (MPV) and C-reactive protein (CRP) of UC. The Cochrane risk of bias tool was used to assess the risk of bias. The selected studies were evaluated for quality and data processing using RevMan5.4 and Stata17.0 software.Results: A total of 37 studies were included. Among them, 26 clinical trials with 2426 patients were included and 11 animal experimental studies involving 208 animals were included. Meta-analysis results showed that compared with mesalazine alone, combined use of DSP can clearly improve the clinical effective rate (RR 0.86%, 95% CI:0.83–0.88, p < 0.00001) of UC. Furthermore it improved blood coagulation function by decreasing serum PLT and increasing MPV levels, and controlled inflammatory responses by reducing serum CRP, TNF-α, IL-6, and IL-8 levels in patients.Conclusion: Combining DSP with mesalazine for UC can enhance clinical efficacy. However, caution should be exercised in interpreting the results of this review due to its flaws, such as allocation concealment and uncertainty resulting from the blinding of the study.Systematic Review Registration: http://www.crd.york.ac.uk/PROSPERO/myprospero.php, identifier PROSPERO: CRD4202229328

Serum 25-hydroxyvitamin D level and erectile dysfunction: a causal relationship? Findings from a two-sample Mendelian randomization study

BackgroundSerum 25-hydroxyvitamin D level is associated with erectile dysfunction (ED) in observational studies. However, whether there is a causal association between them remains uncertain.ObjectiveConduct a two-sample Mendelian randomization (MR) analysis to investigate the causal effect between serum 25-hydroxyvitamin D level and ED risk.MethodGenome-wide association study (GWAS) data of serum 25-hydroxyvitamin D levels comprising 6,896,093 single nucleotide polymorphisms (SNP) from 496,949 people of European ancestry were regarded as exposure for the MR analysis. Additional GWAS data involving 9,310,196 SNPs of 6,175 European ED cases and 217,630 controls were used as outcome data. The MR-Egger, inverse variance weighted (IVW) method, weighted median, simple mode, and weighted mode were employed to evaluate causal effects, among which IVW was the primary MR analysis method. The stability of the MR analysis results was confirmed by a heterogeneity test, a horizontal pleiotropy test, and the leave-one-out method.ResultThere were 103 SNPs utilized as instrumental variables (p < 5 × 10−8). The results of MR analysis showed no causal effects of serum 25(OH) D concentration on ED risks (IVW; OR = 0.9516, 95% CI = 0.7994 to 1.1328, p = 0.5772). There was no heterogeneity and pleiotropy in the statistical models.ConclusionThe present MR study did not support a causal association for genetically predicted serum 25-hydroxyvitamin D concentration in the risk of ED in individuals of European descent

Experiences with GreenGPS – Fuel-Efficient Navigation using Participatory Sensing

Participatory sensing services based on mobile phones constitute an important growing area of mobile computing. Most services start small and hence are initially sparsely deployed. Unless a mobile service adds value while sparsely deployed, it may not survive conditions of sparse deployment. The paper offers a generic solution to this problem and illustrates this solution in the context of GreenGPS; a navigation service that allows drivers to find the most fuel-efficient routes customized for their vehicles between arbitrary end-points. Specifically, when the participatory sensing service is sparsely deployed, we demonstrate a general framework for generalization from sparse collected data to produce models extending beyond the current data coverage. This generalization allows the mobile service to offer value under broader conditions. GreenGPS uses our developed participatory sensing infrastructure and generalization algorithms to perform inexpensive data collection, aggregation, and modeling in an end-to-end automated fashion. The models are subsequently used by our backend engine to predict customized fuel-efficient routes for both members and non-members of the service. GreenGPS is offered as a mobile phone application and can be easily deployed and used by individuals. A preliminary study of our green navigation idea was performed in [1], however, the effort was focused on a proof-of-concept implementation that involved substantial offline and manual processing. In contrast, the results and conclusions in the current paper are based on a more advanced and accurate model and extensive data from a real-world phone-based implementation and deployment, which enables reliable and automatic end-to-end data collection and route recommendation. The system further benefits from lower cost and easier deployment. To evaluate the green navigation service efficiency, we conducted a user subject study consisting of 22 users driving different vehicles over the course of several months in Urbana-Champaign, IL. The experimental results using the collected data suggest that fuel savings of 21.5% over the fastest, 11.2% over the shortest, and 8.4% over the Garmin eco routes can be achieved by following GreenGPS green routes. The study confirms that our navigation service can survive conditions of sparse deployment and at the same time achieve accurate fuel predictions and lead to significant fuel savings.This research was sponsored in part by IBM Research and NSF Grants CNS 10-59294, CNS 10-40380 and CNS 13-45266.Ope

SmartRoad: A Crowd-Sourced Traffic Regulator Detection and Identification System

In this paper we present SmartRoad, a crowd-sourced sensing system that detects and identifies traffic regulators, traffic lights and stop signs in particular. As an alternative to expensive road surveys, SmartRoad works on participatory sensing data collected from GPS sensors from invehicle smartphones. The resulting traffic regulator information can be used for many assisted-driving or navigation systems. In order to achieve accurate detection and identification, SmartRoad addresses various challenges in participatory sensing scenarios, including data unreliability/sparsity, energy constraints, and the general lack of ground truth information. SmartRoad automatically adapts to different application requirements by intelligently choosing the most appropriate information representation and transmission schemes; it also dynamically evolves its core detection and identification engines to effectively take advantage of any external ground truth information or opportunity. With these two characteristics, SmartRoad consistently delivers outstanding performance for its road sensing tasks. We implement SmartRoad on a vehicular smartphone testbed, and deploy on 35 external volunteer users’ vehicles for two months. Experiment results show that SmartRoad can robustly, effectively and efficiently carry out its detection and identification tasks without consuming excessive communication energy/bandwidth or requiring too much ground truth information.unpublishe

SmartRoad: A Mobile Phone Based Crowd-Sourced Road Sensing System

In this paper we describe SmartRoad, a road sensing system that generates and collects mobile sensory data from vehicle-resident mobile phones, in enabling and supporting crowd-sourced road sensing applications and services, as an alternative to expensive road surveys conducted traditionally. We implement the SmartRoad prototype system, and deploy it on 35 volunteer users’ vehicles for 2 months, collecting about 4,000 miles of driving data.unpublishednot peer reviewe

Rapid monitoring the water extraction process of Radix Paeoniae Alba using near infrared spectroscopy

Near infrared (NIR) spectroscopy has been developed into one of the most important process analytical techniques (PAT) in a wide field of applications. The feasibility of NIR spectroscopy with partial least square regression (PLSR) to monitor the concentration of paeoniflorin, albiflorin, gallic acid, and benzoyl paeoniflorin during the water extraction process of Radix Paeoniae Alba was demonstrated and verified in this work. NIR spectra were collected in transmission mode and pretreated with smoothing and/or derivative, and then quantitative models were built up using PLSR. Interval partial least squares (iPLS) method was used for the selection of spectral variables. Determination coefficients (Rcal2 and Rpred2), root mean squares error of prediction (RMSEP), root mean squares error of calibration (RMSEC), and residual predictive deviation (RPD) were applied to verify the performance of the models, and the corresponding values were 0.9873 and 0.9855, 0.0487mg/mL, 0.0545mg/mL and 8.4 for paeoniflorin; 0.9879, 0.9888, 0.0303mg/mL, 0.0321mg/mL and 9.1 for albiflorin; 0.9696, 0.9644, 0.0140mg/mL, 0.0145mg/mL and 5.1 for gallic acid; 0.9794, 0.9781, 0.00169mg/mL, 0.00171mg/mL and 6.9 for benzoyl paeoniflorin, respectively. The results turned out that this approach was very efficient and environmentally friendly for the quantitative monitoring of the water extraction process of Radix Paeoniae Alba