黃仲則寓京期間感遇詩之研究

有清一代，文風大盛。不論詩詞歌賦，抑是經史樸學，無不興盛。及至康、乾二朝，大家漸出。其中尤以詩壇之中名家輩出，然而清代的詩人，往往受唐宋名家所迷，困於前代詩人的範曙中，劉大杰曾謂： 清代詩人，喜言宗派 。[1] 清代詩派，大要言之，有尊唐及宗宋二派主流，而其中，尤以四家詩說影響至鉅。所謂四家詩說是指王士禎的神韻說、沈德潛的格調說、袁枚的性靈說及翁方綱的肌理說。在此四家詩說之下，康乾詩人，鮮有不受影響。而芸芸詩人中，黃景仁可說是少數能獨樹一幟的詩人。 今觀其詩作，既沒有神韻說的空洞，又沒有格調說的泥古不化，亦沒有肌種說的堆集煩瑣。可以說，他的詩歌，較近於袁枚的性重說，此或因黃袁二人為忘年之交，彼此有詩文來往，稱頌對方，故受到性靈說所黨陶的緣故。[2] 惟其詩雖近袁子才的性靈說， 但卻沒有半點袁詩通俗淺白的弊病。 若觀乎《兩當軒集》中諸詩，字字出自肺肺，莫不充溢其個人深厚的感情。正因如此，是以仲則在生前後之文，均對其詩有極高之評價，清代翁方綱云: 然而其詩尚沈鬱青壯，鏗鏘出金石，試摘其一二語，可通風雪而泣鬼神。[3] 清包世臣亦謂: 聲稱噪一時，乾隆六十年間，論詩者推為第一。[4] 清吳嵩梁於《石溪勛講話》中，亦有提及仲則的詩： 仲則詩無奇不有，無妙不臻，如仙人張樂，音外有音；名將用兵，法外有法。[5] 清汪防亦云： 吾鄉黃仲則先生，以詩鳴乾隆中葉。[6] 又，清代邱煒云： 乾隆才子黃仲刻，詩名遠播。[7] 近人伍合曰： 我們知道，作詩要下一番苦工夫，起碼非有幾十年的磨練，詩才會好，所以古人說， 晚節漸於詩律細 ，那一點也不錯，至於天賦的，那真是絕無僅有，往上數，李太白可以算一個，往下數，那只有黃景仁了。[8] 近人君山亦云： 黃仲則在世的日子雖然不長，死時僅得三十五歲，可是，在乾隆年間甚至乾隆以後，他卻是影響清代詩壇最大的一個詩人。[9] 自以上資料可見，黃仲則的詩作在其生前及死後，均得到肯定的評價。而在他芸芸詩作中，仲則的感遇詩最為世人所重。此蓋因其身世坎呵，生活艱苦，一生奔波，卻屢不得志，是故其詩作，情感特真，感慨特深。惟論者往往忽視仲則一生最感傷悲，精神所受痛苦最深切的時期，應當是他久滯京師時，是故仲則此時期的感遇詩，亦最能作為他同類詩作的代表。故此一時期的感遇詩，是極為值得研究的。 本文的研究範圍，當以仲則滯留京師至其歿於山西解州為止。考仲則一生，自乾隆四十年冬入京後，至乾隆四十八年病死止，凡三離京師，計有四十五年遊山東，客於學政程世淳幕中；次為四十六年遊西安，往訪峽西巡撫畢況；而最後一次為四十八年三月，因債主所迫，故抱病出都，至解州病歿。除最後一次以外，餘二次出京，為時皆甚短，故可說仲則寓京時間大約前後只有九年。因此本文研究之範團亦以此段時期為主。據《爾當軒集》中所編，仲則於此段時期約有詩作三百七十多首。本文所研究之對象為其感遇詩，故凡詩題中有感遇等字眼者，自是研究對象；另外尚有其他詩歌，其題雖不涉及感遇等字眼，惟若真內容有抒感言懷者，皆視之為感遇詩，一并研究。而於三百七十多首詩歌中，合乎於感遇題材之詩歌共有一百零九首，是以研究將集中於此一百零九首詩歌，惟若有其他可資佐證之詩歌，雖非於寓京期間所作，本文亦會加以引用。而文中黃仲則在京師時的感遇詩作，將一律簡稱為感遇詩，若遇有寓京以外的感遇詩，本文將加以注明。 本文共分八個部分。除前言後語與及文末注釋及參考書籍部分外，主要內容共分為四個部分：分別為(i) 窮 ； (ii) 貧 ; (iii) 病 ; (iv) 孤 。本文希望通過這四部分，對黃仲則寓京期間的感遇詩的感遇緣起作一研究，非去有所補足，只望可資作參考之用

ELCoRec: Enhance Language Understanding with Co-Propagation of Numerical and Categorical Features for Recommendation

Large language models have been flourishing in the natural language processing (NLP) domain, and their potential for recommendation has been paid much attention to. Despite the intelligence shown by the recommendation-oriented finetuned models, LLMs struggle to fully understand the user behavior patterns due to their innate weakness in interpreting numerical features and the overhead for long context, where the temporal relations among user behaviors, subtle quantitative signals among different ratings, and various side features of items are not well explored. Existing works only fine-tune a sole LLM on given text data without introducing that important information to it, leaving these problems unsolved. In this paper, we propose ELCoRec to Enhance Language understanding with CoPropagation of numerical and categorical features for Recommendation. Concretely, we propose to inject the preference understanding capability into LLM via a GAT expert model where the user preference is better encoded by parallelly propagating the temporal relations, and rating signals as well as various side information of historical items. The parallel propagation mechanism could stabilize heterogeneous features and offer an informative user preference encoding, which is then injected into the language models via soft prompting at the cost of a single token embedding. To further obtain the user's recent interests, we proposed a novel Recent interaction Augmented Prompt (RAP) template. Experiment results over three datasets against strong baselines validate the effectiveness of ELCoRec. The code is available at https://anonymous.4open.science/r/CIKM_Code_Repo-E6F5/README.md

AGADIR: Towards Array-Geometry Agnostic Directional Speech Recognition

Wearable devices like smart glasses are approaching the compute capability to seamlessly generate real-time closed captions for live conversations. We build on our recently introduced directional Automatic Speech Recognition (ASR) for smart glasses that have microphone arrays, which fuses multi-channel ASR with serialized output training, for wearer/conversation-partner disambiguation as well as suppression of cross-talk speech from non-target directions and noise. When ASR work is part of a broader system-development process, one may be faced with changes to microphone geometries as system development progresses. This paper aims to make multi-channel ASR insensitive to limited variations of microphone-array geometry. We show that a model trained on multiple similar geometries is largely agnostic and generalizes well to new geometries, as long as they are not too different. Furthermore, training the model this way improves accuracy for seen geometries by 15 to 28\% relative. Lastly, we refine the beamforming by a novel Non-Linearly Constrained Minimum Variance criterion.Comment: Accepted to ICASSP 202

M-scan: A Multi-Scenario Causal-driven Adaptive Network for Recommendation

We primarily focus on the field of multi-scenario recommendation, which poses a significant challenge in effectively leveraging data from different scenarios to enhance predictions in scenarios with limited data. Current mainstream efforts mainly center around innovative model network architectures, with the aim of enabling the network to implicitly acquire knowledge from diverse scenarios. However, the uncertainty of implicit learning in networks arises from the absence of explicit modeling, leading to not only difficulty in training but also incomplete user representation and suboptimal performance. Furthermore, through causal graph analysis, we have discovered that the scenario itself directly influences click behavior, yet existing approaches directly incorporate data from other scenarios during the training of the current scenario, leading to prediction biases when they directly utilize click behaviors from other scenarios to train models. To address these problems, we propose the Multi-Scenario Causal-driven Adaptive Network M-scan). This model incorporates a Scenario-Aware Co-Attention mechanism that explicitly extracts user interests from other scenarios that align with the current scenario. Additionally, it employs a Scenario Bias Eliminator module utilizing causal counterfactual inference to mitigate biases introduced by data from other scenarios. Extensive experiments on two public datasets demonstrate the efficacy of our M-scan compared to the existing baseline models.Comment: This paper has been accepted by WWW'2

Towards Efficient and Effective Unlearning of Large Language Models for Recommendation

The significant advancements in large language models (LLMs) give rise to a promising research direction, i.e., leveraging LLMs as recommenders (LLMRec). The efficacy of LLMRec arises from the open-world knowledge and reasoning capabilities inherent in LLMs. LLMRec acquires the recommendation capabilities through instruction tuning based on user interaction data. However, in order to protect user privacy and optimize utility, it is also crucial for LLMRec to intentionally forget specific user data, which is generally referred to as recommendation unlearning. In the era of LLMs, recommendation unlearning poses new challenges for LLMRec in terms of \textit{inefficiency} and \textit{ineffectiveness}. Existing unlearning methods require updating billions of parameters in LLMRec, which is costly and time-consuming. Besides, they always impact the model utility during the unlearning process. To this end, we propose \textbf{E2URec}, the first \underline{E}fficient and \underline{E}ffective \underline{U}nlearning method for LLM\underline{Rec}. Our proposed E2URec enhances the unlearning efficiency by updating only a few additional LoRA parameters, and improves the unlearning effectiveness by employing a teacher-student framework, where we maintain multiple teacher networks to guide the unlearning process. Extensive experiments show that E2URec outperforms state-of-the-art baselines on two real-world datasets. Specifically, E2URec can efficiently forget specific data without affecting recommendation performance. The source code is at \url{https://github.com/justarter/E2URec}.Comment: Accepted by Frontier of Computer Scienc

Large Language Models Make Sample-Efficient Recommender Systems

Large language models (LLMs) have achieved remarkable progress in the field of natural language processing (NLP), demonstrating remarkable abilities in producing text that resembles human language for various tasks. This opens up new opportunities for employing them in recommender systems (RSs). In this paper, we specifically examine the sample efficiency of LLM-enhanced recommender systems, which pertains to the model's capacity to attain superior performance with a limited quantity of training data. Conventional recommendation models (CRMs) often need a large amount of training data because of the sparsity of features and interactions. Hence, we propose and verify our core viewpoint: Large Language Models Make Sample-Efficient Recommender Systems. We propose a simple yet effective framework (i.e., Laser) to validate the viewpoint from two aspects: (1) LLMs themselves are sample-efficient recommenders; and (2) LLMs, as feature generators and encoders, make CRMs more sample-efficient. Extensive experiments on two public datasets show that Laser requires only a small fraction of training samples to match or even surpass CRMs that are trained on the entire training set, demonstrating superior sample efficiency.Comment: Accepted by Frontier of Computer Scienc

ReLLa: Retrieval-enhanced Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendation

With large language models (LLMs) achieving remarkable breakthroughs in natural language processing (NLP) domains, LLM-enhanced recommender systems have received much attention and have been actively explored currently. In this paper, we focus on adapting and empowering a pure large language model for zero-shot and few-shot recommendation tasks. First and foremost, we identify and formulate the lifelong sequential behavior incomprehension problem for LLMs in recommendation domains, i.e., LLMs fail to extract useful information from a textual context of long user behavior sequence, even if the length of context is far from reaching the context limitation of LLMs. To address such an issue and improve the recommendation performance of LLMs, we propose a novel framework, namely Retrieval-enhanced Large Language models (ReLLa) for recommendation tasks in both zero-shot and few-shot settings. For zero-shot recommendation, we perform semantic user behavior retrieval (SUBR) to improve the data quality of testing samples, which greatly reduces the difficulty for LLMs to extract the essential knowledge from user behavior sequences. As for few-shot recommendation, we further design retrieval-enhanced instruction tuning (ReiT) by adopting SUBR as a data augmentation technique for training samples. Specifically, we develop a mixed training dataset consisting of both the original data samples and their retrieval-enhanced counterparts. We conduct extensive experiments on a real-world public dataset (i.e., MovieLens-1M) to demonstrate the superiority of ReLLa compared with existing baseline models, as well as its capability for lifelong sequential behavior comprehension.Comment: Under Revie

ClickPrompt: CTR Models are Strong Prompt Generators for Adapting Language Models to CTR Prediction

Click-through rate (CTR) prediction has become increasingly indispensable for various Internet applications. Traditional CTR models convert the multi-field categorical data into ID features via one-hot encoding, and extract the collaborative signals among features. Such a paradigm suffers from the problem of semantic information loss. Another line of research explores the potential of pretrained language models (PLMs) for CTR prediction by converting input data into textual sentences through hard prompt templates. Although semantic signals are preserved, they generally fail to capture the collaborative information (e.g., feature interactions, pure ID features), not to mention the unacceptable inference overhead brought by the huge model size. In this paper, we aim to model both the semantic knowledge and collaborative knowledge for accurate CTR estimation, and meanwhile address the inference inefficiency issue. To benefit from both worlds and close their gaps, we propose a novel model-agnostic framework (i.e., ClickPrompt), where we incorporate CTR models to generate interaction-aware soft prompts for PLMs. We design a prompt-augmented masked language modeling (PA-MLM) pretraining task, where PLM has to recover the masked tokens based on the language context, as well as the soft prompts generated by CTR model. The collaborative and semantic knowledge from ID and textual features would be explicitly aligned and interacted via the prompt interface. Then, we can either tune the CTR model with PLM for superior performance, or solely tune the CTR model without PLM for inference efficiency. Experiments on four real-world datasets validate the effectiveness of ClickPrompt compared with existing baselines.Comment: Accepted by WWW 202

DisCo: Towards Harmonious Disentanglement and Collaboration between Tabular and Semantic Space for Recommendation

Recommender systems play important roles in various applications such as e-commerce, social media, etc. Conventional recommendation methods usually model the collaborative signals within the tabular representation space. Despite the personalization modeling and the efficiency, the latent semantic dependencies are omitted. Methods that introduce semantics into recommendation then emerge, injecting knowledge from the semantic representation space where the general language understanding are compressed. However, existing semantic-enhanced recommendation methods focus on aligning the two spaces, during which the representations of the two spaces tend to get close while the unique patterns are discarded and not well explored. In this paper, we propose DisCo to Disentangle the unique patterns from the two representation spaces and Collaborate the two spaces for recommendation enhancement, where both the specificity and the consistency of the two spaces are captured. Concretely, we propose 1) a dual-side attentive network to capture the intra-domain patterns and the inter-domain patterns, 2) a sufficiency constraint to preserve the task-relevant information of each representation space and filter out the noise, and 3) a disentanglement constraint to avoid the model from discarding the unique information. These modules strike a balance between disentanglement and collaboration of the two representation spaces to produce informative pattern vectors, which could serve as extra features and be appended to arbitrary recommendation backbones for enhancement. Experiment results validate the superiority of our method against different models and the compatibility of DisCo over different backbones. Various ablation studies and efficiency analysis are also conducted to justify each model component

Towards Open-World Recommendation with Knowledge Augmentation from Large Language Models

Recommender systems play a vital role in various online services. However, the insulated nature of training and deploying separately within a specific domain limits their access to open-world knowledge. Recently, the emergence of large language models (LLMs) has shown promise in bridging this gap by encoding extensive world knowledge and demonstrating reasoning capability. Nevertheless, previous attempts to directly use LLMs as recommenders have not achieved satisfactory results. In this work, we propose an Open-World Knowledge Augmented Recommendation Framework with Large Language Models, dubbed KAR, to acquire two types of external knowledge from LLMs -- the reasoning knowledge on user preferences and the factual knowledge on items. We introduce factorization prompting to elicit accurate reasoning on user preferences. The generated reasoning and factual knowledge are effectively transformed and condensed into augmented vectors by a hybrid-expert adaptor in order to be compatible with the recommendation task. The obtained vectors can then be directly used to enhance the performance of any recommendation model. We also ensure efficient inference by preprocessing and prestoring the knowledge from the LLM. Extensive experiments show that KAR significantly outperforms the state-of-the-art baselines and is compatible with a wide range of recommendation algorithms. We deploy KAR to Huawei's news and music recommendation platforms and gain a 7\% and 1.7\% improvement in the online A/B test, respectively