A Content-Based Model for Tag Recommendation in Software Information Sites

2019 ◽  
Author(s):  
Reza Gharibi ◽  
Atefeh Safdel ◽  
Seyed Mostafa Fakhrahmad ◽  
Mohammad Hadi Sadreddini
Keyword(s):  
Recommendation Systems ◽  
Short Description ◽  
Tag Recommendation ◽  
Stack Overflow ◽  
Proposed Model ◽  
Software Information ◽  
Exchange Network ◽  
Textual Content ◽  
Label Content

Abstract Developers use software information sites such as Stack Overflow to get and give information on various subjects. These sites allow developers to label content with tags as a short description. Tags, then, are used to describe, categorize and search the posted content. However, tags might be noisy, and postings may become poorly categorized since people tag a posting based on their knowledge of its content and other existing tags. To keep the content well organized, tag recommendation systems can help users by suggesting appropriate tags for their posted content. In this paper, we propose a tag recommendation scheme that uses the textual content of already tagged postings to recommend suitable tags for newly posted content. Our approach combines multi-label classification and textual similarity techniques to improve the performance of tag recommendation. We evaluate the performance of the proposed scheme on 11 software information sites from the Stack Exchange network. The results show a significant improvement over TagCombine, TagMulRec and FastTagRec, which are well-known tag recommendation systems. On average, the proposed model outperforms TagCombine, TagMulRec and FastTagRec by 26.2, 15.9 and 13.8% in terms of Recall@5 and by 16.9, 12.4 and 9.4% in terms of Recall@10, respectively.

scholarly journals DeepTagRec: A Content-cum-User Based Tag Recommendation Framework for Stack Overflow

2019 ◽  
pp. 125-131 ◽  
Author(s):  
Suman Kalyan Maity ◽  
Abhishek Panigrahi ◽  
Sayan Ghosh ◽  
Arundhati Banerjee ◽  
Pawan Goyal ◽  
...  
Keyword(s):  
Tag Recommendation ◽  
Stack Overflow

Research of User-Based Collaborative Filtering Algorithm in Tag Recommendation

2014 ◽  
Vol 926-930 ◽  
pp. 3677-3683
Author(s):  
Qi Zhi Qiu ◽  
Jin Bao Zhang
Keyword(s):  
Collaborative Filtering ◽  
Recommendation Systems ◽  
Tag Recommendation ◽  
Rating Data ◽  
Filtering Algorithm ◽  
Collaborative Filtering Algorithm

Collaborative Filtering (CF) is one of the most popular techniques used in recommendation systems. User-based CF uses rating data to calculate similarity between users and is effective in traditional recommendation systems. However, it does not work well when applied in tag recommendation systems as there are tags with same semantics which does not match literally. Based on tag concept category, the improvement of traditional CF is achieved by compressing the dataset, analyzing the users preference and extracting the tags objective characteristics. The result of experiment shows the more satisfied accuracy than the traditional one does.

scholarly journals Multipointer Coattention Recommendation with Gated Neural Fusion between ID Embedding and Reviews

2022 ◽  
Vol 12 (2) ◽  
pp. 594
Author(s):  
Jianjie Shao ◽  
Jiwei Qin ◽  
Wei Zeng ◽  
Jiong Zheng
Keyword(s):  
Neural Network ◽  
Recommendation Systems ◽  
Experimental Results ◽  
Interaction Information ◽  
Word Level ◽  
Learning Scheme ◽  
Proposed Model ◽  
Sparsity Problem

Recently, the interaction information from reviews has been modeled to acquire representations between users and items and improve the sparsity problem in recommendation systems. Reviews are more responsive to information about users’ preferences for the different aspects and attributes of items. However, how to better construct the representation of users (items) still needs further research. Inspired by the interaction information from reviews, auxiliary ID embedding information is used to further enrich the word-level representation in the proposed model named MPCAR. In this paper, first, a multipointer learning scheme is adopted to extract the most informative reviews from user and item reviews and represent users (items) in a word-by-word manner. Then, users and items are embedded to extract the ID embedding that can reveal the identity of users (items). Finally, the review features and ID embedding are input to the gated neural network for effective fusion to obtain richer representations of users and items. We randomly select ten subcategory datasets from the Amazon dataset to evaluate our algorithm. The experimental results show that our algorithm can achieve the best results compared to other recommendation approaches.

scholarly journals EnTagRec ++: An enhanced tag recommendation system for software information sites

2017 ◽  
Vol 23 (2) ◽  
pp. 800-832 ◽  
Author(s):  
Shaowei Wang ◽  
David Lo ◽  
Bogdan Vasilescu ◽  
Alexander Serebrenik
Keyword(s):  
Recommendation System ◽  
Tag Recommendation ◽  
Software Information

Enhanced hotel recommendation method addressing the deviation between overall rating and detailed criteria ratings on Tripadvisor.com

2021 ◽  
pp. 1-16
Author(s):  
Leiguang Zhong ◽  
Yiyue Luo ◽  
Xin Zhang ◽  
Hongyu Zhang ◽  
Jianqiang Wang
Keyword(s):  
Decision Making ◽  
Service Providers ◽  
Recommendation Systems ◽  
Actual Data ◽  
Proposed Model ◽  
Hotel Service ◽  
Rating Information ◽  
Comprehensive Score

User rating information on multiple predefined aspects gathered by hotel recommendation systems generally shows a deviation between the overall rating and detailed criteria ratings. In this study, to address this deviation, we proposed a novel hotel recommendation method that clusters users with different preferences into different groups using the K-means algorithm. Moreover, we allocated weights to different criteria and obtained a comprehensive score. A case study on actual data from Tripadvisor.com showed that compared with three other models, our proposed model demonstrated a more impressive performance. This research can offer advantages to hotel service providers and customers in terms of decision making.

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