We consider the problem of generating interpretable recommendations by identifying overlapping co-clusters of clients and products, based only on positive or implicit feedback. Our approach is applicable on very large datasets because it exhibits almost linear complexity in the input examples and the number of co-clusters. We show, both on real industrial data and on publicly available datasets, that the recommendation accuracy of our algorithm is competitive to that of state-of-art matrix factorization techniques. In addition, our technique has the advantage of offering recommendations that are textually and visually interpretable. Finally, we examine how to implement our technique efficiently on Graphical Processing Units (GPUs).
FedCRF: A Federated Cross-domain Recommendation Method with Semantic-driven Deep Knowledge Fusion
School of Computer and Artificial Intelligence, Shandong Normal University（山东师范大学计算机与人工智能学院）；Qingdao Institute of Software, China University of Petroleum (East China)（中国石油大学（华东）青岛软件研究院）；Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences)（齐鲁工业大学（山东省科学院））；Shandong Provincial Key Laboratory of Industrial Network and Information System Security, Shandong Fundamental Research Center for Computer Science（山东省计算机基础研究中心）；School of Computer Science and Technology, Ocean University of China（中国海洋大学计算机科学与技术学院）；School of Computing and Artificial Intelligence, Shandong University of Finance and Economics（山东财经大学计算与人工智能学院）
Product feature recommendations are critical for online customers to purchase the right products based on the right features. For a customer, selecting the product that has the best trade-off between price and functionality is a time-consuming step in an online shopping experience, and customers can be overwhelmed by the available choices. However, determining the set of product features that most differentiate a particular product is still an open question in online recommender systems. In this paper, we focus on using interpretable machine learning methods to tackle this problem. First, we i…
As a fundamental yet significant process in personalized recommendation, candidate generation and suggestion effectively help users spot the most suitable items for them. Consequently, identifying substitutable items that are interchangeable opens up new opportunities to refine the quality of generated candidates. When a user is browsing a specific type of product (e.g., a laptop) to buy, the accurate recommendation of substitutes (e.g., better equipped laptops) can offer the user more suitable options to choose from, thus substantially increasing the chance of a successful purchase. However, …
Deep learning-based recommender systems may lead to over-fitting when lacking training interaction data. This over-fitting significantly degrades recommendation performances. To address this data sparsity problem, cross-domain recommender systems (CDRSs) exploit the data from an auxiliary source domain to facilitate the recommendation on the sparse target domain. Most existing CDRSs rely on overlapping users or items to connect domains and transfer knowledge. However, matching users is an arduous task and may involve privacy issues when data comes from different companies, resulting in a limit…
University of Hyderabad（海得拉巴大学）；University of Delhi（德里大学）；Mahindra University（马恒达大学）
Recommender systems based on collaborative filtering play a vital role in many E-commerce applications as they guide the user in finding their items of interest based on the user's past transactions and feedback of other similar customers. Data Sparsity is one of the major drawbacks with collaborative filtering technique arising due to the less number of transactions and feedback data. In order to reduce the sparsity problem, techniques called transfer learning/cross-domain recommendation has emerged. In transfer learning methods, the data from other dense domain(s) (source) is considered in o…
School of Computer Science, Fudan University（复旦大学计算机学院）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；School of Electronics, Electrical Engineering and Computer Science, Queen’s University Belfast（贝尔法斯特女王大学电子、电气工程与计算机科学学院）
In this paper, we develop a neural attentive interpretable recommendation system, named NAIRS. A self-attention network, as a key component of the system, is designed to assign attention weights to interacted items of a user. This attention mechanism can distinguish the importance of the various interacted items in contributing to a user profile. Based on the user profiles obtained by the self-attention network, NAIRS offers personalized high-quality recommendation. Moreover, it develops visual cues to interpret recommendations. This demo application with the implementation of NAIRS enables us…
Pontificia Universidad Católica de Chile（智利天主教大学）；Millennium Institute Foundational Research on Data, IMFD（千年数据基础研究所）
The video game industry has adopted recommendation systems to boost users interest with a focus on game sales. Other exciting applications within video games are those that help the player make decisions that would maximize their playing experience, which is a desirable feature in real-time strategy video games such as Multiplayer Online Battle Arena (MOBA) like as DotA and LoL. Among these tasks, the recommendation of items is challenging, given both the contextual nature of the game and how it exposes the dependence on the formation of each team. Existing works on this topic do not take adva…
Recommender systems are used in many different applications and contexts, however their main goal can always be summarised as "connecting relevant content to interested users". Personalized recommendation algorithms achieve this goal by first building a profile of the user, either implicitly or explicitly, and then matching items with this profile to find relevant content. The more interpretable the profile and this "matching function" are, the easier it is to provide users with accurate and intuitive explanations, and also to let them interact with the system. Indeed, for a user to see what t…
Recent work has shown that in a dataset of user ratings on items there exists a group of Core Users who hold most of the information necessary for recommendation. This set of Core Users can be as small as 20 percent of the users. Core Users can be used to make predictions for out-of-sample users without much additional work. Since Core Users substantially shrink a ratings dataset without much loss of information, they can be used to improve recommendation efficiency. We propose a method, combining latent factor models, ensemble boosting and K-means clustering, to generate a small set of Artifi…
The tripartite graph is one of the commonest topological structures in social tagging systems such as Delicious, which has three types of nodes (i.e., users, URLs and tags). Traditional recommender systems developed based on collaborative filtering for the social tagging systems bring very high demands on CPU time cost. In this paper, to overcome this drawback, we propose a novel approach that extracts non-overlapping user clusters and corresponding overlapping item clusters simultaneously through coarse clustering to accelerate the user-based collaborative filtering and develop a fast recomme…
SAERec: Constructing Fine-grained Interpretable Intents Priors via Sparse Autoencoders for Recommendation
University of Georgia（佐治亚大学）；Shanghai AI Laboratory（上海人工智能实验室）；The Education University of Hong Kong（香港教育大学）；Jilin University（吉林大学）；The Hong Kong Polytechnic University（香港理工大学）
Model-based approaches to recommendation can recommend items with a very high level of accuracy. Unfortunately, even when the model embeds content-based information, if we move to a latent space we miss references to the actual semantics of recommended items. Consequently, this makes non-trivial the interpretation of a recommendation process. In this paper, we show how to initialize latent factors in Factorization Machines by using semantic features coming from a knowledge graph in order to train an interpretable model. With our model, semantic features are injected into the learning process t…
Recently, recommender systems have been able to emit substantially improved recommendations by leveraging user-provided reviews. Existing methods typically merge all reviews of a given user or item into a long document, and then process user and item documents in the same manner. In practice, however, these two sets of reviews are notably different: users' reviews reflect a variety of items that they have bought and are hence very heterogeneous in their topics, while an item's reviews pertain only to that single item and are thus topically homogeneous. In this work, we develop a novel neural n…
DISCO: A Hierarchical Disentangled Cognitive Diagnosis Framework for Interpretable Job Recommendation
School of Artificial Intelligence, Anhui University（安徽大学人工智能学院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；University of Science and Technology of China（中国科学技术大学）；Institutes of Physical Science and Information Technology, Anhui University（安徽大学物理与信息技术研究院）；School of Computer Science and Technology, Anhui University（安徽大学计算机科学与技术学院）；Computer Network Information Center, Chinese Academy of Sciences（中国科学院计算机网络信息中心）
An Interpretable Recommendation Model for Psychometric Data, With an Application to Gerontological Primary Care
Institute of Mathematics；Computer Science, University of S\ ao Paulo S\ ao Carlos Brazil；Department of Gerontology, S\ ao Carlos Federal University S\ ao Carlos Brazil；School of Arts, Sciences；Humanities, University of S\ ao Paulo S\ ao Paulo Brazil；Computer Science, University of S\ ao Paulo；Department of Gerontology, S\ ao Carlos Federal University；Humanities, University of S\ ao Paulo
Collaborative filtering is one of the most popular techniques in designing recommendation systems, and its most representative model, matrix factorization, has been wildly used by researchers and the industry. However, this model suffers from the lack of interpretability and the item cold-start problem, which limit its reliability and practicability. In this paper, we propose an interpretable recommendation model called Multi-Matrix Factorization (MMF), which addresses these two limitations and achieves the state-of-the-art prediction accuracy by exploiting common attributes that are present i…
With the increasing availability of videos, how to edit them and present the most interesting parts to users, i.e., video highlight, has become an urgent need with many broad applications. As users'visual preferences are subjective and vary from person to person, previous generalized video highlight extraction models fail to tailor to users' unique preferences. In this paper, we study the problem of personalized video highlight recommendation with rich visual content. By dividing each video into non-overlapping segments, we formulate the problem as a personalized segment recommendation task wi…
The problem of detecting terms that can be interesting to the advertiser is considered. If a company has already bought some advertising terms which describe certain services, it is reasonable to find out the terms bought by competing companies. A part of them can be recommended as future advertising terms to the company. The goal of this work is to propose better interpretable recommendations based on FCA and association rules.
The task of recommending items to a group of users, a.k.a. group recommendation, is receiving increasing attention. However, the cold-start problem inherent in recommender systems is amplified in group recommendation because interaction data between groups and items are extremely scarce in practice. Most existing work exploits associations between groups and items to mitigate the data scarcity problem. However, existing approaches inevitably fail in extreme cold-start scenarios where associations between groups and items are lacking. For this reason, we design a group recommendation model for …
From latent factors to language: a user study on LLM-generated explanations for an inherently interpretable matrix-based recommender system
Department of Technological Innovation Management, Faculty of Engineering, University of Mons (UMONS), Mons, Belgium（蒙斯大学工程学院技术创新管理系）；Department of Mathematics and Operational Research, Faculty of Engineering, University of Mons (UMONS), Mons, Belgium（蒙斯大学工程学院数学与运筹学系）；Department of Mathematics（数学系）；Operational Research, Faculty of Engineering, University of Mons (UMONS), Mons, Belgium（运筹学系）
Internet is changing the world, adapting to the trend of internet sales will bring revenue to traditional insurance companies. Online insurance is still in its early stages of development, where cold start problem (prospective customer) is one of the greatest challenges. In traditional e-commerce field, several cross-domain recommendation (CDR) methods have been studied to infer preferences of cold start users based on their preferences in other domains. However, these CDR methods could not be applied to insurance domain directly due to the domain specific properties. In this paper, we propose…
