The Experts below are selected from a list of 22641 Experts worldwide ranked by ideXlab platform
David L. Olson - One of the best experts on this subject based on the ideXlab platform.
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online to offline o2o service recommendation method based on multi dimensional Similarity Measurement
Decision Support Systems, 2017Co-Authors: Desheng Wu, David L. OlsonAbstract:Abstract With the rapid development of information technology, consumers are able to search for and buy services or products online, and then consume them in an offline store. This emerging ecommerce model is called online to offline (O2O) service, which has attracted business and academic attention. The large number of O2O services on the Internet creates a scalability problem, creating massive but highly sparse matrices relating customers to items purchased. In this paper, we proposed a novel O2O service recommendation method based on multi-dimensional Similarity Measurements. This approach encompasses three Similarity measures: collaborative Similarity, preference Similarity and trajectory Similarity. Experimental results show that a combination of multiple Similarity measures performs better than any one single Similarity measure. We also find that trajectory Similarity performs better than the rating-based Similarity metrics (collaborative Similarity and preference Similarity) in sparse matrices.
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online to offline o2o service recommendation method based on multi dimensional Similarity Measurement
Decision Support Systems, 2017Co-Authors: Yuchen Pan, David L. OlsonAbstract:With the rapid development of information technology, consumers are able to search for and buy services or products online, and then consume them in an offline store. This emerging ecommerce model ...
Desheng Wu - One of the best experts on this subject based on the ideXlab platform.
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online to offline o2o service recommendation method based on multi dimensional Similarity Measurement
Decision Support Systems, 2017Co-Authors: Desheng Wu, David L. OlsonAbstract:Abstract With the rapid development of information technology, consumers are able to search for and buy services or products online, and then consume them in an offline store. This emerging ecommerce model is called online to offline (O2O) service, which has attracted business and academic attention. The large number of O2O services on the Internet creates a scalability problem, creating massive but highly sparse matrices relating customers to items purchased. In this paper, we proposed a novel O2O service recommendation method based on multi-dimensional Similarity Measurements. This approach encompasses three Similarity measures: collaborative Similarity, preference Similarity and trajectory Similarity. Experimental results show that a combination of multiple Similarity measures performs better than any one single Similarity measure. We also find that trajectory Similarity performs better than the rating-based Similarity metrics (collaborative Similarity and preference Similarity) in sparse matrices.
Licia Capra - One of the best experts on this subject based on the ideXlab platform.
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empirical comparison of text based mobile apps Similarity Measurement techniques
Empirical Software Engineering, 2019Co-Authors: Federica Sarro, Sue Black, Afnan A Alsubaihin, Licia CapraAbstract:Context: Code-free software Similarity detection techniques have been used to support different software engineering tasks, including clustering mobile applications (apps). The way of measuring Similarity may affect both the efficiency and quality of clustering solutions. However, there has been no previous comparative study of feature extraction methods used to guide mobile app clustering. Objective: In this paper, we investigate different techniques to compute the Similarity of apps based on their textual descriptions and evaluate their effectiveness using hierarchical agglomerative clustering. Method: To this end we carry out an empirical study comparing five different techniques, based on topic modelling and keyword feature extraction, to cluster 12,664 apps randomly sampled from the Google Play App Store. The comparison is based on three main criteria: silhouette width measure, human judgement and execution time. Results: The results of our study show that using topic modelling, in addition to collocation-based and dependency-based feature extractors perform similarly in detecting app-feature Similarity. However, dependency-based feature extraction performs better than any other in finding application domain Similarity (ρ = 0.7,p − value < 0.01). Conclusions: Current categorisation in the app store studied does not exhibit a good classification quality in terms of the claimed feature space. However, a better quality can be achieved using a good feature extraction technique and a traditional clustering method.
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Empirical comparison of text-based mobile apps Similarity Measurement techniques
Empirical Software Engineering, 2019Co-Authors: Afnan Al-subaihin, Federica Sarro, Sue Black, Licia CapraAbstract:ContextCode-free software Similarity detection techniques have been used to support different software engineering tasks, including clustering mobile applications (apps). The way of measuring Similarity may affect both the efficiency and quality of clustering solutions. However, there has been no previous comparative study of feature extraction methods used to guide mobile app clustering.ObjectiveIn this paper, we investigate different techniques to compute the Similarity of apps based on their textual descriptions and evaluate their effectiveness using hierarchical agglomerative clustering.MethodTo this end we carry out an empirical study comparing five different techniques, based on topic modelling and keyword feature extraction, to cluster 12,664 apps randomly sampled from the Google Play App Store. The comparison is based on three main criteria: silhouette width measure, human judgement and execution time.ResultsThe results of our study show that using topic modelling, in addition to collocation-based and dependency-based feature extractors perform similarly in detecting app-feature Similarity. However, dependency-based feature extraction performs better than any other in finding application domain Similarity ( ρ = 0.7, p − v a l u e
Afnan A Alsubaihin - One of the best experts on this subject based on the ideXlab platform.
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empirical comparison of text based mobile apps Similarity Measurement techniques
Empirical Software Engineering, 2019Co-Authors: Federica Sarro, Sue Black, Afnan A Alsubaihin, Licia CapraAbstract:Context: Code-free software Similarity detection techniques have been used to support different software engineering tasks, including clustering mobile applications (apps). The way of measuring Similarity may affect both the efficiency and quality of clustering solutions. However, there has been no previous comparative study of feature extraction methods used to guide mobile app clustering. Objective: In this paper, we investigate different techniques to compute the Similarity of apps based on their textual descriptions and evaluate their effectiveness using hierarchical agglomerative clustering. Method: To this end we carry out an empirical study comparing five different techniques, based on topic modelling and keyword feature extraction, to cluster 12,664 apps randomly sampled from the Google Play App Store. The comparison is based on three main criteria: silhouette width measure, human judgement and execution time. Results: The results of our study show that using topic modelling, in addition to collocation-based and dependency-based feature extractors perform similarly in detecting app-feature Similarity. However, dependency-based feature extraction performs better than any other in finding application domain Similarity (ρ = 0.7,p − value < 0.01). Conclusions: Current categorisation in the app store studied does not exhibit a good classification quality in terms of the claimed feature space. However, a better quality can be achieved using a good feature extraction technique and a traditional clustering method.
Yuxin Yuan - One of the best experts on this subject based on the ideXlab platform.
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modality specific cross modal Similarity Measurement with recurrent attention network
IEEE Transactions on Image Processing, 2018Co-Authors: Yuxin Peng, Yuxin YuanAbstract:Nowadays, cross-modal retrieval plays an important role to flexibly find useful information across different modalities of data. Effectively measuring the Similarity between different modalities of data is the key of cross-modal retrieval. Different modalities, such as image and text, have imbalanced and complementary relationship, and they contain unequal amount of information when describing the same semantics. For example, images often contain more details that cannot be demonstrated by textual descriptions and vice versa. Existing works based on deep neural network mostly construct one common space for different modalities, to find the latent alignments between them, which lose their exclusive modality-specific characteristics. Therefore, we propose modality-specific cross-modal Similarity Measurement approach by constructing the independent semantic space for each modality, which adopts an end-to-end framework to directly generate the modality-specific cross-modal Similarity without explicit common representation. For each semantic space, modality-specific characteristics within one modality are fully exploited by recurrent attention network, while the data of another modality is projected into this space with attention based joint embedding, which utilizes the learned attention weights for guiding the fine-grained cross-modal correlation learning, and captures the imbalanced and complementary relationship between different modalities. Finally, the complementarity between the semantic spaces for different modalities is explored by adaptive fusion of the modality-specific cross-modal similarities to perform the cross-modal retrieval. Experiments on the widely used Wikipedia, Pascal Sentence, and MS-COCO data sets as well as our constructed large-scale XMediaNet data set verify the effectiveness of our proposed approach, outperforming nine state-of-the-art methods.
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modality specific cross modal Similarity Measurement with recurrent attention network
arXiv: Computer Vision and Pattern Recognition, 2017Co-Authors: Yuxin Peng, Yuxin YuanAbstract:Nowadays, cross-modal retrieval plays an indispensable role to flexibly find information across different modalities of data. Effectively measuring the Similarity between different modalities of data is the key of cross-modal retrieval. Different modalities such as image and text have imbalanced and complementary relationships, which contain unequal amount of information when describing the same semantics. For example, images often contain more details that cannot be demonstrated by textual descriptions and vice versa. Existing works based on Deep Neural Network (DNN) mostly construct one common space for different modalities to find the latent alignments between them, which lose their exclusive modality-specific characteristics. Different from the existing works, we propose modality-specific cross-modal Similarity Measurement (MCSM) approach by constructing independent semantic space for each modality, which adopts end-to-end framework to directly generate modality-specific cross-modal Similarity without explicit common representation. For each semantic space, modality-specific characteristics within one modality are fully exploited by recurrent attention network, while the data of another modality is projected into this space with attention based joint embedding to utilize the learned attention weights for guiding the fine-grained cross-modal correlation learning, which can capture the imbalanced and complementary relationships between different modalities. Finally, the complementarity between the semantic spaces for different modalities is explored by adaptive fusion of the modality-specific cross-modal similarities to perform cross-modal retrieval. Experiments on the widely-used Wikipedia and Pascal Sentence datasets as well as our constructed large-scale XMediaNet dataset verify the effectiveness of our proposed approach, outperforming 9 state-of-the-art methods.
