We describe the Universal Recommender, a recommender system for semantic datasets that generalizes domain-specific recommenders such as content-based, collaborative, social, bibliographic, lexicographic, hybrid and other recommenders. In contrast to existing recommender systems, the Universal Recommender applies to any dataset that allows a semantic representation. We describe the scalable three-stage architecture of the Universal Recommender and its application to Internet Protocol Television (IPTV). To achieve good recommendation accuracy, several novel machine learning and optimization problems are identified. We finally give a brief argument supporting the need for machine learning recommenders.
Recommender systems provide personalized recommendations to the users from a large number of possible options in online stores. Matrix factorization is a well-known and accurate collaborative filtering approach for recommender system, which suffers from cold-start problem for new users and items. Whenever a new user participate with the system there is not enough interactions with the system, therefore there are not enough ratings in the user-item matrix to learn the matrix factorization model. Using auxiliary data such as users demographic, ratings and reviews in relevant domains, is an effec…
Renmin University of China（中国人民大学）；Beijing Academy of Artificial Intelligence（北京人工智能研究院）；Alibaba Group（阿里巴巴集团）
In order to develop effective sequential recommenders, a series of sequence representation learning (SRL) methods are proposed to model historical user behaviors. Most existing SRL methods rely on explicit item IDs for developing the sequence models to better capture user preference. Though effective to some extent, these methods are difficult to be transferred to new recommendation scenarios, due to the limitation by explicitly modeling item IDs. To tackle this issue, we present a novel universal sequence representation learning approach, named UniSRec. The proposed approach utilizes the asso…
Recently, graph neural networks (GNNs) have been successfully applied to recommender systems. In recommender systems, the user's feedback behavior on an item is usually the result of multiple factors acting at the same time. However, a long-standing challenge is how to effectively aggregate multi-order interactions in GNN. In this paper, we propose a Graph Factorization Machine (GFM) which utilizes the popular Factorization Machine to aggregate multi-order interactions from neighborhood for recommendation. Meanwhile, cross-domain recommendation has emerged as a viable method to solve the data …
Performance of recommender systems (RS) relies heavily on the amount of training data available. This poses a chicken-and-egg problem for early-stage products, whose amount of data, in turn, relies on the performance of their RS. On the other hand, zero-shot learning promises some degree of generalization from an old dataset to an entirely new dataset. In this paper, we explore the possibility of zero-shot learning in RS. We develop an algorithm, dubbed ZEro-Shot Recommenders (ZESRec), that is trained on an old dataset and generalize to a new one where there are neither overlapping users nor o…
Advertising and Marketing Services, Corporate Development Group, Tencent Inc.（腾讯公司广告与营销服务部、企业发展集团）；zju.edu.cn（浙江大学（注：此为邮箱域名，非明确机构名，按要求不应提取，但原HTML中作者邮箱部分可能存在格式问题，根据提取范围，仅提取明确的机构名，此处原文本中唯一明确的机构是腾讯公司的部门，故仅保留该机构））
Factorization Machine (FM) is a widely used supervised learning approach by effectively modeling of feature interactions. Despite the successful application of FM and its many deep learning variants, treating every feature interaction fairly may degrade the performance. For example, the interactions of a useless feature may introduce noises; the importance of a feature may also differ when interacting with different features. In this work, we propose a novel model named \emph{Interaction-aware Factorization Machine} (IFM) by introducing Interaction-Aware Mechanism (IAM), which comprises the \e…
