A Cross-Domain Recommender System With Kernel-Induced Knowledge Transfer for Overlapping Entities

Interaction-knowledge semantic alignment for recommendation

In order to alleviate the issue of data sparsity, knowledge graphs are introduced into recommender systems because they contain diverse information about items. The existing knowledge graph enhanced recommender systems utilize both user-item interaction data and knowledge graph, but those methods ignore the semantic difference between interaction data and knowledge graph. On the other hand, for the item representations obtained from two kinds of graph structure data respectively, the existing methods of fusing representations only consider the item representations themselves, without considering the personalized preference of users. In order to overcome the limitations mentioned above, we present a recommendation method named Interaction-Knowledge Semantic Alignment for Recommendation (IKSAR). By introducing a semantic alignment module, the semantic difference between the interaction bipartite graph and the knowledge graph is reduced. The representation of user is integrated during the fusion of representations of item, which improves the quality of the fused representation of item. To validate the efficacy of the proposed approach, we perform comprehensive experiments on three datasets. The experimental results demonstrate that the IKSAR is superior to the existing methods, showcasing notable improvement.

Master-Slave Deep Architecture for Top-K Multiarmed Bandits With Nonlinear Bandit Feedback and Diversity Constraints

We propose a novel master-slave architecture to solve the top- combinatorial multiarmed bandits (CMABs) problem with nonlinear bandit feedback and diversity constraints, which, to the best of our knowledge, is the first combinatorial bandits setting considering diversity constraints under bandit feedback. Specifically, to efficiently explore the combinatorial and constrained action space, we introduce six slave models with distinguished merits to generate diversified samples well balancing rewards and constraints as well as efficiency. Moreover, we propose teacher learning-based optimization and the policy cotraining technique to boost the performance of the multiple slave models. The master model then collects the elite samples provided by the slave models and selects the best sample estimated by a neural contextual UCB-based network (NeuralUCB) to decide on a tradeoff between exploration and exploitation. Thanks to the elaborate design of slave models, the cotraining mechanism among slave models, and the novel interactions between the master and slave models, our approach significantly surpasses existing state-of-the-art algorithms in both synthetic and real datasets for recommendation tasks. The code is available at https://github.com/huanghanchi/Master-slave-Algorithm-for-Top-K-Bandits.

CIPL: Counterfactual Interactive Policy Learning to Eliminate Popularity Bias for Online Recommendation

Popularity bias, as a long-standing problem in recommender systems (RSs), has been fully considered and explored for offline recommendation systems in most existing relevant researches, but very few studies have paid attention to eliminate such bias in online interactive recommendation scenarios. Bias amplification will become increasingly serious over time due to the existence of feedback loop between the user and the interactive system. However, existing methods have only investigated the causal relations among different factors statically without considering temporal dependencies inherent in the online interactive recommendation system, making them difficult to be adapted to online settings. To address these problems, we propose a novel counterfactual interactive policy learning (CIPL) method to eliminate popularity bias for online recommendation. It first scrutinizes the causal relations in the interactive recommender models and formulates a novel temporal causal graph (TCG) to guide the training and counterfactual inference of the causal interactive recommendation system. Concretely, TCG is used to estimate the causal relations of item popularity on prediction score when the user interacts with the system at each time during model training. Besides, it is also used to remove the negative effect of popularity bias in the test stage. To train the causal interactive recommendation system, we formulated our CIPL by the actor-critic framework with an online interactive environment simulator. We conduct extensive experiments on three public benchmarks and the experimental results demonstrate that our proposed method can achieve the new state-of-the-art performance.

Heterogeneous Multidomain Recommender System Through Adversarial Learning

To solve the user data sparsity problem, which is the main issue in generating user preference prediction, cross-domain recommender systems transfer knowledge from one source domain with dense data to assist recommendation tasks in the target domain with sparse data. However, data are usually sparsely scattered in multiple possible source domains, and in each domain (source/target) the data may be heterogeneous, thus it is difficult for existing cross-domain recommender systems to find one source domain with dense data from multiple domains. In this way, they fail to deal with data sparsity problems in the target domain and cannot provide an accurate recommendation. In this article, we propose a novel multidomain recommender system (called HMRec) to deal with two challenging issues: 1) how to exploit valuable information from multiple source domains when no single source domain is sufficient and 2) how to ensure positive transfer from heterogeneous data in source domains with different feature spaces. In HMRec, domain-shared and domain-specific features are extracted to enable the knowledge transfer between multiple heterogeneous source and target domains. To ensure positive transfer, the domain-shared subspaces from multiple domains are maximally matched by a multiclass domain discriminator in an adversarial learning process. The recommendation in the target domain is completed by a matrix factorization module with aligned latent features from both the user and the item side. Extensive experiments on four cross-domain recommendation tasks with real-world datasets demonstrate that HMRec can effectively transfer knowledge from multiple heterogeneous domains collaboratively to increase the rating prediction accuracy in the target domain and significantly outperforms six state-of-the-art non-transfer or cross-domain baselines.

A Survey on Cross-domain Recommendation: Taxonomies, Methods, and Future Directions

Traditional recommendation systems are faced with two long-standing obstacles, namely, data sparsity and cold-start problems, which promote the emergence and development of Cross-Domain Recommendation (CDR). The core idea of CDR is to leverage information collected from other domains to alleviate the two problems in one domain. Over the last decade, many efforts have been engaged for cross-domain recommendation. Recently, with the development of deep learning and neural networks, a large number of methods have emerged. However, there is a limited number of systematic surveys on CDR, especially regarding the latest proposed methods as well as the recommendation scenarios and recommendation tasks they address. In this survey paper, we first proposed a two-level taxonomy of cross-domain recommendation which classifies different recommendation scenarios and recommendation tasks. We then introduce and summarize existing cross-domain recommendation approaches under different recommendation scenarios in a structured manner. We also organize datasets commonly used. We conclude this survey by providing several potential research directions about this field.

Online Unsupervised Domain Adaptation via Reducing Inter- and Intra-Domain Discrepancies

Unsupervised domain adaptation (UDA) transfers knowledge from a labeled source domain to an unlabeled target domain on cross-domain object recognition by reducing a distribution discrepancy between the source and target domains (interdomain discrepancy). Prevailing methods on UDA were presented based on the premise that target data are collected in advance. However, in online scenarios, the target data often arrive in a streamed manner, such as visual image recognition in daily monitoring, which means that there is a distribution discrepancy between incoming target data and collected target data (intradomain discrepancy). Consequently, most existing methods need to re-adapt the incoming data and retrain a new model on online data. This paradigm is difficult to meet the real-time requirements of online tasks. In this study, we propose an online UDA framework via jointly reducing interdomain and intradomain discrepancies on cross-domain object recognition where target data arrive in a streamed manner. Specifically, the proposed framework comprises two phases: classifier training and online recognition phases. In the former, we propose training a classifier on a shared subspace where there is a lower interdomain discrepancy between the two domains. In the latter, a low-rank subspace alignment method is introduced to adapt incoming data to the shared subspace by reducing the intradomain discrepancy. Finally, online recognition results can be obtained by the trained classifier. Extensive experiments on DA benchmarks and real-world datasets are employed to evaluate the performance of the proposed framework in online scenarios. The experimental results show the superiority of the proposed framework in online recognition tasks.

A hybrid group-based movie recommendation framework with overlapping memberships

Recommender Systems (RS) are widely used to help people or group of people in finding their required information amid the issue of ever-growing information overload. The existing group recommender approaches consider users to be part of a single group only, but in real life a user may be associated with multiple groups having conflicting preferences. For instance, a person may have different preferences in watching movies with friends than with family. In this paper, we address this problem by proposing a Hybrid Two-phase Group Recommender Framework (HTGF) that takes into consideration the possibility of users having simultaneous membership of multiple groups. Unlike the existing group recommender systems that use traditional methods like K-Means, Pearson correlation, and cosine similarity to form groups, we use Fuzzy C-means clustering which assigns a degree of membership to each user for each group, and then Pearson similarity is used to form groups. We demonstrate the usefulness of our proposed framework using a movies data set. The experiments were conducted on MovieLens 1M dataset where we used Neural Collaborative Filtering to recommend Top-k movies to each group. The results demonstrate that our proposed framework outperforms the traditional approaches when compared in terms of group satisfaction parameters, as well as the conventional metrics of precision, recall, and F-measure.

Evolutionary Social Poisson Factorizationfor Temporal Recommendation

Preference-based recommendation systems analyze user-item interactions to reveal latent factors that explain our latent preferences for items and form personalized recommendations based on the behavior of others with similar tastes. Most of the works in the recommendation systems literature have been developed under the assumption that user preference is a static pattern, although user preferences and item attributes may be changed through time. To achieve this goal, we develop an Evolutionary Social Poisson Factorization (EPF$$\_$$ _ Social) model, a new Bayesian factorization model that can effectively model the smoothly drifting latent factors using Conjugate Gamma–Markov chains. Otherwise, EPF$$\_$$ _ Social can obtain the impact of friends on social network for user’ latent preferences. We studied our models with two large real-world datasets, and demonstrated that our model gives better predictive performance than state-of-the-art static factorization models.

CAME: Content- and Context-Aware Music Embedding for Recommendation

Traditional recommendation methods suffer from limited performance, which can be addressed by incorporating abundant auxiliary/side information. This article focuses on a personalized music recommender system that incorporates rich content and context data in a unified and adaptive way to address the abovementioned problems. The content information includes music textual content, such as metadata, tags, and lyrics, and the context data incorporate users' behaviors, including music listening records, music playing sequences, and sessions. Specifically, a heterogeneous information network (HIN) is first presented to incorporate different kinds of content and context data. Then, a novel method called content- and context-aware music embedding (CAME) is proposed to obtain the low-dimension dense real-valued feature representations (embeddings) of music pieces from HIN. Especially, one music piece generally highlights different aspects when interacting with various neighbors, and it should have different representations separately. CAME seamlessly combines deep learning techniques, including convolutional neural networks and attention mechanisms, with the embedding model to capture the intrinsic features of music pieces as well as their dynamic relevance and interactions adaptively. Finally, we further infer users' general musical preferences as well as their contextual preferences for music and propose a content- and context-aware music recommendation method. Comprehensive experiments as well as quantitative and qualitative evaluations have been performed on real-world music data sets, and the results show that the proposed recommendation approach outperforms state-of-the-art baselines and is able to handle sparse data effectively.

Artificial intelligence in recommender systems

Recommender systems provide personalized service support to users by learning their previous behaviors and predicting their current preferences for particular products. Artificial intelligence (AI), particularly computational intelligence and machine learning methods and algorithms, has been naturally applied in the development of recommender systems to improve prediction accuracy and solve data sparsity and cold start problems. This position paper systematically discusses the basic methodologies and prevailing techniques in recommender systems and how AI can effectively improve the technological development and application of recommender systems. The paper not only reviews cutting-edge theoretical and practical contributions, but also identifies current research issues and indicates new research directions. It carefully surveys various issues related to recommender systems that use AI, and also reviews the improvements made to these systems through the use of such AI approaches as fuzzy techniques, transfer learning, genetic algorithms, evolutionary algorithms, neural networks and deep learning, and active learning. The observations in this paper will directly support researchers and professionals to better understand current developments and new directions in the field of recommender systems using AI.

Implicit Stochastic Gradient Descent Method for Cross-Domain Recommendation System

The previous recommendation system applied the matrix factorization collaborative filtering (MFCF) technique to only single domains. Due to data sparsity, this approach has a limitation in overcoming the cold-start problem. Thus, in this study, we focus on discovering latent features from domains to understand the relationships between domains (called domain coherence). This approach uses potential knowledge of the source domain to improve the quality of the target domain recommendation. In this paper, we consider applying MFCF to multiple domains. Mainly, by adopting the implicit stochastic gradient descent algorithm to optimize the objective function for prediction, multiple matrices from different domains are consolidated inside the cross-domain recommendation system (CDRS). Additionally, we design a conceptual framework for CDRS, which applies to different industrial scenarios for recommenders across domains. Moreover, an experiment is devised to validate the proposed method. By using a real-world dataset gathered from Amazon Food and MovieLens, experimental results show that the proposed method improves 15.2% and 19.7% in terms of computation time and MSE over other methods on a utility matrix. Notably, a much lower convergence value of the loss function has been obtained from the experiment. Furthermore, a critical analysis of the obtained results shows that there is a dynamic balance between prediction accuracy and computational complexity.