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Advances in Recommender Systems and Information Retrieval
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Advances in Recommender Systems and Information Retrieval

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (30 December 2023) | Viewed by 6631

Special Issue Editor

Dr. Xi Niu

E-Mail Website
Guest Editor
Department of Software and Information Systems, University of North Carolina at Charlotte, Charlotte, NC, USA
Interests: information retrieval; recommeder systems; text mining

Special Issue Information

Dear Colleagues,

The Special Issue will publish papers on recommender systems and information retrieval (such as search engines) that contain:

  • New principled recommender or information retrieval models or algorithms with sound empirical validation;
  • Observational, experimental and/or theoretical studies yielding new insights into recommendations or retrieval;
  • Accounts of applications of existing recommendation or retrieval techniques that shed light on the strengths and weaknesses of the techniques;
  • Formalization of new recommender system or search engine tasks and of methods for evaluating the performance on those tasks;
  • Development of content (text, image, speech, video, etc.) analysis methods to support recommender systems or search engines;
  • Development of computational models of user information preferences and interaction behaviors;
  • Creation and analysis of evaluation methodologies for recommender systems and search engines; or
  • Surveys of existing work that propose a significant synthesis.

Dr. Xi Niu
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • recommender systems
  • search engines
  • information retrieval

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (5 papers)

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Research

15 pages, 667 KiB  
Article
Personalized News Recommendation Method with Double-Layer Residual Connections and Double Multi-Head Self-Attention Mechanisms
by Dehai Zhang, Zhaoyang Zhu, Zhengwu Wang, Jianxin Wang, Liang Xiao, Yin Chen and Di Zhao
Appl. Sci. 2024, 14(13), 5667; https://doi.org/10.3390/app14135667 - 28 Jun 2024
Viewed by 339
Abstract
In today’s society, there is an urgent need to help users better access information that they are interested in, as there is an increasing amount of news and messages available with the development of the Internet. Many existing methods involve directly inputting text [...] Read more.
In today’s society, there is an urgent need to help users better access information that they are interested in, as there is an increasing amount of news and messages available with the development of the Internet. Many existing methods involve directly inputting text into a pre-trained model, which limits the effectiveness of text feature extraction. The personalized news recommendation model discussed in this article is a model that can enhance feature extraction from news articles. It consists of a candidate news module, a historically accessed news module, and an access prediction module. Using news titles that accurately summarize news content, a model with double multi-head attention mechanisms and double residual structures (DDM) is utilized to better capture the features of news articles historically accessed by users, thereby achieving an improved recommendation functionality. The candidate news module aims to help the model learn representations of news that users are likely to select from the news titles. The user historical click news module primarily serves to enable the model to learn personalized representations of users from news they have previously browsed. The model has been tested on MIND-small. The AUC reached 0.6665, the MRR reached 0.3205, the nDCG@5 reached 0.3532, and the nDCG@10 reached 0.4158. The results indicate this model has achieved good results in the downstream tasks of preprocessing news-title texts. Full article
(This article belongs to the Special Issue Advances in Recommender Systems and Information Retrieval)
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12 pages, 1025 KiB  
Article
De-Selection Bias Recommendation Algorithm Based on Propensity Score Estimation
by Teng Ma and Su Yu
Appl. Sci. 2023, 13(14), 8038; https://doi.org/10.3390/app13148038 - 10 Jul 2023
Cited by 1 | Viewed by 1253
Abstract
There are various biases present in recommendation systems, and recommendation results that do not consider these biases are unfair to users, items, and platforms. To address the problem of selection bias in recommendation systems, in this study, the propensity score was utilized to [...] Read more.
There are various biases present in recommendation systems, and recommendation results that do not consider these biases are unfair to users, items, and platforms. To address the problem of selection bias in recommendation systems, in this study, the propensity score was utilized to mitigate this bias. A selection bias propensity score estimation method (SPE) was developed, which takes into account both user and item information. This method accurately estimates the user’s choice tendency by calculating the degree of difference between the user’s selection rate and the selected preference of the item. Subsequently, the SPE method was combined with the traditional matrix decomposition-based recommendation algorithms, such as the latent semantic model (LFM) and the bias singular value model (BiasSVD). The propensity score was then inversely weighted into the loss function, creating a recommendation model that effectively eliminated selection bias. The experiments were carried out on the public dataset MovieLens, and root mean square error (RMSE) and mean absolute error (MAE) were selected as evaluation indicators and compared with two baseline models and three models with other propensity score estimation methods. Overall, the experimental results demonstrate that the model combined with SPE achieves a minimum increase of 2.00% in RMSE and 2.97% in MAE compared to its baseline model. Moreover, in comparison to other propensity score estimation methods, the SPE method effectively eliminates selection bias in the scoring data, thereby enhancing the performance of the recommendation model. Full article
(This article belongs to the Special Issue Advances in Recommender Systems and Information Retrieval)
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21 pages, 3720 KiB  
Article
Perceiving Conflict of Interest Experts Recommendation System Based on a Machine Learning Approach
by Yunjeong Im, Gyuwon Song and Minsang Cho
Appl. Sci. 2023, 13(4), 2214; https://doi.org/10.3390/app13042214 - 9 Feb 2023
Cited by 3 | Viewed by 1597
Abstract
Academic societies and funding bodies that conduct peer reviews need to select the best reviewers in each field to ensure publication quality. Conventional approaches for reviewer selection focus on evaluating expertise based on research relevance by subject or discipline. An improved perceiving conflict [...] Read more.
Academic societies and funding bodies that conduct peer reviews need to select the best reviewers in each field to ensure publication quality. Conventional approaches for reviewer selection focus on evaluating expertise based on research relevance by subject or discipline. An improved perceiving conflict of interest (CoI) reviewer recommendation process that combines the five expertise indices and graph analysis techniques is proposed in this paper. This approach collects metadata from the academic database and extracts candidates based on research field similarities utilizing text mining; then, the candidate scores are calculated and ranked through a professionalism index-based analysis. The highly connected subgraphs (HCS) algorithm is used to cluster similar researchers based on their association or intimacy in the researcher network. The proposed method is evaluated using root mean square error (RMSE) indicators for matching the field of publication and research fields of the recommended experts using keywords of papers published in Korean journals over the past five years. The results show that the system configures a group of Top-K reviewers with an RMSE 0.76. The proposed method can be applied to the academic society and national research management system to realize fair and efficient screening and management. Full article
(This article belongs to the Special Issue Advances in Recommender Systems and Information Retrieval)
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28 pages, 676 KiB  
Article
Utilizing Alike Neighbor Influenced Similarity Metric for Efficient Prediction in Collaborative Filter-Approach-Based Recommendation System
by Raushan Kumar Singh, Pradeep Kumar Singh, Juginder Pal Singh, Akhilesh Kumar Singh and Seshathiri Dhanasekaran
Appl. Sci. 2022, 12(22), 11686; https://doi.org/10.3390/app122211686 - 17 Nov 2022
Cited by 1 | Viewed by 1398
Abstract
The most popular method collaborative filter approach is primarily used to handle the information overloading problem in E-Commerce. Traditionally, collaborative filtering uses ratings of similar users for predicting the target item. Similarity calculation in the sparse dataset greatly influences the predicted rating, as [...] Read more.
The most popular method collaborative filter approach is primarily used to handle the information overloading problem in E-Commerce. Traditionally, collaborative filtering uses ratings of similar users for predicting the target item. Similarity calculation in the sparse dataset greatly influences the predicted rating, as less count of co-rated items may degrade the performance of the collaborative filtering. However, consideration of item features to find the nearest neighbor can be a more judicious approach to increase the proportion of similar users. In this study, we offer a new paradigm for raising the rating prediction accuracy in collaborative filtering. The proposed framework uses rated items of the similar feature of the ’most’ similar individuals, instead of using the wisdom of the crowd. The reliability of the proposed framework is evaluated on the static MovieLens datasets and the experimental results corroborate our anticipations. Full article
(This article belongs to the Special Issue Advances in Recommender Systems and Information Retrieval)
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15 pages, 689 KiB  
Article
An Area Recommendation Method Using Similarity Analysis for Play Patterns in MMORPG
by Yuyeon Jo, Shengmin Cui and Inwhee Joe
Appl. Sci. 2022, 12(21), 10833; https://doi.org/10.3390/app122110833 - 26 Oct 2022
Viewed by 1301
Abstract
Recently, game companies have been increasingly offering a variety of content in their games. The more this happens, the more players will need to consider what is best for them. Players who have played such a game may not find it difficult to [...] Read more.
Recently, game companies have been increasingly offering a variety of content in their games. The more this happens, the more players will need to consider what is best for them. Players who have played such a game may not find it difficult to play, but those who are not used to play may have a hard time finding content. Therefore, in this paper, we try to give a customized guide to players in Massively Multiplayer Online Role-Playing Games (MMORPGs). We compare the similarity of growth speeds and visited areas, and then utilize this information to recommend the most similar characters. In this work, the K-means algorithm is used for clustering based on location, the Euclidean distance is calculated to recommend similar characters with similar growth speeds. In addition, Jaccard Similarity is introduced to recommend similar characters with similar access areas. Finally, we propose a method to recommend suitable areas by applying the access speed to the recommended characters in the previous steps. Our method achieves Precision and Recall of 0.74 and 0.81, respectively, on the real-life PvE (Player VS Environment) dataset. Full article
(This article belongs to the Special Issue Advances in Recommender Systems and Information Retrieval)
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