scholarly journals Cross-Domain Recommendation via Coupled Factorization Machines

2019 ◽  
Vol 33 ◽  
pp. 9965-9966 ◽  
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.

scholarly journals DA-GCN: A Domain-aware Attentive Graph Convolution Network for Shared-account Cross-domain Sequential Recommendation

2021 ◽  
Author(s):  
Lei Guo ◽  
Li Tang ◽  
Tong Chen ◽  
Lei Zhu ◽  
Quoc Viet Hung Nguyen ◽  
...  
Keyword(s):  
Real World ◽  
Message Passing ◽  
Sequential Patterns ◽  
Graph Structure ◽  
Domain Specific ◽  
User Behaviors ◽  
Cross Domain ◽  
Latent Space ◽  
Real World Datasets ◽  
Multiple Domains

Shared-account Cross-domain Sequential Recommendation (SCSR) is the task of recommending the next item based on a sequence of recorded user behaviors, where multiple users share a single account, and their behaviours are available in multiple domains. Existing work on solving SCSR mainly relies on mining sequential patterns via RNN-based models, which are not expressive enough to capture the relationships among multiple entities. Moreover, all existing algorithms try to bridge two domains via knowledge transfer in the latent space, and the explicit cross-domain graph structure is unexploited. In this work, we propose a novel graph-based solution, namely DA-GCN, to address the above challenges. Specifically, we first link users and items in each domain as a graph. Then, we devise a domain-aware graph convolution network to learn user-specific node representations. To fully account for users' domain-specific preferences on items, two novel attention mechanisms are further developed to selectively guide the message passing process. Extensive experiments on two real-world datasets are conducted to demonstrate the superiority of our DA-GCN method.

SOCIAL INTEREST FOR USER SELECTING ITEMS IN RECOMMENDER SYSTEMS

2013 ◽  
Vol 24 (04) ◽  
pp. 1350022 ◽  
Author(s):  
DA-CHENG NIE ◽  
MING-JING DING ◽  
YAN FU ◽  
JUN-LIN ZHOU ◽  
ZI-KE ZHANG
Keyword(s):  
Recommender Systems ◽  
Real World ◽  
Social Interest ◽  
Experimental Results ◽  
Simple Method ◽  
The Social ◽  
Social Interests ◽  
Similarity Computation ◽  
Real World Datasets

Recommender systems have developed rapidly and successfully. The system aims to help users find relevant items from a potentially overwhelming set of choices. However, most of the existing recommender algorithms focused on the traditional user-item similarity computation, other than incorporating the social interests into the recommender systems. As we know, each user has their own preference field, they may influence their friends' preference in their expert field when considering the social interest on their friends' item collecting. In order to model this social interest, in this paper, we proposed a simple method to compute users' social interest on the specific items in the recommender systems, and then integrate this social interest with similarity preference. The experimental results on two real-world datasets Epinions and Friendfeed show that this method can significantly improve not only the algorithmic precision-accuracy but also the diversity-accuracy.

scholarly journals Discrete Trust-aware Matrix Factorization for Fast Recommendation

2019 ◽  
Author(s):  
Guibing Guo ◽  
Enneng Yang ◽  
Li Shen ◽  
Xiaochun Yang ◽  
Xiaodong He
Keyword(s):  
Social Influence ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Social Relations ◽  
Real World ◽  
Matrix Factorization ◽  
State Of The Art ◽  
Proposed Model ◽  
Hamming Space ◽  
Real World Datasets

Trust-aware recommender systems have received much attention recently for their abilities to capture the influence among connected users. However, they suffer from the efficiency issue due to large amount of data and time-consuming real-valued operations. Although existing discrete collaborative filtering may alleviate this issue to some extent, it is unable to accommodate social influence. In this paper we propose a discrete trust-aware matrix factorization (DTMF) model to take dual advantages of both social relations and discrete technique for fast recommendation. Specifically, we map the latent representation of users and items into a joint hamming space by recovering the rating and trust interactions between users and items. We adopt a sophisticated discrete coordinate descent (DCD) approach to optimize our proposed model. In addition, experiments on two real-world datasets demonstrate the superiority of our approach against other state-of-the-art approaches in terms of ranking accuracy and efficiency.

scholarly journals Does Every Data Instance Matter? Enhancing Sequential Recommendation by Eliminating Unreliable Data

2021 ◽  
Author(s):  
Yatong Sun ◽  
Bin Wang ◽  
Zhu Sun ◽  
Xiaochun Yang
Keyword(s):  
Recommender Systems ◽  
Gaussian Distribution ◽  
Real World ◽  
Sequential Pattern ◽  
High Loss ◽  
Real World Datasets

Most sequential recommender systems (SRSs) predict next-item as target for each user given its preceding items as input, assuming that each input is related to its target. However, users may unintentionally click on items that are inconsistent with their preference. We empirically verify that SRSs can be misguided with such unreliable instances (i.e. targets mismatch inputs). This inspires us to design a novel SRS By Eliminating unReliable Data (BERD) guided with two observations: (1) unreliable instances generally have high training loss; and (2) high-loss instances are not necessarily unreliable but uncertain ones caused by blurry sequential pattern. Accordingly, BERD models both loss and uncertainty of each instance via a Gaussian distribution to better distinguish unreliable instances; meanwhile an uncertainty-aware graph convolution network is exploited to assist in mining unreliable instances by lowering uncertainty. Extensive experiments on four real-world datasets demonstrate the superiority of our proposed BERD.

scholarly journals Experimental Analysis of Friend-And-Native Based Location Awareness for Accurate Collaborative Filtering

2021 ◽  
Vol 11 (6) ◽  
pp. 2510
Author(s):  
Aaron Ling Chi Yi ◽  
Dae-Ki Kang
Keyword(s):  
Social Influence ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Real World ◽  
Experimental Analysis ◽  
Location Awareness ◽  
Research Communities ◽  
Current Location ◽  
Real World Datasets ◽  
Filtering Approach

Location-based recommender systems have gained a lot of attention in both commercial domains and research communities where there are various approaches that have shown great potential for further studies. However, there has been little attention in previous research on location-based recommender systems for generating recommendations considering the locations of target users. Such recommender systems sometimes recommend places that are far from the target user’s current location. In this paper, we explore the issues of generating location recommendations for users who are traveling overseas by taking into account the user’s social influence and also the native or local expert’s knowledge. Accordingly, we have proposed a collaborative filtering recommendation framework called the Friend-And-Native-Aware Approach for Collaborative Filtering (FANA-CF), to generate reasonable location recommendations for users. We have validated our approach by systematic and extensive experiments using real-world datasets collected from Foursquare TM. By comparing algorithms such as the collaborative filtering approach (item-based collaborative filtering and user-based collaborative filtering) and the personalized mean approach, we have shown that our proposed approach has slightly outperformed the conventional collaborative filtering approach and personalized mean approach.

Matrix factorization completed multicontext data for tensor-enhanced recommendation

2021 ◽  
pp. 1-12
Author(s):  
Shangju Deng ◽  
Jiwei Qin
Keyword(s):  
Recommender Systems ◽  
Real World ◽  
Matrix Factorization ◽  
Cold Start ◽  
User Preferences ◽  
High Dimensional ◽  
Satisfactory Performance ◽  
Interactive Data ◽  
Real World Datasets ◽  
Item Data

Tensors have been explored to share latent user-item relations and have been shown to be effective for recommendation. Tensors suffer from sparsity and cold start problems in real recommendation scenarios; therefore, researchers and engineers usually use matrix factorization to address these issues and improve the performance of recommender systems. In this paper, we propose matrix factorization completed multicontext data for tensor-enhanced algorithm a using matrix factorization combined with a multicontext data method for tensor-enhanced recommendation. To take advantage of existing user-item data, we add the context time and trust to enrich the interactive data via matrix factorization. In addition, Our approach is a high-dimensional tensor framework that further mines the latent relations from the user-item-trust-time tensor to improve recommendation performance. Through extensive experiments on real-world datasets, we demonstrated the superiority of our approach in predicting user preferences. This method is also shown to be able to maintain satisfactory performance even if user-item interactions are sparse.

scholarly journals HERS: Modeling Influential Contexts with Heterogeneous Relations for Sparse and Cold-Start Recommendation

2019 ◽  
Vol 33 ◽  
pp. 3830-3837 ◽  
Author(s):  
Liang Hu ◽  
Songlei Jian ◽  
Longbing Cao ◽  
Zhiping Gu ◽  
Qingkui Chen ◽  
...  
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
Real World ◽  
Cold Start ◽  
Data Sparsity ◽  
Real World Datasets ◽  
Item Item ◽  
Cold Start Problem

Classic recommender systems face challenges in addressing the data sparsity and cold-start problems with only modeling the user-item relation. An essential direction is to incorporate and understand the additional heterogeneous relations, e.g., user-user and item-item relations, since each user-item interaction is often influenced by other users and items, which form the user’s/item’s influential contexts. This induces important yet challenging issues, including modeling heterogeneous relations, interactions, and the strength of the influence from users/items in the influential contexts. To this end, we design Influential-Context Aggregation Units (ICAU) to aggregate the user-user/item-item relations within a given context as the influential context embeddings. Accordingly, we propose a Heterogeneous relations-Embedded Recommender System (HERS) based on ICAUs to model and interpret the underlying motivation of user-item interactions by considering user-user and item-item influences. The experiments on two real-world datasets show the highly improved recommendation quality made by HERS and its superiority in handling the cold-start problem. In addition, we demonstrate the interpretability of modeling influential contexts in explaining the recommendation results.

scholarly journals NeuRec: On Nonlinear Transformation for Personalized Ranking

2018 ◽  
Author(s):  
Shuai Zhang ◽  
Lina Yao ◽  
Aixin Sun ◽  
Sen Wang ◽  
Guodong Long ◽  
...  
Keyword(s):  
Neural Network ◽  
Real World ◽  
Real Life ◽  
Interaction Matrix ◽  
Interaction Patterns ◽  
Latent Factors ◽  
Integrated Network ◽  
Ranking Task ◽  
Real World Datasets ◽  
Personalized Ranking

Modeling user-item interaction patterns is an important task for personalized recommendations. Many recommender systems are based on the assumption that there exists a linear relationship between users and items while neglecting the intricacy and non-linearity of real-life historical interactions. In this paper, we propose a neural network based recommendation model (NeuRec) that untangles the complexity of user-item interactions and establish an integrated network to combine non-linear transformation with latent factors. We further design two variants of NeuRec: user-based NeuRec and item-based NeuRec, by focusing on different aspects of the interaction matrix. Extensive experiments on four real-world datasets demonstrated their superior performances on personalized ranking task.

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

2018 ◽  
Author(s):  
Feng Zhu ◽  
Yan Wang ◽  
Chaochao Chen ◽  
Guanfeng Liu ◽  
Mehmet Orgun ◽  
...  
Keyword(s):  
State Of The Art ◽  
Target Domain ◽  
Latent Factors ◽  
Rating Data ◽  
Cross Domain ◽  
Recommendation Accuracy ◽  
Real World Datasets ◽  
Complex Relationships ◽  
Fully Connected ◽  
Cross System

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.

scholarly journals Concept Extraction and Prerequisite Relation Learning from Educational Data

2019 ◽  
Vol 33 ◽  
pp. 9678-9685 ◽  
Author(s):  
Weiming Lu ◽  
Yangfan Zhou ◽  
Jiale Yu ◽  
Chenhao Jia
Keyword(s):  
Real World ◽  
High Quality ◽  
Learning Materials ◽  
Concept Extraction ◽  
Learning Framework ◽  
Domain Specific ◽  
Concept Pair ◽  
Real World Datasets ◽  
Educational Applications ◽  
Relation Learning

Prerequisite relations among concepts are crucial for educational applications. However, it is difficult to automatically extract domain-specific concepts and learn the prerequisite relations among them without labeled data.In this paper, we first extract high-quality phrases from a set of educational data, and identify the domain-specific concepts by a graph based ranking method. Then, we propose an iterative prerequisite relation learning framework, called iPRL, which combines a learning based model and recovery based model to leverage both concept pair features and dependencies among learning materials. In experiments, we evaluated our approach on two real-world datasets Textbook Dataset and MOOC Dataset, and validated that our approach can achieve better performance than existing methods. Finally, we also illustrate some examples of our approach.

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