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<p><span class="fr-highlight-change" data-track-id="pending-1-1" data-tracking="true"><span class="fr-tracking-deleted" contenteditable="false" data-tracking-deleted="true">Besides,</span></span> <span class="fr-highlight-change" data-track-id="pending-1-1" data-tracking="true"><span class="fr-tracking-deleted" contenteditable="false" data-tracking-deleted="true">s</span></span><span class="fr-highlight-change" data-track-id="pending-1-0" data-tracking="true">&nbsp;S</span>ome of the results of user grouping are as follows:</p><p>- A significant percentage of people who visit this website are looking for home appliances, including kitchen and electrical appliances.</p><p>- The second favorite item among this website&rsquo;s users is mobile because it is seen in a high percentage of groups.</p><p>- Some groups include people who do not have a specific purpose in their search and randomly select pages.</p><p>- Users who are looking for <span class="fr-highlight-change" data-track-id="pending-1-0" data-tracking="true">a&nbsp;</span>camera and video equipment are also interested in digital and mobile devices.</p><p>- Users who are interested in the car and its equipment are also interested in digital and mobile devices.</p><p>With this information, new users can be placed in each of these 25 groups according to the pages they refer to, and their favorite topics can be extracted as well. In addition, their next navigational behaviors can be predicted. In addition to extracting users&rsquo; favorite topics, their behavioral parameters have also been extracted to build the model. Some of the results of users&rsquo; navigational behavior are as follows:</p><p>- Most of the users have an average of one to two sessions per day. This means that many users of this website conduct their surveys purposefully.</p><p>- Users with the highest number of sessions have spent more time since their last visit to the website. It can be stated that these users are not loyal customers of the website.</p><p>- Users who visit the website purposefully are people who are generally looking for home appliances and digital devices.</p><p>- Considering the targeted customers, the website manager should consider that the users have access to the desired product in their first little navigation. This is crucial in designing a website and choosing its layers to access each product.</p><p>Today, e-commerce websites need to know more about their customers to increase their satisfaction and loyalty, which is possible by analyzing and understanding their behavior and interests. Studying users&rsquo; needs and interests and anticipating their online behaviors is part of web personalization. Therefore, this study presented a general framework of web personalization for a Persian website using an integration of web content and usage mining. Integrating content and usage mining will lead to identifying different groups of customers to predict new customers&rsquo; future behavior and interests. Previous studies such as Gurbas et al. (2013) have tried to add content features to the results of usage mining to increase the accuracy and quality of patterns and facilitate their understanding. However, researchers have generally combined the results of usage mining with the web anthology. &nbsp;However, similar to the results of Herwanto (2016), the results of our study indicated that integrating web usage and content mining will allow removing subjectivity from profile data and keeping it updated. Besides, including semantic information in creating a navigation pattern more accurate recommendations can be provided. This indicates an increase in the quality of the patterns.</p><p>The data used in this study was the real data from a Persian language retail website. Since the syntax and semantics of the Persian language are different from English and other languages, recognizing nouns and pronouns, parts of speech tagging, finding word boundaries, tracing, or manipulating characters are also different (Habib, 2021). Besides, the research design and methods used in this study were the <span class="fr-highlight-change" data-track-id="pending-1-0" data-tracking="true">combinations&nbsp;</span><span class="fr-highlight-change" data-track-id="pending-1-1" data-tracking="true"><span class="fr-tracking-deleted" contenteditable="false" data-tracking-deleted="true">combination</span></span> of the methods used in previous studies. For example, LDA was used for content mining which is similar to the studies of Abdi Ghavidel et al. (2015), which used LDA for Persian metaphor classification and its frequency prediction; Du et al. (2020), Yang &amp; Zhang (2018), Herwanto (2016) and Shotorbani et al. (2016) which used LDA for content mining. Besides, similar to Habib (2021) and Shotorbani et al. (2016), the SVM was used for classification. However, no similar model that combines both content and usage mining for predicting customers&rsquo; behavior was used in this study, which distinguishes it from previous studies and <span class="fr-highlight-change" data-track-id="pending-1-0" data-tracking="true">makes&nbsp;</span><span class="fr-highlight-change" data-track-id="pending-1-1" data-tracking="true"><span class="fr-tracking-deleted" contenteditable="false" data-tracking-deleted="true">make</span></span> it impossible to compare the results with previous studies.</p><p>This study will help the literature in the field of web personalization in three ways: first, in this research, five parameters were introduced and calculated to identify users&rsquo; behavioral patterns; Second, to extract users&rsquo; interests, topic modeling of web pages along with web mining techniques was used, which had not been done before in a Persian-language website and is the innovation of this study. The third contribution of this study to the literature in this field is the use of a dependency distribution algorithm for clustering user navigational patterns that have not been used in previous studies and is one of the innovations of this study.</p><p>Owing to the internet&rsquo;s nature and the lack of physical customers, meeting customers&rsquo; needs and improving the quality of services require accurate knowledge of customer priorities; customers, however, are generally not interested in long queries or filling out forms. Therefore, online retailers must gather customers&rsquo; preferences from the interactions and information provided by the sales process; it is essential to know how their customers use their websites. Inferring useful results requires in-depth data analysis. Thus, the integration of web usage and content mining is crucial for optimally designing the structure of sales websites, increasing the attraction of potential customers, and retaining existing ones. Because e-commerce has become a competitive space for online retailers, electronic marketers should focus on increasing customer satisfaction. For this purpose, e-business and electronic commerce websites should have quick and accurate access to customers&rsquo; needs, customize web pages accordingly, and provide personalized products and services. The combination of the results of web content and usage mining increases the accuracy and quality of the recommender systems, helping them achieve more effective personalization. Therefore, online retailers can use the results of this study to better personalize their websites. In addition, the results can help organizations gather customer data, suggest products, send personalized messages to customers, and more.</p><p>&nbsp;</p><p>&nbsp;</p><p>&nbsp;</p><p>&nbsp;</p>
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Navigational behavior Essay

   Essay 2021-11-30T08:41:06.616000+00:00 avatar

Saeedeh Rajaee

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Besides, s Some of the results of user grouping are as follows:

- A significant percentage of people who visit this website are looking for home appliances, including kitchen and electrical appliances.

- The second favorite item among this website’s users is mobile because it is seen in a high percentage of groups.

- Some groups include people who do not have a specific purpose in their search and randomly select pages.

- Users who are looking for camera and video equipment are also interested in digital and mobile devices.

- Users who are interested in the car and its equipment are also interested in digital and mobile devices.

With this information, new users can be placed in each of these 25 groups according to the pages they refer to, and their favorite topics can be extracted as well. In addition, their next navigational behaviors can be predicted. In addition to extracting users’ favorite topics, their behavioral parameters have also been extracted to build the model. Some of the results of users’ navigational behavior are as follows:

- Most of the users have an average of one to two sessions per day. This means that many users of this website conduct their surveys purposefully.

- Users with the highest number of sessions have spent more time since their last visit to the website. It can be stated that these users are not loyal customers of the website.

- Users who visit the website purposefully are people who are generally looking for home appliances and digital devices.

- Considering the targeted customers, the website manager should consider that the users have access to the desired product in their first little navigation. This is crucial in designing a website and choosing its layers to access each product.

Today, e-commerce websites need to know more about their customers to increase their satisfaction and loyalty, which is possible by analyzing and understanding their behavior and interests. Studying users’ needs and interests and anticipating their online behaviors is part of web personalization. Therefore, this study presented a general framework of web personalization for a Persian website using an integration of web content and usage mining. Integrating content and usage mining will lead to identifying different groups of customers to predict new customers’ future behavior and interests. Previous studies such as Gurbas et al. (2013) have tried to add content features to the results of usage mining to increase the accuracy and quality of patterns and facilitate their understanding. However, researchers have generally combined the results of usage mining with the web anthology.  However, similar to the results of Herwanto (2016), the results of our study indicated that integrating web usage and content mining will allow removing subjectivity from profile data and keeping it updated. Besides, including semantic information in creating a navigation pattern more accurate recommendations can be provided. This indicates an increase in the quality of the patterns.

The data used in this study was the real data from a Persian language retail website. Since the syntax and semantics of the Persian language are different from English and other languages, recognizing nouns and pronouns, parts of speech tagging, finding word boundaries, tracing, or manipulating characters are also different (Habib, 2021). Besides, the research design and methods used in this study were the combinations combination of the methods used in previous studies. For example, LDA was used for content mining which is similar to the studies of Abdi Ghavidel et al. (2015), which used LDA for Persian metaphor classification and its frequency prediction; Du et al. (2020), Yang & Zhang (2018), Herwanto (2016) and Shotorbani et al. (2016) which used LDA for content mining. Besides, similar to Habib (2021) and Shotorbani et al. (2016), the SVM was used for classification. However, no similar model that combines both content and usage mining for predicting customers’ behavior was used in this study, which distinguishes it from previous studies and makes make it impossible to compare the results with previous studies.

This study will help the literature in the field of web personalization in three ways: first, in this research, five parameters were introduced and calculated to identify users’ behavioral patterns; Second, to extract users’ interests, topic modeling of web pages along with web mining techniques was used, which had not been done before in a Persian-language website and is the innovation of this study. The third contribution of this study to the literature in this field is the use of a dependency distribution algorithm for clustering user navigational patterns that have not been used in previous studies and is one of the innovations of this study.

Owing to the internet’s nature and the lack of physical customers, meeting customers’ needs and improving the quality of services require accurate knowledge of customer priorities; customers, however, are generally not interested in long queries or filling out forms. Therefore, online retailers must gather customers’ preferences from the interactions and information provided by the sales process; it is essential to know how their customers use their websites. Inferring useful results requires in-depth data analysis. Thus, the integration of web usage and content mining is crucial for optimally designing the structure of sales websites, increasing the attraction of potential customers, and retaining existing ones. Because e-commerce has become a competitive space for online retailers, electronic marketers should focus on increasing customer satisfaction. For this purpose, e-business and electronic commerce websites should have quick and accurate access to customers’ needs, customize web pages accordingly, and provide personalized products and services. The combination of the results of web content and usage mining increases the accuracy and quality of the recommender systems, helping them achieve more effective personalization. Therefore, online retailers can use the results of this study to better personalize their websites. In addition, the results can help organizations gather customer data, suggest products, send personalized messages to customers, and more.

 

 

 

 


Besides, s Some of the results of user grouping are as follows:

- A significant percentage of people who visit this website are looking for home appliances, including kitchen and electrical appliances.

- The second favorite item among this website’s users is mobile because it is seen in a high percentage of groups.

- Some groups include people who do not have a specific purpose in their search and randomly select pages.

- Users who are looking for camera and video equipment are also interested in digital and mobile devices.

- Users who are interested in the car and its equipment are also interested in digital and mobile devices.

With this information, new users can be placed in each of these 25 groups according to the pages they refer to, and their favorite topics can be extracted as well. In addition, their next navigational behaviors can be predicted. In addition to extracting users’ favorite topics, their behavioral parameters have also been extracted to build the model. Some of the results of users’ navigational behavior are as follows:

- Most of the users have an average of one to two sessions per day. This means that many users of this website conduct their surveys purposefully.

- Users with the highest number of sessions have spent more time since their last visit to the website. It can be stated that these users are not loyal customers of the website.

- Users who visit the website purposefully are people who are generally looking for home appliances and digital devices.

- Considering the targeted customers, the website manager should consider that the users have access to the desired product in their first little navigation. This is crucial in designing a website and choosing its layers to access each product.

Today, e-commerce websites need to know more about their customers to increase their satisfaction and loyalty, which is possible by analyzing and understanding their behavior and interests. Studying users’ needs and interests and anticipating their online behaviors is part of web personalization. Therefore, this study presented a general framework of web personalization for a Persian website using an integration of web content and usage mining. Integrating content and usage mining will lead to identifying different groups of customers to predict new customers’ future behavior and interests. Previous studies such as Gurbas et al. (2013) have tried to add content features to the results of usage mining to increase the accuracy and quality of patterns and facilitate their understanding. However, researchers have generally combined the results of usage mining with the web anthology.  However, similar to the results of Herwanto (2016), the results of our study indicated that integrating web usage and content mining will allow removing subjectivity from profile data and keeping it updated. Besides, including semantic information in creating a navigation pattern more accurate recommendations can be provided. This indicates an increase in the quality of the patterns.

The data used in this study was the real data from a Persian language retail website. Since the syntax and semantics of the Persian language are different from English and other languages, recognizing nouns and pronouns, parts of speech tagging, finding word boundaries, tracing, or manipulating characters are also different (Habib, 2021). Besides, the research design and methods used in this study were the combinations combination of the methods used in previous studies. For example, LDA was used for content mining which is similar to the studies of Abdi Ghavidel et al. (2015), which used LDA for Persian metaphor classification and its frequency prediction; Du et al. (2020), Yang & Zhang (2018), Herwanto (2016) and Shotorbani et al. (2016) which used LDA for content mining. Besides, similar to Habib (2021) and Shotorbani et al. (2016), the SVM was used for classification. However, no similar model that combines both content and usage mining for predicting customers’ behavior was used in this study, which distinguishes it from previous studies and makes make it impossible to compare the results with previous studies.

This study will help the literature in the field of web personalization in three ways: first, in this research, five parameters were introduced and calculated to identify users’ behavioral patterns; Second, to extract users’ interests, topic modeling of web pages along with web mining techniques was used, which had not been done before in a Persian-language website and is the innovation of this study. The third contribution of this study to the literature in this field is the use of a dependency distribution algorithm for clustering user navigational patterns that have not been used in previous studies and is one of the innovations of this study.

Owing to the internet’s nature and the lack of physical customers, meeting customers’ needs and improving the quality of services require accurate knowledge of customer priorities; customers, however, are generally not interested in long queries or filling out forms. Therefore, online retailers must gather customers’ preferences from the interactions and information provided by the sales process; it is essential to know how their customers use their websites. Inferring useful results requires in-depth data analysis. Thus, the integration of web usage and content mining is crucial for optimally designing the structure of sales websites, increasing the attraction of potential customers, and retaining existing ones. Because e-commerce has become a competitive space for online retailers, electronic marketers should focus on increasing customer satisfaction. For this purpose, e-business and electronic commerce websites should have quick and accurate access to customers’ needs, customize web pages accordingly, and provide personalized products and services. The combination of the results of web content and usage mining increases the accuracy and quality of the recommender systems, helping them achieve more effective personalization. Therefore, online retailers can use the results of this study to better personalize their websites. In addition, the results can help organizations gather customer data, suggest products, send personalized messages to customers, and more.

 

 

 

 


Besides, s Some of the results of user grouping are as follows:

- A significant percentage of people who visit this website are looking for home appliances, including kitchen and electrical appliances.

- The second favorite item among this website’s users is mobile because it is seen in a high percentage of groups.

- Some groups include people who do not have a specific purpose in their search and randomly select pages.

- Users who are looking for camera and video equipment are also interested in digital and mobile devices.

- Users who are interested in the car and its equipment are also interested in digital and mobile devices.

With this information, new users can be placed in each of these 25 groups according to the pages they refer to, and their favorite topics can be extracted as well. In addition, their next navigational behaviors can be predicted. In addition to extracting users’ favorite topics, their behavioral parameters have also been extracted to build the model. Some of the results of users’ navigational behavior are as follows:

- Most of the users have an average of one to two sessions per day. This means that many users of this website conduct their surveys purposefully.

- Users with the highest number of sessions have spent more time since their last visit to the website. It can be stated that these users are not loyal customers of the website.

- Users who visit the website purposefully are people who are generally looking for home appliances and digital devices.

- Considering the targeted customers, the website manager should consider that the users have access to the desired product in their first little navigation. This is crucial in designing a website and choosing its layers to access each product.

Today, e-commerce websites need to know more about their customers to increase their satisfaction and loyalty, which is possible by analyzing and understanding their behavior and interests. Studying users’ needs and interests and anticipating their online behaviors is part of web personalization. Therefore, this study presented a general framework of web personalization for a Persian website using an integration of web content and usage mining. Integrating content and usage mining will lead to identifying different groups of customers to predict new customers’ future behavior and interests. Previous studies such as Gurbas et al. (2013) have tried to add content features to the results of usage mining to increase the accuracy and quality of patterns and facilitate their understanding. However, researchers have generally combined the results of usage mining with the web anthology.  However, similar to the results of Herwanto (2016), the results of our study indicated that integrating web usage and content mining will allow removing subjectivity from profile data and keeping it updated. Besides, including semantic information in creating a navigation pattern more accurate recommendations can be provided. This indicates an increase in the quality of the patterns.

The data used in this study was the real data from a Persian language retail website. Since the syntax and semantics of the Persian language are different from English and other languages, recognizing nouns and pronouns, parts of speech tagging, finding word boundaries, tracing, or manipulating characters are also different (Habib, 2021). Besides, the research design and methods used in this study were the combinations combination of the methods used in previous studies. For example, LDA was used for content mining which is similar to the studies of Abdi Ghavidel et al. (2015), which used LDA for Persian metaphor classification and its frequency prediction; Du et al. (2020), Yang & Zhang (2018), Herwanto (2016) and Shotorbani et al. (2016) which used LDA for content mining. Besides, similar to Habib (2021) and Shotorbani et al. (2016), the SVM was used for classification. However, no similar model that combines both content and usage mining for predicting customers’ behavior was used in this study, which distinguishes it from previous studies and makes make it impossible to compare the results with previous studies.

This study will help the literature in the field of web personalization in three ways: first, in this research, five parameters were introduced and calculated to identify users’ behavioral patterns; Second, to extract users’ interests, topic modeling of web pages along with web mining techniques was used, which had not been done before in a Persian-language website and is the innovation of this study. The third contribution of this study to the literature in this field is the use of a dependency distribution algorithm for clustering user navigational patterns that have not been used in previous studies and is one of the innovations of this study.

Owing to the internet’s nature and the lack of physical customers, meeting customers’ needs and improving the quality of services require accurate knowledge of customer priorities; customers, however, are generally not interested in long queries or filling out forms. Therefore, online retailers must gather customers’ preferences from the interactions and information provided by the sales process; it is essential to know how their customers use their websites. Inferring useful results requires in-depth data analysis. Thus, the integration of web usage and content mining is crucial for optimally designing the structure of sales websites, increasing the attraction of potential customers, and retaining existing ones. Because e-commerce has become a competitive space for online retailers, electronic marketers should focus on increasing customer satisfaction. For this purpose, e-business and electronic commerce websites should have quick and accurate access to customers’ needs, customize web pages accordingly, and provide personalized products and services. The combination of the results of web content and usage mining increases the accuracy and quality of the recommender systems, helping them achieve more effective personalization. Therefore, online retailers can use the results of this study to better personalize their websites. In addition, the results can help organizations gather customer data, suggest products, send personalized messages to customers, and more.

 

 

 

 

Saeedeh Rajaee 2021-11-30T08:41:06.616000+00:00
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