ÂÛÎÄÖܱ¨ | ÍƼöϵͳÁìÓò×îÐÂÑо¿½øÕ¹£¬º¬RecSys, SIGIR, CIKMµÈ¶¥»áÂÛÎÄ

±¾Îľ«Ñ¡ÁËÉÏÖÜ£¨0619-0625£©×îз¢²¼µÄ16ƪÍƼöϵͳÏà¹ØÂÛÎÄ£¬Ö÷ÒªÑо¿·½Ïò°üÀ¨¿É½âÊÍÍƼö¡¢¶àÈÎÎñÍƼö¡¢Éú³ÉʽÐòÁÐÍƼö¡¢»ù´¡Ä£Ð͸³ÄÜÍƼöϵͳ¡¢¶Ô»°ÍƼöϵͳ¡¢¹«Æ½ÐÔÍƼöϵͳÓë¶àÐÐΪÍƼöϵͳµÈ¡£

ÒÔÏÂÕûÀíÁËÂÛÎıêÌâÒÔ¼°ÕªÒª£¬Èç¸ÐÐËȤ¿ÉÒƲ½Ô­Îľ«¶Á¡£

1. Explainable Recommendation with Personalized Review Retrieval and Aspect Learning

2. STAN: Stage-Adaptive Network for Multi-Task Recommendation by Learning User Lifecycle-Based Representation, RecSys2023

3. Post-hoc Selection of Pareto-Optimal Solutions in Search and Recommendation

4. Addressing the Rank Degeneration in Sequential Recommendation via Singular Spectrum Smoothing

5. Mining Interest Trends and Adaptively Assigning Sample Weight for Session-based Recommendation, SIGIR2023

6. CAPRI: Context-Aware Interpretable Point-of-Interest Recommendation Framework, CIKM2023

7. OpenP5: Benchmarking Foundation Models for Recommendation

8. Generative Sequential Recommendation with GPTRec, SIGIR2023

9. AUGUST: an Automatic Generation Understudy for Synthesizing Conversational Recommendation Datasets

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

11. A Preliminary Study of ChatGPT on News Recommendation: Personalization, Provider Fairness, Fake News

12. MB-HGCN: A Hierarchical Graph Convolutional Network for Multi-behavior Recommendation

13. Personalized Elastic Embedding Learning for On-Device Recommendation

14. Interpolating Item and User Fairness in Recommendation Systems

15. Neighborhood-based Hard Negative Mining for Sequential Recommendation, SIGIR2023

16. Pseudo session-based recommendation with hierarchical embedding and session attributes

1. Explainable Recommendation with Personalized Review Retrieval and Aspect Learning

Hao Cheng, Shuo Wang, Wensheng Lu, Wei Zhang, Mingyang Zhou, Kezhong Lu, Hao Liao

https://arxiv.org/abs/2306.12657

Explainable recommendation is a technique that combines prediction and generation tasks to produce more persuasive results. Among these tasks, textual generation demands large amounts of data to achieve satisfactory accuracy. However, historical user reviews of items are often insufficient, making it challenging to ensure the precision of generated explanation text. To address this issue, we propose a novel model, ERRA (Explainable Recommendation by personalized Review retrieval and Aspect learning). With retrieval enhancement, ERRA can obtain additional information from the training sets. With this additional information, we can generate more accurate and informative explanations. Furthermore, to better capture users' preferences, we incorporate an aspect enhancement component into our model. By selecting the top-n aspects that users are most concerned about for different items, we can model user representation with more relevant details, making the explanation more persuasive. To verify the effectiveness of our model, extensive experiments on three datasets show that our model outperforms state-of-the-art baselines (for example, 3.4% improvement in prediction and 15.8% improvement in explanation for TripAdvisor).

2. STAN: Stage-Adaptive Network for Multi-Task Recommendation by Learning User Lifecycle-Based Representation, RecSys2023

Wanda Li, Wenhao Zheng, Xuanji Xiao, Suhang Wang

https://arxiv.org/abs/2306.12232

Recommendation systems play a vital role in many online platforms, with their primary objective being to satisfy and retain users. As directly optimizing user retention is challenging, multiple evaluation metrics are often employed. Existing methods generally formulate the optimization of these evaluation metrics as a multitask learning problem, but often overlook the fact that user preferences for different tasks are personalized and change over time. Identifying and tracking the evolution of user preferences can lead to better user retention. To address this issue, we introduce the concept of "user lifecycle", consisting of multiple stages characterized by users' varying preferences for different tasks. We propose a novel Stage-Adaptive Network (STAN) framework for modeling user lifecycle stages. STAN first identifies latent user lifecycle stages based on learned user preferences, and then employs the stage representation to enhance multi-task learning performance. Our experimental results using both public and industrial datasets demonstrate that the proposed model significantly improves multi-task prediction performance compared to state-of-the-art methods, highlighting the importance of considering user lifecycle stages in recommendation systems. Furthermore, online A/B testing reveals that our model outperforms the existing model, achieving a significant improvement of 3.05% in staytime per user and 0.88% in CVR. These results indicate that our approach effectively improves the overall efficiency of the multi-task recommendation system.

3. Post-hoc Selection of Pareto-Optimal Solutions in Search and Recommendation

Vincenzo Paparella, Vito Walter Anelli, Franco Maria Nardini, Raffaele Perego, Tommaso Di Noia

https://arxiv.org/abs/2306.12165

Information Retrieval (IR) and Recommender Systems (RS) tasks are moving from computing a ranking of final results based on a single metric to multi-objective problems. Solving these problems leads to a set of Pareto-optimal solutions, known as Pareto frontier, in which no objective can be further improved without hurting the others. In principle, all the points on the Pareto frontier are potential candidates to represent the best model selected with respect to the combination of two, or more, metrics. To our knowledge, there are no well-recognized strategies to decide which point should be selected on the frontier. In this paper, we propose a novel, post-hoc, theoretically-justified technique, named "Population Distance from Utopia" (PDU), to identify and select the one-best Pareto-optimal solution from the frontier. In detail, PDU analyzes the distribution of the points by investigating how far each point is from its utopia point (the ideal performance for the objectives). The possibility of considering fine-grained utopia points allows PDU to select solutions tailored to individual user preferences, a novel feature we call "calibration". We compare PDU against existing state-of-the-art strategies through extensive experiments on tasks from both IR and RS. Experimental results show that PDU and combined with calibration notably impact the solution selection. Furthermore, the results show that the proposed framework selects a solution in a principled way, irrespective of its position on the frontier, thus overcoming the limits of other strategies.

4. Addressing the Rank Degeneration in Sequential Recommendation via Singular Spectrum Smoothing

Ziwei Fan, Zhiwei Liu, Hao Peng, Philip S. Yu

https://arxiv.org/abs/2306.11986

Sequential recommendation (SR) investigates the dynamic user preferences modeling and generates the next-item prediction. The next item preference is typically generated by the affinity between the sequence and item representations. However, both sequence and item representations suffer from the rank degeneration issue due to the data sparsity problem. The rank degeneration issue significantly impairs the representations for SR. This motivates us to measure how severe is the rank degeneration issue and alleviate the sequence and item representation rank degeneration issues simultaneously for SR.

In this work, we theoretically connect the sequence representation degeneration issue with the item rank degeneration, particularly for short sequences and cold items. We also identify the connection between the fast singular value decay phenomenon and the rank collapse issue in transformer sequence output and item embeddings. We propose the area under the singular value curve metric to evaluate the severity of the singular value decay phenomenon and use it as an indicator of rank degeneration. We further introduce a novel singular spectrum smoothing regularization to alleviate the rank degeneration on both sequence and item sides, which is the Singular sPectrum sMoothing for sequential Recommendation (SPMRec). We also establish a correlation between the ranks of sequence and item embeddings and the rank of the user-item preference prediction matrix, which can affect recommendation diversity. We conduct experiments on four benchmark datasets to demonstrate the superiority of SPMRec over the state-of-the-art recommendation methods, especially in short sequences. The experiments also demonstrate a strong connection between our proposed singular spectrum smoothing and recommendation diversity. Project page: https://github.com/zfan20/SPMRec

5. Mining Interest Trends and Adaptively Assigning Sample Weight for Session-based Recommendation, SIGIR2023

Kai Ouyang, Xianghong Xu, Miaoxin Chen, Zuotong Xie, Hai-Tao Zheng, Shuangyong Song, Yu Zhao

https://arxiv.org/abs/2306.11610

Session-based Recommendation (SR) aims to predict users' next click based on their behavior within a short period, which is crucial for online platforms. However, most existing SR methods somewhat ignore the fact that user preference is not necessarily strongly related to the order of interactions. Moreover, they ignore the differences in importance between different samples, which limits the model-fitting performance. To tackle these issues, we put forward the method, Mining Interest Trends and Adaptively Assigning Sample Weight, abbreviated as MTAW. Specifically, we model users' instant interest based on their present behavior and all their previous behaviors. Meanwhile, we discriminatively integrate instant interests to capture the changing trend of user interest to make more personalized recommendations. Furthermore, we devise a novel loss function that dynamically weights the samples according to their prediction difficulty in the current epoch. Extensive experimental results on two benchmark datasets demonstrate the effectiveness and superiority of our method.

6. CAPRI: Context-Aware Interpretable Point-of-Interest Recommendation Framework, CIKM2023

Ali Tourani, Hossein A. Rahmani, Mohammadmehdi Naghiaei, Yashar Deldjoo

https://arxiv.org/abs/2306.11395

Point-of-Interest (POI ) recommendation systems have gained popularity for their unique ability to suggest geographical destinations with the incorporation of contextual information such as time, location, and user-item interaction. Existing recommendation frameworks lack the contextual fusion required for POI systems. This paper presents CAPRI, a novel POI recommendation framework that effectively integrates context-aware models, such as GeoSoCa, LORE, and USG, and introduces a novel strategy for the efficient merging of contextual information. CAPRI integrates an evaluation module that expands the evaluation scope beyond accuracy to include novelty, personalization, diversity, and fairness. With an aim to establish a new industry standard for reproducible results in the realm of POI recommendation systems, we have made CAPRI openly accessible on GitHub, facilitating easy access and contribution to the continued development and refinement of this innovative framework. Project page: https://github.com/CapriRecSys/CAPRI

7. OpenP5: Benchmarking Foundation Models for Recommendation

Shuyuan Xu, Wenyue Hua, Yongfeng Zhang

https://arxiv.org/abs/2306.11134

This paper presents OpenP5, an open-source library for benchmarking foundation models for recommendation under the Pre-train, Personalized Prompt and Predict Paradigm (P5). We consider the implementation of P5 on three dimensions: 1) downstream task, 2) recommendation dataset, and 3) item indexing method. For 1), we provide implementation over two downstream tasks: sequential recommendation and straightforward recommendation. For 2), we surveyed frequently used datasets in recommender system research in recent years and provide implementation on ten datasets. In particular, we provide both single-dataset implementation and the corresponding checkpoints (P5) and another Super P5 (SP5) implementation that is pre-trained on all of the datasets, which supports recommendation across various domains with one model. For 3), we provide implementation of three item indexing methods to create item IDs: random indexing, sequential indexing, and collaborative indexing. We also provide comprehensive evaluation results of the library over the two downstream tasks, the ten datasets, and the three item indexing methods to facilitate reproducibility and future research. We open-source the code and the pre-trained checkpoints of the OpenP5 library at https://github.com/agiresearch/OpenP5

8. Generative Sequential Recommendation with GPTRec, SIGIR2023

Aleksandr V. Petrov, Craig Macdonald

https://arxiv.org/abs/2306.11114

Sequential recommendation is an important recommendation task that aims to predict the next item in a sequence. Recently, adaptations of language models, particularly Transformer-based models such as SASRec and BERT4Rec, have achieved state-of-the-art results in sequential recommendation. In these models, item ids replace tokens in the original language models. However, this approach has limitations. First, the vocabulary of item ids may be many times larger than in language models. Second, the classical Top-K recommendation approach used by these models may not be optimal for complex recommendation objectives, including auxiliary objectives such as diversity, coverage or coherence. Recent progress in generative language models inspires us to revisit generative approaches to address these challenges. This paper presents the GPTRec sequential recommendation model, which is based on the GPT-2 architecture. GPTRec can address large vocabulary issues by splitting item ids into sub-id tokens using a novel SVD Tokenisation algorithm based on quantised item embeddings from an SVD decomposition of the user-item interaction matrix. The paper also presents a novel Next-K recommendation strategy, which generates recommendations item-by-item, considering already recommended items. The Next-K strategy can be used for producing complex interdependent recommendation lists. We experiment with GPTRec on the MovieLens-1M dataset and show that using sub-item tokenisation GPTRec can match the quality of SASRec while reducing the embedding table by 40%. We also show that the recommendations generated by GPTRec on MovieLens-1M using the Next-K recommendation strategy match the quality of SASRec in terms of NDCG@10, meaning that the model can serve as a strong starting point for future research.

9. AUGUST: an Automatic Generation Understudy for Synthesizing Conversational Recommendation Datasets

Yu Lu, Junwei Bao, Zichen Ma, Xiaoguang Han, Youzheng Wu, Shuguang Cui, Xiaodong He

https://arxiv.org/abs/2306.09631

High-quality data is essential for conversational recommendation systems and serves as the cornerstone of the network architecture development and training strategy design. Existing works contribute heavy human efforts to manually labeling or designing and extending recommender dialogue templates. However, they suffer from (i) the limited number of human annotators results in that datasets can hardly capture rich and large-scale cases in the real world, (ii) the limited experience and knowledge of annotators account for the uninformative corpus and inappropriate recommendations. In this paper, we propose a novel automatic dataset synthesis approach that can generate both large-scale and high-quality recommendation dialogues through a data2text generation process, where unstructured recommendation conversations are generated from structured graphs based on user-item information from the real world. In doing so, we comprehensively exploit: (i) rich personalized user profiles from traditional recommendation datasets, (ii) rich external knowledge from knowledge graphs, and (iii) the conversation ability contained in human-to-human conversational recommendation datasets. Extensive experiments validate the benefit brought by the automatically synthesized data under low-resource scenarios and demonstrate the promising potential to facilitate the development of a more effective conversational recommendation system. Project page: https://github.com/JD-AI-Research-NLP/AUGUST

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

Yunjia Xi, Weiwen Liu, Jianghao Lin, Jieming Zhu, Bo Chen, Ruiming Tang, Weinan Zhang, Rui Zhang, Yong Yu

https://arxiv.org/abs/2306.10933

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.

11. A Preliminary Study of ChatGPT on News Recommendation: Personalization, Provider Fairness, Fake News

Xinyi Li, Yongfeng Zhang, Edward C. Malthouse

https://arxiv.org/abs/2306.10702

Online news platforms commonly employ personalized news recommendation methods to assist users in discovering interesting articles, and many previous works have utilized language model techniques to capture user interests and understand news content. With the emergence of large language models like GPT-3 and T-5, a new recommendation paradigm has emerged, leveraging pre-trained language models for making recommendations. ChatGPT, with its user-friendly interface and growing popularity, has become a prominent choice for text-based tasks. Considering the growing reliance on ChatGPT for language tasks, the importance of news recommendation in addressing social issues, and the trend of using language models in recommendations, this study conducts an initial investigation of ChatGPT's performance in news recommendations, focusing on three perspectives: personalized news recommendation, news provider fairness, and fake news detection. ChatGPT has the limitation that its output is sensitive to the input phrasing. We therefore aim to explore the constraints present in the generated responses of ChatGPT for each perspective. Additionally, we investigate whether specific prompt formats can alleviate these constraints or if these limitations require further attention from researchers in the future. We also surpass fixed evaluations by developing a webpage to monitor ChatGPT's performance on weekly basis on the tasks and prompts we investigated. Our aim is to contribute to and encourage more researchers to engage in the study of enhancing news recommendation performance through the utilization of large language models such as ChatGPT. Project page: https://imrecommender.github.io/ChatNews

12. MB-HGCN: A Hierarchical Graph Convolutional Network for Multi-behavior Recommendation

Mingshi Yan, Zhiyong Cheng, Jing Sun, Fuming Sun, Yuxin Peng

https://arxiv.org/abs/2306.10679

Collaborative filtering-based recommender systems that rely on a single type of behavior often encounter serious sparsity issues in real-world applications, leading to unsatisfactory performance. Multi-behavior Recommendation (MBR) is a method that seeks to learn user preferences, represented as vector embeddings, from auxiliary information. By leveraging these preferences for target behavior recommendations, MBR addresses the sparsity problem and improves the accuracy of recommendations. In this paper, we propose MB-HGCN, a novel multi-behavior recommendation model that uses a hierarchical graph convolutional network to learn user and item embeddings from coarse-grained on the global level to fine-grained on the behavior-specific level. Our model learns global embeddings from a unified homogeneous graph constructed by the interactions of all behaviors, which are then used as initialized embeddings for behavior-specific embedding learning in each behavior graph. We also emphasize the distinct of the user and item behaviorspecific embeddings and design two simple-yet-effective strategies to aggregate the behavior-specific embeddings for users and items, respectively. Finally, we adopt multi-task learning for optimization. Extensive experimental results on three real-world datasets demonstrate that our model significantly outperforms the baselines, achieving a relative improvement of 73.93% and 74.21% for HR@10 and NDCG@10, respectively, on the Tmall datasets.

13. Personalized Elastic Embedding Learning for On-Device Recommendation

Ruiqi Zheng, Liang Qu, Tong Chen, Kai Zheng, Yuhui Shi, Hongzhi Yin

https://arxiv.org/abs/2306.10532

To address privacy concerns and reduce network latency, there has been a recent trend of compressing cumbersome recommendation models trained on the cloud and deploying compact recommender models to resource-limited devices for real-time recommendation. Existing solutions generally overlook device heterogeneity and user heterogeneity. They either require all devices to share the same compressed model or the devices with the same resource budget to share the same model. However, even users with the same devices may have different preferences. In addition, they assume the available resources (e.g., memory) for the recommender on a device are constant, which is not reflective of reality. In light of device and user heterogeneities as well as dynamic resource constraints, this paper proposes a Personalized Elastic Embedding Learning framework (PEEL) for on-device recommendation, which generates personalized embeddings for devices with various memory budgets in once-for-all manner, efficiently adapting to new or dynamic budgets, and effectively addressing user preference diversity by assigning personalized embeddings for different groups of users. Specifically, it pretrains using user-item interaction instances to generate the global embedding table and cluster users into groups. Then, it refines the embedding tables with local interaction instances within each group. Personalized elastic embedding is generated from the group-wise embedding blocks and their weights that indicate the contribution of each embedding block to the local recommendation performance. PEEL efficiently generates personalized elastic embeddings by selecting embedding blocks with the largest weights, making it adaptable to dynamic memory budgets. Extensive experiments are conducted on two public datasets, and the results show that PEEL yields superior performance on devices with heterogeneous and dynamic memory budgets.

14. Interpolating Item and User Fairness in Recommendation Systems

Qinyi Chen, Jason Cheuk Nam Liang, Negin Golrezaei, Djallel Bouneffouf

https://arxiv.org/abs/2306.10050

Online platforms employ recommendation systems to enhance customer engagement and drive revenue. However, in a multi-sided platform where the platform interacts with diverse stakeholders such as sellers (items) and customers (users), each with their own desired outcomes, finding an appropriate middle ground becomes a complex operational challenge. In this work, we investigate the ''price of fairness'', which captures the platform's potential compromises when balancing the interests of different stakeholders. Motivated by this, we propose a fair recommendation framework where the platform maximizes its revenue while interpolating between item and user fairness constraints. We further examine the fair recommendation problem in a more realistic yet challenging online setting, where the platform lacks knowledge of user preferences and can only observe binary purchase decisions. To address this, we design a low-regret online optimization algorithm that preserves the platform's revenue while achieving fairness for both items and users. Finally, we demonstrate the effectiveness of our framework and proposed method via a case study on MovieLens data.

15. Neighborhood-based Hard Negative Mining for Sequential Recommendation, SIGIR2023

Lu Fan, Jiashu Pu, Rongsheng Zhang, Xiao-Ming Wu

https://arxiv.org/abs/2306.10047

Negative sampling plays a crucial role in training successful sequential recommendation models. Instead of merely employing random negative sample selection, numerous strategies have been proposed to mine informative negative samples to enhance training and performance. However, few of these approaches utilize structural information. In this work, we observe that as training progresses, the distributions of node-pair similarities in different groups with varying degrees of neighborhood overlap change significantly, suggesting that item pairs in distinct groups may possess different negative relationships. Motivated by this observation, we propose a Graph-based Negative sampling approach based on Neighborhood Overlap (GNNO) to exploit structural information hidden in user behaviors for negative mining. GNNO first constructs a global weighted item transition graph using training sequences. Subsequently, it mines hard negative samples based on the degree of overlap with the target item on the graph. Furthermore, GNNO employs curriculum learning to control the hardness of negative samples, progressing from easy to difficult. Extensive experiments on three Amazon benchmarks demonstrate GNNO's effectiveness in consistently enhancing the performance of various state-of-the-art models and surpassing existing negative sampling strategies. The code will be released at https://github.com/floatSDSDS/GNNO

16. Pseudo session-based recommendation with hierarchical embedding and session attributes

Yuta Sumiya, Ryusei Numata, Satoshi Takahashi

https://arxiv.org/abs/2306.10029

Recently, electronic commerce (EC) websites have been unable to provide an identification number (user ID) for each transaction data entry because of privacy issues. Because most recommendation methods assume that all data are assigned a user ID, they cannot be applied to the data without user IDs. Recently, session-based recommendation (SBR) based on session information, which is short-term behavioral information of users, has been studied. A general SBR uses only information about the item of interest to make a recommendation (e.g., item ID for an EC site). Particularly in the case of EC sites, the data recorded include the name of the item being purchased, the price of the item, the category hierarchy, and the gender and region of the user. In this study, we define a pseudo--session for the purchase history data of an EC site without user IDs and session IDs. Finally, we propose an SBR with a co-guided heterogeneous hypergraph and global graph network plus, called CoHHGN+. The results show that our CoHHGN+ can recommend items with higher performance than other methods.