Recent years have witnessed success of sequential modeling, generative recommender, and large language model for recommendation. Though the scaling law has been validated for sequential models, it showed inefficiency in computational capacity when considering real-world applications like recommendation, due to the non-linear(quadratic) increasing nature of the transformer model. To improve the efficiency of the sequential model, we introduced a novel approach to sequential recommendation that leverages personalization techniques to enhance efficiency and performance. Our method compresses long user interaction histories into learnable tokens, which are then combined with recent interactions to generate recommendations. This approach significantly reduces computational costs while maintaining high recommendation accuracy. Our method could be applied to existing transformer based recommendation models, e.g., HSTU and HLLM. Extensive experiments on multiple sequential models demonstrate its versatility and effectiveness. Source code is available at \href{https://github.com/facebookresearch/PerSRec}{https://github.com/facebookresearch/PerSRec}.
Towards Unified Multi-Modal Personalization: Large Vision-Language Models for Generative Recommendation and Beyond
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Amazon（亚马逊公司）；University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；University of Virginia（弗吉尼亚大学）；The Pennsylvania State University（宾夕法尼亚州立大学）
Exploring Applications of State Space Models and Advanced Training Techniques in Sequential Recommendations: A Comparative Study on Efficiency and Performance
We propose a method for generating simulated contextual bandit environments for personalization tasks from recommendation datasets like MovieLens, Netflix, Last.fm, Million Song, etc. This allows for personalization environments to be developed based on real-life data to reflect the nuanced nature of real-world user interactions. The obtained environments can be used to develop methods for solving personalization tasks, algorithm benchmarking, model simulation, and more. We demonstrate our approach with numerical examples on MovieLens and IMDb datasets.
University of Colorado, Boulder（科罗拉多大学博尔德分校）；University of Minnesota（明尼苏达大学）；eBay, Inc.（eBay公司）；University of Klagenfurt（克拉根福大学）；National Institute of Advanced Industrial Science and Technology（日本产业技术综合研究所）；Expedia Group（Expedia集团）；Accenture（埃森哲）
Recommender systems are personalized information access applications; they are ubiquitous in today's online environment, and effective at finding items that meet user needs and tastes. As the reach of recommender systems has extended, it has become apparent that the single-minded focus on the user common to academic research has obscured other important aspects of recommendation outcomes. Properties such as fairness, balance, profitability, and reciprocity are not captured by typical metrics for recommender system evaluation. The concept of multistakeholder recommendation has emerged as a unif…
Recommendation system has been widely used in different areas. Collaborative filtering focuses on rating, ignoring the features of items itself. In order to effectively evaluate customers preferences on books, taking into consideration of the characteristics of offline book retail, we use LDA model to calculate customers preference on book topics and use word2vec to calculate customers preference on book types. When forecasting rating on books, we take two factors into consideration: similarity of customers and correlation between customers and books. The experiment shows that our hybrid recom…
An effective content recommendation in modern social media platforms should benefit both creators to bring genuine benefits to them and consumers to help them get really interesting content. In this paper, we propose a model called Social Explorative Attention Network (SEAN) for content recommendation. SEAN uses a personalized content recommendation model to encourage personal interests driven recommendation. Moreover, SEAN allows the personalization factors to attend to users' higher-order friends on the social network to improve the accuracy and diversity of recommendation results. Construct…
With the advent of deep learning, neural network-based recommendation models have emerged as an important tool for tackling personalization and recommendation tasks. These networks differ significantly from other deep learning networks due to their need to handle categorical features and are not well studied or understood. In this paper, we develop a state-of-the-art deep learning recommendation model (DLRM) and provide its implementation in both PyTorch and Caffe2 frameworks. In addition, we design a specialized parallelization scheme utilizing model parallelism on the embedding tables to mit…
This paper presents a set of algorithms used for music recommendations and personalization in a general purpose social network www.ok.ru, the second largest social network in the CIS visited by more then 40 millions users per day. In addition to classical recommendation features like "recommend a sequence" and "find similar items" the paper describes novel algorithms for construction of context aware recommendations, personalization of the service, handling of the cold-start problem, and more. All algorithms described in the paper are working on-line and are able to detect and address changes …
Over the last few years, MaaS has been extensively studied and evolved into offering a multitude of mobility services that continuously increase, from alternative car or bike-sharing modes to autonomous vehicles, that aspire to become a part of this novel ecosystem. MaaS provides end-users with multimodal, integrated, and digital mobility solutions, including a multitude of different choices able to cover users specific needs in a personalized manner. This practically leads to a range of novel MaaS products, that may have complex structures and the challenge of matching them to user preference…
Conversational recommenders are emerging as a powerful tool to personalize a user's recommendation experience. Through a back-and-forth dialogue, users can quickly hone in on just the right items. Many approaches to conversational recommendation, however, only partially explore the user preference space and make limiting assumptions about how user feedback can be best incorporated, resulting in long dialogues and poor recommendation performance. In this paper, we propose a novel conversational recommendation framework with two unique features: (i) a greedy NDCG attribute selector, to enhance u…
Bringing GRACE to Recommendation: Fine-Tuning for Sustainable and Accurate Personalization
Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR) and School of Software, Shandong University（山东大学软件学院与联合山东大学-南洋理工大学人工智能研究中心 (C-FAIR)）；Alibaba-NTU Global e-Sustainability CorpLab (ANGEL), Nanyang Technological University（南洋理工大学阿里巴巴-南洋理工大学全球电子可持续发展公司实验室 (ANGEL)）
Personalized Recommendation Models in Federated Settings: A Survey
College of Computer Science and Technology, Jilin University（吉林大学计算机科学与技术学院）；School of Computer Science and Technology, Beijing Jiaotong University（北京交通大学计算机科学与技术学院）；Hong Kong University of Science and Technology（香港科技大学）；WeBank（微众银行）
In this paper we develop a novel recommendation model that explicitly incorporates time information. The model relies on an embedding layer and TSL attention-like mechanism with inner products in different vector spaces, that can be thought of as a modification of multi-headed attention. This mechanism allows the model to efficiently treat sequences of user behavior of different length. We study the properties of our state-of-the-art model on statistically designed data set. Also, we show that it outperforms more complex models with longer sequence length on the Taobao User Behavior dataset.
University of Southern California（南加州大学）；University College London（伦敦大学学院）；Shahid Beheshti University（沙希德·贝赫什提大学）
Recent studies have shown that recommendation systems commonly suffer from popularity bias. Popularity bias refers to the problem that popular items (i.e., frequently rated items) are recommended frequently while less popular items are recommended rarely or not at all. Researchers adopted two approaches to examining popularity bias: (i) from the users' perspective, by analyzing how far a recommendation system deviates from user's expectations in receiving popular items, and (ii) by analyzing the amount of exposure that long-tail items receive, measured by overall catalog coverage and novelty. …
Online gaming is growing faster than ever before, with increasing challenges of providing better user experience. Recommender systems (RS) for online games face unique challenges since they must fulfill players' distinct desires, at different user levels, based on their action sequences of various action types. Although many sequential RS already exist, they are mainly single-sequence, single-task, and single-user-level. In this paper, we introduce a new sequential recommendation model for multiple sequences, multiple tasks, and multiple user levels (abbreviated as M$^3$Rec) in Tencent Games p…
In recommendation systems, utilizing the user interaction history as sequential information has resulted in great performance improvement. However, in many online services, user interactions are commonly grouped by sessions that presumably share preferences, which requires a different approach from ordinary sequence representation techniques. To this end, sequence representation models with a hierarchical structure or various viewpoints have been developed but with a rather complex network structure. In this paper, we propose three methods to improve recommendation performance by exploiting se…
University of Electronic Science and Technology of China（电子科技大学）；University of Chinese Academy of Sciences（中国科学院大学）；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；Institute of Automation and Artificial Intelligence Research, Chinese Academy of Sciences（中国科学院自动化与人工智能研究院）
This paper explores meta-learning in sequential recommendation to alleviate the item cold-start problem. Sequential recommendation aims to capture user's dynamic preferences based on historical behavior sequences and acts as a key component of most online recommendation scenarios. However, most previous methods have trouble recommending cold-start items, which are prevalent in those scenarios. As there is generally no side information in the setting of sequential recommendation task, previous cold-start methods could not be applied when only user-item interactions are available. Thus, we propo…
Do We Care About Personalization and Explainability? An Interview Study with News Recommendation Engineers
University of Amsterdam（阿姆斯特丹大学）；Jheronimus Academy of Data Science（耶罗尼穆斯数据科学学院）；Birla Institute of Technology & Science, Pilani（比拉理工学院皮拉尼校区）；Eindhoven University of Technology（埃因霍温理工大学）
The sequential recommendation systems capture users' dynamic behavior patterns to predict their next interaction behaviors. Most existing sequential recommendation methods only exploit the local context information of an individual interaction sequence and learn model parameters solely based on the item prediction loss. Thus, they usually fail to learn appropriate sequence representations. This paper proposes a novel recommendation framework, namely Graph Contrastive Learning for Sequential Recommendation (GCL4SR). Specifically, GCL4SR employs a Weighted Item Transition Graph (WITG), built bas…
East China Normal University（华东师范大学）；Shanghai Jiao Tong University（上海交通大学）；Beijing Institute of Technology（北京理工大学）；Tsinghua University（清华大学）
Sequential Recommendation aims to recommend items that a target user will interact with in the near future based on the historically interacted items. While modeling temporal dynamics is crucial for sequential recommendation, most of the existing studies concentrate solely on the user side while overlooking the sequential patterns existing in the counterpart, i.e., the item side. Although a few studies investigate the dynamics involved in the dual sides, the complex user-item interactions are not fully exploited from a global perspective to derive dynamic user and item representations. In this…
University of Illinois at Chicago（伊利诺伊大学芝加哥分校）；Salesforce Research（Salesforce研究院）；UC San Diego（加州大学圣迭戈分校）
Sequential Recommendationdescribes a set of techniques to model dynamic user behavior in order to predict future interactions in sequential user data. At their core, such approaches model transition probabilities between items in a sequence, whether through Markov chains, recurrent networks, or more recently, Transformers. However both old and new issues remain, including data-sparsity and noisy data; such issues can impair the performance, especially in complex, parameter-hungry models. In this paper, we investigate the application of contrastive Self-Supervised Learning (SSL) to the sequenti…
We propose a novel software service recommendation model to help users find their suitable repositories in GitHub. Our model first designs a novel context-induced repository graph embedding method to leverage rich contextual information of repositories to alleviate the difficulties caused by the data sparsity issue. It then leverages sequence information of user-repository interactions for the first time in the software service recommendation field. Specifically, a deep-learning based sequential recommendation technique is adopted to capture the dynamics of user preferences. Comprehensive expe…
University of Technology Sydney（悉尼科技大学）；Tongji University（同济大学）；Wuhan University of Technology（武汉理工大学）；University of Illinois at Chicago（伊利诺伊大学芝加哥分校）
Recent advances in path-based explainable recommendation systems have attracted increasing attention thanks to the rich information provided by knowledge graphs. Most existing explainable recommendations only utilize static knowledge graphs and ignore the dynamic user-item evolutions, leading to less convincing and inaccurate explanations. Although there are some works that realize that modelling user's temporal sequential behaviour could boost the performance and explainability of the recommender systems, most of them either only focus on modelling user's sequential interactions within a path…
Growing evidence suggests that YouTube's recommendation algorithm plays a role in online radicalization via surfacing extreme content. Radical Islamist groups, in particular, have been profiting from the global appeal of YouTube to disseminate hate and jihadist propaganda. In this quantitative, data-driven study, we investigate the prevalence of religiously intolerant Arabic YouTube videos, the tendency of the platform to recommend such videos, and how these recommendations are affected by demographics and watch history. Based on our deep learning classifier developed to detect hateful videos …
