The Experts below are selected from a list of 51309 Experts worldwide ranked by ideXlab platform
Gencai Chen - One of the best experts on this subject based on the ideXlab platform.
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automatic multi way domain concept hierarchy construction from Customer Reviews
Neurocomputing, 2015Co-Authors: Ding Tu, Ling Chen, Gencai ChenAbstract:Abstract A concept hierarchy is important for many applications to manage and analyze text corpora. In the literature, most previous hierarchy construction works are under the assumption that the semantic relations in the concept hierarchy can be extracted from a text corpus, which is not fully satisfied for short and informal texts, e.g. tweets and Customer Reviews. And many works utilize hierarchical clustering methods to get the final concept hierarchy, in which the resulting binary-tree form concept hierarchy cannot fit the demand in many applications. In this paper, we propose a general process for building a concept hierarchy from Customer Reviews with an appropriate depth. The process can be divided into three steps. First, all highly ranked topic words are extracted as concept words using a topic model. And a word sense disambiguation task is performed to derive the possible semantics of the words. Then, the distances between these words are computed by combining their contexts and relations in the WordNet. Finally, all words are organized using a modified multi-way hierarchical clustering method. In addition, a new concept hierarchy evaluation model is presented. Our approach is compared to approaches using hierarchical clustering methods on the Amazon Customer Review data set, and the results show that our approach can get higher similarity scores with the reference concept hierarchy.
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wordnet based multi way concept hierarchy construction from text corpus
National Conference on Artificial Intelligence, 2013Co-Authors: Ding Tu, Ling Chen, Gencai ChenAbstract:In this paper, we propose an approach to build a multiway concept hierarchy from a text corpus, which is based on WordNet and multi-way hierarchical clustering. In addition, a new evaluation metric is presented, and our approach is compared with 4 kinds of existing methods on the Amazon Customer Review data set.
Christopher S Tang - One of the best experts on this subject based on the ideXlab platform.
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Customer Review provision policies with heterogeneous cluster preferences
Social Science Research Network, 2021Co-Authors: Shihong Xiao, Yingju Chen, Christopher S TangAbstract:Companies often post user-generated Reviews online so that potential buyers in different clusters (age, geographic region, occupation, etc.) can learn from existing Customers about the quality of an experience good and cluster preferences before purchasing. In this paper, we evaluate two common user-generated Review provision policies for selling experience goods to Customers in different clusters with heterogeneous preferences. The first policy is called the Association-based policy (AP) under which a Customer in a cluster can only observe the aggregate Review (i.e., average rating) generated by users within the same cluster. The second policy is called the Global-based policy (GP) under which each Customer is presented with the aggregate Review generated by all users across clusters. We find that, in general, the firm benefits from a policy that provides a larger number of ``relevant Reviews'' to Customers. When Customers are more certain about the product quality and when product preferences are more diverse across clusters, AP is more profitable than GP because it provides cluster-specific Reviews to Customers. Otherwise, GP is more profitable as it provides a larger number of Reviews, although less relevant, to Customers. Moreover, we propose a third provision policy that imparts the union of the information by AP and GP and show that it is more profitable for the firm. While the third policy always renders a higher consumer welfare than GP, it may generate a lower consumer welfare than AP.
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Customer Review provision policies with heterogeneous cluster preferences
2020Co-Authors: Shihong Xiao, Yingju Chen, Christopher S TangAbstract:Companies often post user-generated Reviews online so that potential buyers in different clusters (age, geographic region, occupation, etc.) can learn from existing Customers about the quality of an experience good and cluster preferences before purchasing. In this paper, we evaluate two common user-generated Review provision policies for selling experience goods to Customers in different clusters with heterogeneous preferences. The first policy is called the Association-based policy (AP) under which a Customer in a cluster can only observe the aggregate Review (i.e., average rating) generated by users within the same cluster. The second policy is called the Global-based policy (GP) under which each Customer is presented with the aggregate Review generated by all users across clusters. We find that, in general, the firm benefits from a policy that provides a larger number of ``relevant Reviews'' to Customers because it produces ``mixing'' to Customer expectations. Creating more mixing can increase the variance of Customer expectations a priori, which is more profitable for the firm. When Customers are more certain about the product quality and when product preferences are more diverse across clusters, AP is more profitable than GP because it provides cluster-specific Reviews to Customers. Otherwise, GP is more profitable as it provides a larger number of Reviews, although less relevant, to Customers. Moreover, we propose a third provision policy that imparts the union of the information by AP and GP and show that it is more profitable for the firm. While the third policy always renders a higher consumer welfare than GP, it may generate a lower consumer welfare than AP.
Ding Tu - One of the best experts on this subject based on the ideXlab platform.
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automatic multi way domain concept hierarchy construction from Customer Reviews
Neurocomputing, 2015Co-Authors: Ding Tu, Ling Chen, Gencai ChenAbstract:Abstract A concept hierarchy is important for many applications to manage and analyze text corpora. In the literature, most previous hierarchy construction works are under the assumption that the semantic relations in the concept hierarchy can be extracted from a text corpus, which is not fully satisfied for short and informal texts, e.g. tweets and Customer Reviews. And many works utilize hierarchical clustering methods to get the final concept hierarchy, in which the resulting binary-tree form concept hierarchy cannot fit the demand in many applications. In this paper, we propose a general process for building a concept hierarchy from Customer Reviews with an appropriate depth. The process can be divided into three steps. First, all highly ranked topic words are extracted as concept words using a topic model. And a word sense disambiguation task is performed to derive the possible semantics of the words. Then, the distances between these words are computed by combining their contexts and relations in the WordNet. Finally, all words are organized using a modified multi-way hierarchical clustering method. In addition, a new concept hierarchy evaluation model is presented. Our approach is compared to approaches using hierarchical clustering methods on the Amazon Customer Review data set, and the results show that our approach can get higher similarity scores with the reference concept hierarchy.
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wordnet based multi way concept hierarchy construction from text corpus
National Conference on Artificial Intelligence, 2013Co-Authors: Ding Tu, Ling Chen, Gencai ChenAbstract:In this paper, we propose an approach to build a multiway concept hierarchy from a text corpus, which is based on WordNet and multi-way hierarchical clustering. In addition, a new evaluation metric is presented, and our approach is compared with 4 kinds of existing methods on the Amazon Customer Review data set.
Shihong Xiao - One of the best experts on this subject based on the ideXlab platform.
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Customer Review provision policies with heterogeneous cluster preferences
Social Science Research Network, 2021Co-Authors: Shihong Xiao, Yingju Chen, Christopher S TangAbstract:Companies often post user-generated Reviews online so that potential buyers in different clusters (age, geographic region, occupation, etc.) can learn from existing Customers about the quality of an experience good and cluster preferences before purchasing. In this paper, we evaluate two common user-generated Review provision policies for selling experience goods to Customers in different clusters with heterogeneous preferences. The first policy is called the Association-based policy (AP) under which a Customer in a cluster can only observe the aggregate Review (i.e., average rating) generated by users within the same cluster. The second policy is called the Global-based policy (GP) under which each Customer is presented with the aggregate Review generated by all users across clusters. We find that, in general, the firm benefits from a policy that provides a larger number of ``relevant Reviews'' to Customers. When Customers are more certain about the product quality and when product preferences are more diverse across clusters, AP is more profitable than GP because it provides cluster-specific Reviews to Customers. Otherwise, GP is more profitable as it provides a larger number of Reviews, although less relevant, to Customers. Moreover, we propose a third provision policy that imparts the union of the information by AP and GP and show that it is more profitable for the firm. While the third policy always renders a higher consumer welfare than GP, it may generate a lower consumer welfare than AP.
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Customer Review provision policies with heterogeneous cluster preferences
2020Co-Authors: Shihong Xiao, Yingju Chen, Christopher S TangAbstract:Companies often post user-generated Reviews online so that potential buyers in different clusters (age, geographic region, occupation, etc.) can learn from existing Customers about the quality of an experience good and cluster preferences before purchasing. In this paper, we evaluate two common user-generated Review provision policies for selling experience goods to Customers in different clusters with heterogeneous preferences. The first policy is called the Association-based policy (AP) under which a Customer in a cluster can only observe the aggregate Review (i.e., average rating) generated by users within the same cluster. The second policy is called the Global-based policy (GP) under which each Customer is presented with the aggregate Review generated by all users across clusters. We find that, in general, the firm benefits from a policy that provides a larger number of ``relevant Reviews'' to Customers because it produces ``mixing'' to Customer expectations. Creating more mixing can increase the variance of Customer expectations a priori, which is more profitable for the firm. When Customers are more certain about the product quality and when product preferences are more diverse across clusters, AP is more profitable than GP because it provides cluster-specific Reviews to Customers. Otherwise, GP is more profitable as it provides a larger number of Reviews, although less relevant, to Customers. Moreover, we propose a third provision policy that imparts the union of the information by AP and GP and show that it is more profitable for the firm. While the third policy always renders a higher consumer welfare than GP, it may generate a lower consumer welfare than AP.
Nisrina Afifah - One of the best experts on this subject based on the ideXlab platform.
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pengaruh online Customer Review dan online Customer rating terhadap keputusan pembelian melalui marketplace shopee
2021Co-Authors: Nisrina AfifahAbstract:Penelitian ini bertujuan untuk menganalisis Pengaruh Online Customer Review dan Online Customer Rating terhadap Keputusan Pembelian pada Marketplace Shopee. Subjek dalam penelitian ini adalah konsumen yang berdomisili di wilayah Daerah Istimewa Yogyakarta yang pernah melalukan pembelian di Marketplace Shopee minimal satu kali. Penelitian ini menggunakan sampel sebanyak 100 responden yang ditentukan dengan Teknik pengambilan sampel menggunakan non-probability sampling dengan metode purposive sampling. Alat analisis yang digunakan dalam penelitian ini menggunakan software Statistical Package for Social Science (SPSS) dengan metode analisis regresi linear berganda. Hasil dari penelitian ini menunjukkan bahwa variabel Online Customer Review dan Online Customer Rating berpengaruh positif dan signifikan terhadap Keputusan Pembelian. Variabel Keputusan Pembelian dapat memediasi pengaruh antara Online Customer Review dan Online Customer Rating. Kata Kunci: Online Customer Review, Online Customer Rating, Keputusan Pembelian.
