Improving Recommendation Accuracy using Cross-domain Similarity

2020 ◽  
Author(s):  
Pradeep Kumar Singh ◽  
Pijush Kanti Dutta Pramanik ◽  
Samriddhi Mishra ◽  
Anand Nayyar ◽  
Divyanshu Shukla ◽  
...  
Keyword(s):  
Cross Domain ◽  
Recommendation Accuracy ◽  
Domain Similarity

scholarly journals A New Algorithm for Sketch-Based Fashion Image Retrieval Based on Cross-Domain Transformation

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Haopeng Lei ◽  
Simin Chen ◽  
Mingwen Wang ◽  
Xiangjian He ◽  
Wenjing Jia ◽  
...  
Keyword(s):  
Image Retrieval ◽  
Online Shopping ◽  
Unsolved Problem ◽  
Correct Match ◽  
Retrieval Accuracy ◽  
Fine Grained ◽  
Cross Domain ◽  
Domain Transformation ◽  
Domain Similarity ◽  
Natural Way

Due to the rise of e-commerce platforms, online shopping has become a trend. However, the current mainstream retrieval methods are still limited to using text or exemplar images as input. For huge commodity databases, it remains a long-standing unsolved problem for users to find the interested products quickly. Different from the traditional text-based and exemplar-based image retrieval techniques, sketch-based image retrieval (SBIR) provides a more intuitive and natural way for users to specify their search need. Due to the large cross-domain discrepancy between the free-hand sketch and fashion images, retrieving fashion images by sketches is a significantly challenging task. In this work, we propose a new algorithm for sketch-based fashion image retrieval based on cross-domain transformation. In our approach, the sketch and photo are first transformed into the same domain. Then, the sketch domain similarity and the photo domain similarity are calculated, respectively, and fused to improve the retrieval accuracy of fashion images. Moreover, the existing fashion image datasets mostly contain photos only and rarely contain the sketch-photo pairs. Thus, we contribute a fine-grained sketch-based fashion image retrieval dataset, which includes 36,074 sketch-photo pairs. Specifically, when retrieving on our Fashion Image dataset, the accuracy of our model ranks the correct match at the top-1 which is 96.6%, 92.1%, 91.0%, and 90.5% for clothes, pants, skirts, and shoes, respectively. Extensive experiments conducted on our dataset and two fine-grained instance-level datasets, i.e., QMUL-shoes and QMUL-chairs, show that our model has achieved a better performance than other existing methods.

scholarly journals Property-based Semantic Similarity and Relatedness for Improving Recommendation Accuracy and Diversity

2015 ◽  
Vol 11 (4) ◽  
pp. 1-40 ◽  
Author(s):  
Silvia Likavec ◽  
Francesco Osborne ◽  
Federica Cena
Keyword(s):  
Semantic Similarity ◽  
The Other ◽  
Spreading Activation ◽  
User Interest ◽  
User Models ◽  
Cross Domain ◽  
Other Hand ◽  
Event Recommendation ◽  
Recommendation Accuracy ◽  
Edge Based

The authors introduce new measures of semantic similarity and relatedness for ontological concepts, based on the properties associated to them. They consider two concepts similar if, for some properties they have in common, they also have the same values assigned to these properties. On the other hand, the authors consider two concepts related if they have the same values assigned to different properties. These measures are used in the propagation of user interest values in ontology-based user models to other similar or related concepts in the domain. The authors tested their algorithm in event recommendation domain and in recipe domain and showed that property-based propagation based on similarity outperforms the standard edge-based propagation. Adding relatedness as a criterion for propagation improves diversity without sacrificing accuracy. In addition, assigning a certain relevance to each property improves the accuracy of recommendation. Finally, the property-based spreading activation is effective for cross-domain recommendation.

scholarly journals Cross-Domain Similarity Learning for Face Recognition in Unseen Domains

2021 ◽  
Author(s):  
Masoud Faraki ◽  
Xiang Yu ◽  
Yi-Hsuan Tsai ◽  
Yumin Suh ◽  
Manmohan Chandraker
Keyword(s):  
Face Recognition ◽  
Similarity Learning ◽  
Cross Domain ◽  
Domain Similarity

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.

Cross domain similarity mining

2012 ◽  
Vol 14 (1) ◽  
pp. 43-47 ◽  
Author(s):  
Guozhu Dong
Keyword(s):  
Cross Domain ◽  
Domain Similarity

Sketch-Based Shape Retrieval via Best View Selection and a Cross-Domain Similarity Measure

2020 ◽  
pp. 1-1 ◽  
Author(s):  
Yongzhe Xu ◽  
Jiangchuan Hu ◽  
Kanoksak Wattanachote ◽  
Kun Zeng ◽  
Yongyi Gong
Keyword(s):  
Similarity Measure ◽  
Shape Retrieval ◽  
View Selection ◽  
Cross Domain ◽  
Best View Selection ◽  
Domain Similarity

Mitigating Sparsity and Cold Start Problem in Collaborative Filtering using Cross-domain Similarity

2020 ◽  
Author(s):  
Pradeep Kumar Singh ◽  
Pijush Kanti Dutta Pramanik ◽  
Garima Ahuja ◽  
Anand Nayyar ◽  
Vaibhav Pandey ◽  
...  
Keyword(s):  
Collaborative Filtering ◽  
Cold Start ◽  
Cross Domain ◽  
Cold Start Problem ◽  
Domain Similarity

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

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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