A Deep Framework for Cross-Domain and Cross-System Recommendations

Cross-Domain Recommendation (CDR) and Cross-System Recommendations (CSR) are two of the promising solutions to address the long-standing data sparsity problem in recommender systems. They leverage the relatively richer information, e.g., ratings, from the source domain or system to improve the recommendation accuracy in the target domain or system. Therefore, finding an accurate mapping of the latent factors across domains or systems is crucial to enhancing recommendation accuracy. However, this is a very challenging task because of the complex relationships between the latent factors of the source and target domains or systems. To this end, in this paper, we propose a Deep framework for both Cross-Domain and Cross-System Recommendations, called DCDCSR, based on Matrix Factorization (MF) models and a fully connected Deep Neural Network (DNN). Specifically, DCDCSR first employs the MF models to generate user and item latent factors and then employs the DNN to map the latent factors across domains or systems. More importantly, we take into account the rating sparsity degrees of individual users and items in different domains or systems and use them to guide the DNN training process for utilizing the rating data more effectively. Extensive experiments conducted on three real-world datasets demonstrate that DCDCSR framework outperforms the state-of-the-art CDR and CSR approaches in terms of recommendation accuracy.

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A Graphical and Attentional Framework for Dual-Target Cross-Domain Recommendation

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2020/415 ◽

2020 ◽

Author(s):

Feng Zhu ◽

Yan Wang ◽

Chaochao Chen ◽

Guanfeng Liu ◽

Xiaolin Zheng

Keyword(s):

State Of The Art ◽

The Novel ◽

Target Domain ◽

Cross Domain ◽

Single Target ◽

Recommendation Accuracy ◽

Real World Datasets ◽

Content Information ◽

Dual Target

The conventional single-target Cross-Domain Recommendation (CDR) only improves the recommendation accuracy on a target domain with the help of a source domain (with relatively richer information). In contrast, the novel dual-target CDR has been proposed to improve the recommendation accuracies on both domains simultaneously. However, dual-target CDR faces two new challenges: (1) how to generate more representative user and item embeddings, and (2) how to effectively optimize the user/item embeddings on each domain. To address these challenges, in this paper, we propose a graphical and attentional framework, called GA-DTCDR. In GA-DTCDR, we first construct two separate heterogeneous graphs based on the rating and content information from two domains to generate more representative user and item embeddings. Then, we propose an element-wise attention mechanism to effectively combine the embeddings of common users learned from both domains. Both steps significantly enhance the quality of user and item embeddings and thus improve the recommendation accuracy on each domain. Extensive experiments conducted on four real-world datasets demonstrate that GA-DTCDR significantly outperforms the state-of-the-art approaches.

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IMPROVING LATENT FACTOR MODEL BASED COLLABORATIVE FILTERING VIA INTEGRATED FOLKSONOMY FACTORS

International Journal of Uncertainty Fuzziness and Knowledge-Based Systems ◽

10.1142/s0218488511007015 ◽

2011 ◽

Vol 19 (02) ◽

pp. 307-327 ◽

Cited By ~ 5

Author(s):

LUO XIN ◽

YUANXIN OUYANG ◽

XIONG ZHANG

Keyword(s):

Collaborative Filtering ◽

Matrix Factorization ◽

Factor Model ◽

State Of The Art ◽

Latent Factors ◽

Latent Factor ◽

Rating Data ◽

Latent Factor Model ◽

Probabilistic Matrix Factorization ◽

Recommendation Accuracy

Latent Factor Model (LFM) based approaches are becoming popular when implementing Collaborative Filtering (CF) recommenders, due to their high recommendation accuracy. However, current LFM approaches address the accuracy issue only based on the rating data, whereas early research indicates that integrating information from additional data sources is helpful to the recommendation accuracy. In this work we focus on improving the recommendation accuracy of a LFM based CF recommender by integrating folksonomy information. To implement this approach, we first propose a novel model named Item Folsonomy Relevance (IFR) to analyze the item relevance inside the folksonomy; we subsequently integrate the latent factors of the IFR model and rating data through probabilistic matrix factorization (PMF), a state-of-the-art matrix factorization technique, to produce recommendations based on information from both the ratings and folksonomy simultaneously. The experiments on MovieLens dataset showed that compared to two state-of-the-art LFM approaches and another folksonomy-augmented recommder, our approach could obtain advantage in recommendation accuracy.

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Deeply Fusing Reviews and Contents for Cold Start Users in Cross-Domain Recommendation Systems

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.330194 ◽

2019 ◽

Vol 33 ◽

pp. 94-101 ◽

Cited By ~ 9

Author(s):

Wenjing Fu ◽

Zhaohui Peng ◽

Senzhang Wang ◽

Yang Xu ◽

Jin Li

Keyword(s):

Semantic Information ◽

Side Information ◽

Recommendation Systems ◽

Cold Start ◽

Superior Performance ◽

Fusion Model ◽

Target Domain ◽

Latent Factors ◽

Data Sparsity ◽

Cross Domain

As one promising way to solve the challenging issues of data sparsity and cold start in recommender systems, crossdomain recommendation has gained increasing research interest recently. Cross-domain recommendation aims to improve the recommendation performance by means of transferring explicit or implicit feedback from the auxiliary domain to the target domain. Although the side information of review texts and item contents has been proven to be useful in recommendation, most existing works only use one kind of side information and cannot deeply fuse this side information with ratings. In this paper, we propose a Review and Content based Deep Fusion Model named RC-DFM for crossdomain recommendation. We first extend Stacked Denoising Autoencoders (SDAE) to effectively fuse review texts and item contents with the rating matrix in both auxiliary and target domains. Through this way, the learned latent factors of users and items in both domains preserve more semantic information for recommendation. Then we utilize a multi-layer perceptron to transfer user latent factors between the two domains to address the data sparsity and cold start issues. Experimental results on real datasets demonstrate the superior performance of RC-DFM compared with state-of-the-art recommendation methods.Deeply Fusing Reviews and Contents for Cold Start Users in Cross-Domain Recommendation Systems

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Distributional Correspondence Indexing for Cross-Lingual and Cross-Domain Sentiment Classification (Extended Abstract)

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2018/802 ◽

2018 ◽

Author(s):

Alejandro Moreo Fernández ◽

Andrea Esuli ◽

Fabrizio Sebastiani

Keyword(s):

Domain Adaptation ◽

State Of The Art ◽

Sentiment Classification ◽

Training Data ◽

Target Domain ◽

Source Domain ◽

Machine Learning Methods ◽

Cross Domain ◽

Current State ◽

Cross Lingual

Domain Adaptation (DA) techniques aim at enabling machine learning methods learn effective classifiers for a “target” domain when the only available training data belongs to a different “source” domain. In this extended abstract, we briefly describe our new DA method called Distributional Correspondence Indexing (DCI) for sentiment classification. DCI derives term representations in a vector space common to both domains where each dimension reflects its distributional correspondence to a pivot, i.e., to a highly predictive term that behaves similarly across domains. The experiments we have conducted show that DCI obtains better performance than current state-of-the-art techniques for cross-lingual and cross-domain sentiment classification.

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Distributional Correspondence Indexing for Cross-Lingual and Cross-Domain Sentiment Classification.

Journal of Artificial Intelligence Research ◽

10.1613/jair.4762 ◽

2016 ◽

Vol 55 ◽

pp. 131-163 ◽

Cited By ~ 13

Author(s):

Alejandro Moreo Fernández ◽

Andrea Esuli ◽

Fabrizio Sebastiani

Keyword(s):

Domain Adaptation ◽

State Of The Art ◽

Computational Cost ◽

Sentiment Classification ◽

Training Data ◽

Target Domain ◽

Machine Learning Methods ◽

Cross Domain ◽

Current State ◽

Cross Lingual

Domain Adaptation (DA) techniques aim at enabling machine learning methods learn effective classifiers for a "target'' domain when the only available training data belongs to a different "source'' domain. In this paper we present the Distributional Correspondence Indexing (DCI) method for domain adaptation in sentiment classification. DCI derives term representations in a vector space common to both domains where each dimension reflects its distributional correspondence to a pivot, i.e., to a highly predictive term that behaves similarly across domains. Term correspondence is quantified by means of a distributional correspondence function (DCF). We propose a number of efficient DCFs that are motivated by the distributional hypothesis, i.e., the hypothesis according to which terms with similar meaning tend to have similar distributions in text. Experiments show that DCI obtains better performance than current state-of-the-art techniques for cross-lingual and cross-domain sentiment classification. DCI also brings about a significantly reduced computational cost, and requires a smaller amount of human intervention. As a final contribution, we discuss a more challenging formulation of the domain adaptation problem, in which both the cross-domain and cross-lingual dimensions are tackled simultaneously.

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Unsupervised multi-source domain adaptation with no observable source data

PLoS ONE ◽

10.1371/journal.pone.0253415 ◽

2021 ◽

Vol 16 (7) ◽

pp. e0253415

Author(s):

Hyunsik Jeon ◽

Seongmin Lee ◽

U Kang

Keyword(s):

Real World ◽

Domain Adaptation ◽

State Of The Art ◽

Multiple Source ◽

Multiple Sources ◽

Target Domain ◽

Source Domain ◽

Source Data ◽

Real World Datasets ◽

Target Data

Given trained models from multiple source domains, how can we predict the labels of unlabeled data in a target domain? Unsupervised multi-source domain adaptation (UMDA) aims for predicting the labels of unlabeled target data by transferring the knowledge of multiple source domains. UMDA is a crucial problem in many real-world scenarios where no labeled target data are available. Previous approaches in UMDA assume that data are observable over all domains. However, source data are not easily accessible due to privacy or confidentiality issues in a lot of practical scenarios, although classifiers learned in source domains are readily available. In this work, we target data-free UMDA where source data are not observable at all, a novel problem that has not been studied before despite being very realistic and crucial. To solve data-free UMDA, we propose DEMS (Data-free Exploitation of Multiple Sources), a novel architecture that adapts target data to source domains without exploiting any source data, and estimates the target labels by exploiting pre-trained source classifiers. Extensive experiments for data-free UMDA on real-world datasets show that DEMS provides the state-of-the-art accuracy which is up to 27.5% point higher than that of the best baseline.

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Contrastive Trajectory Learning for Tour Recommendation

ACM Transactions on Intelligent Systems and Technology ◽

10.1145/3462331 ◽

2022 ◽

Vol 13 (1) ◽

pp. 1-25

Author(s):

Fan Zhou ◽

Pengyu Wang ◽

Xovee Xu ◽

Wenxin Tai ◽

Goce Trajcevski

Keyword(s):

Data Augmentation ◽

State Of The Art ◽

Implicit Feedback ◽

Data Sparsity ◽

Weak Supervision ◽

Point Of Interest ◽

Data Correlations ◽

Recommendation Accuracy ◽

Real World Datasets ◽

Trajectory Learning

The main objective of Personalized Tour Recommendation (PTR) is to generate a sequence of point-of-interest (POIs) for a particular tourist, according to the user-specific constraints such as duration time, start and end points, the number of attractions planned to visit, and so on. Previous PTR solutions are based on either heuristics for solving the orienteering problem to maximize a global reward with a specified budget or approaches attempting to learn user visiting preferences and transition patterns with the stochastic process or recurrent neural networks. However, existing learning methodologies rely on historical trips to train the model and use the next visited POI as the supervised signal, which may not fully capture the coherence of preferences and thus recommend similar trips to different users, primarily due to the data sparsity problem and long-tailed distribution of POI popularity. This work presents a novel tour recommendation model by distilling knowledge and supervision signals from the trips in a self-supervised manner. We propose Contrastive Trajectory Learning for Tour Recommendation (CTLTR), which utilizes the intrinsic POI dependencies and traveling intent to discover extra knowledge and augments the sparse data via pre-training auxiliary self-supervised objectives. CTLTR provides a principled way to characterize the inherent data correlations while tackling the implicit feedback and weak supervision problems by learning robust representations applicable for tour planning. We introduce a hierarchical recurrent encoder-decoder to identify tourists’ intentions and use the contrastive loss to discover subsequence semantics and their sequential patterns through maximizing the mutual information. Additionally, we observe that a data augmentation step as the preliminary of contrastive learning can solve the overfitting issue resulting from data sparsity. We conduct extensive experiments on a range of real-world datasets and demonstrate that our model can significantly improve the recommendation performance over the state-of-the-art baselines in terms of both recommendation accuracy and visiting orders.

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Cross-Domain Recommendation via Coupled Factorization Machines

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.33019965 ◽

2019 ◽

Vol 33 ◽

pp. 9965-9966 ◽

Cited By ~ 1

Author(s):

Lile Li ◽

Quan Do ◽

Wei Liu

Keyword(s):

Recommender Systems ◽

Real World ◽

Original Data ◽

Data Sets ◽

Latent Factors ◽

Domain Specific ◽

Coupled Mode ◽

Cross Domain ◽

Factorization Machine ◽

Real World Datasets

Data across many business domains can be represented by two or more coupled data sets. Correlations among these coupled datasets have been studied in the literature for making more accurate cross-domain recommender systems. However, in existing methods, cross-domain recommendations mostly assume the coupled mode of data sets share identical latent factors, which limits the discovery of potentially useful domain-specific properties of the original data. In this paper, we proposed a novel cross-domain recommendation method called Coupled Factorization Machine (CoFM) that addresses this limitation. Compared to existing models, our research is the first model that uses factorization machines to capture both common characteristics of coupled domains while simultaneously preserving the differences among them. Our experiments with real-world datasets confirm the advantages of our method in making across-domain recommendations.

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Novel Collaborative Filtering Recommender Friendly to Privacy Protection

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2019/668 ◽

2019 ◽

Cited By ~ 1

Author(s):

Jun Wang ◽

Qiang Tang ◽

Afonso Arriaga ◽

Peter Y. A. Ryan

Keyword(s):

Privacy Protection ◽

State Of The Art ◽

Research Question ◽

Huge Amount ◽

Recommendation Algorithm ◽

Recommendation Algorithms ◽

Very Large Datasets ◽

Recommendation Accuracy ◽

Real World Datasets ◽

Indispensable Tool

Nowadays, recommender system is an indispensable tool in many information services, and a large number of algorithms have been designed and implemented. However, fed with very large datasets, state-of-the-art recommendation algorithms often face an efficiency bottleneck, i.e., it takes huge amount of computing resources to train a recommendation model. In order to satisfy the needs of privacy-savvy users who do not want to disclose their information to the service provider, the complexity of most existing solutions becomes prohibitive. As such, it is an interesting research question to design simple and efficient recommendation algorithms that achieve reasonable accuracy and facilitate privacy protection at the same time. In this paper, we propose an efficient recommendation algorithm, named CryptoRec, which has two nice properties: (1) can estimate a new user's preferences by directly using a model pre-learned from an expert dataset, and the new user's data is not required to train the model; (2) can compute recommendations with only addition and multiplication operations. As to the evaluation, we first test the recommendation accuracy on three real-world datasets and show that CryptoRec is competitive with state-of-the-art recommenders. Then, we evaluate the performance of the privacy-preserving variants of CryptoRec and show that predictions can be computed in seconds on a PC. In contrast, existing solutions will need tens or hundreds of hours on more powerful computers.

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Cross-Domain Text Sentiment Analysis Based on CNN_FT Method

Information ◽

10.3390/info10050162 ◽

2019 ◽

Vol 10 (5) ◽

pp. 162 ◽

Cited By ~ 4

Author(s):

Jiana Meng ◽

Yingchun Long ◽

Yuhai Yu ◽

Dandan Zhao ◽

Shuang Liu

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Transfer Learning ◽

Target Domain ◽

Data Set ◽

Source Domain ◽

Cross Domain ◽

The Cross ◽

Fully Connected ◽

Text Sentiment Analysis

Transfer learning is one of the popular methods for solving the problem that the models built on the source domain cannot be directly applied to the target domain in the cross-domain sentiment classification. This paper proposes a transfer learning method based on the multi-layer convolutional neural network (CNN). Interestingly, we construct a convolutional neural network model to extract features from the source domain and share the weights in the convolutional layer and the pooling layer between the source and target domain samples. Next, we fine-tune the weights in the last layer, named the fully connected layer, and transfer the models from the source domain to the target domain. Comparing with the classical transfer learning methods, the method proposed in this paper does not need to retrain the network for the target domain. The experimental evaluation of the cross-domain data set shows that the proposed method achieves a relatively good performance.

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