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Uta M, Felfernig A, Le VM, Tran TNT, Garber D, Lubos S, Burgstaller T. Knowledge-based recommender systems: overview and research directions. Front Big Data 2024; 7:1304439. [PMID: 38469430 PMCID: PMC10925703 DOI: 10.3389/fdata.2024.1304439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 02/06/2024] [Indexed: 03/13/2024] Open
Abstract
Recommender systems are decision support systems that help users to identify items of relevance from a potentially large set of alternatives. In contrast to the mainstream recommendation approaches of collaborative filtering and content-based filtering, knowledge-based recommenders exploit semantic user preference knowledge, item knowledge, and recommendation knowledge, to identify user-relevant items which is of specific relevance when dealing with complex and high-involvement items. Such recommenders are primarily applied in scenarios where users specify (and revise) their preferences, and related recommendations are determined on the basis of constraints or attribute-level similarity metrics. In this article, we provide an overview of the existing state-of-the-art in knowledge-based recommender systems. Different related recommendation techniques are explained on the basis of a working example from the domain of survey software services. On the basis of our analysis, we outline different directions for future research.
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Affiliation(s)
| | - Alexander Felfernig
- Institute of Software Technology (IST) - Applied Software Engineering & Ai Research Group (ASE), Graz University of Technology, Graz, Austria
| | - Viet-Man Le
- Institute of Software Technology (IST) - Applied Software Engineering & Ai Research Group (ASE), Graz University of Technology, Graz, Austria
| | - Thi Ngoc Trang Tran
- Institute of Software Technology (IST) - Applied Software Engineering & Ai Research Group (ASE), Graz University of Technology, Graz, Austria
| | - Damian Garber
- Institute of Software Technology (IST) - Applied Software Engineering & Ai Research Group (ASE), Graz University of Technology, Graz, Austria
| | - Sebastian Lubos
- Institute of Software Technology (IST) - Applied Software Engineering & Ai Research Group (ASE), Graz University of Technology, Graz, Austria
| | - Tamim Burgstaller
- Institute of Software Technology (IST) - Applied Software Engineering & Ai Research Group (ASE), Graz University of Technology, Graz, Austria
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Noorian Avval AA, Harounabadi A. A hybrid recommender system using topic modeling and prefixspan algorithm in social media. COMPLEX INTELL SYST 2023. [DOI: 10.1007/s40747-022-00958-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
AbstractRoute schema is difficult to plan for tourists, because they demand to pick points of interest (POI) in unknown areas that align with their preferences and limitations. This research proposes a novel personalized method for POI route recommendation that employs contextual data. The proposed approach enhances the existing methods by considering user preferences and multifaceted tourism contexts. Due to the sparsity of the data, the proposed method employs two-level clustering (DBSCAN based on the Manhattan distance) that reduces the time to discover POI. In specific, this approach utilizes the following: first, a topic pattern model is employed to discover the users’ attraction diffusion while improving the user–user similarity model using a novel asymmetric schema. Second, it has used explicit demographic information to alleviate the cold start issue, and third, it proposes a new strategy for assessing user preferences and also combined the context parameters in the form of a vector model with the Term Frequency Inverse Document Frequency technique to find contexts’ similarity. Furthermore, our framework discovers a list of optimal candidate trips by involving personalized POIs in sequential patterns’ mining (SPM); also, it used an adjusted forgotten function to involve the date context of each trip. Based on two datasets (Flickr and Gowalla), our methodology beats other prior approaches in F-score, RMSE, MAP, and NDCG factors in the experimental evaluation.
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Jia J, Chen S, Shang T. A Group Recommendation Algorithm Based on Dividing Subgroup. ADVANCED THEORY AND SIMULATIONS 2022. [DOI: 10.1002/adts.202200557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Affiliation(s)
- Junjie Jia
- School of Computer Science and Engineering Northwest Normal University Lanzhou Gansu 730050 China
| | - Si Chen
- School of Computer Science and Engineering Northwest Normal University Lanzhou Gansu 730050 China
| | - Tianyue Shang
- School of Computer Science and Engineering Northwest Normal University Lanzhou Gansu 730050 China
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A fuzzy content-based group recommender system with dynamic selection of the aggregation functions. Int J Approx Reason 2022. [DOI: 10.1016/j.ijar.2022.08.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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Migliorini S, Quintarelli E, Gambini M, Belussi A, Carra D. Sequence recommendations for groups: A dynamic approach to balance preferences. INFORM SYST 2022. [DOI: 10.1016/j.is.2022.102023] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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Kumar C, Chowdary CR, Shukla D. Automatically detecting groups using locality-sensitive hashing in group recommendations. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.04.028] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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A hybrid group-based movie recommendation framework with overlapping memberships. PLoS One 2022; 17:e0266103. [PMID: 35358269 PMCID: PMC8970527 DOI: 10.1371/journal.pone.0266103] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2021] [Accepted: 03/14/2022] [Indexed: 11/19/2022] Open
Abstract
Recommender Systems (RS) are widely used to help people or group of people in finding their required information amid the issue of ever-growing information overload. The existing group recommender approaches consider users to be part of a single group only, but in real life a user may be associated with multiple groups having conflicting preferences. For instance, a person may have different preferences in watching movies with friends than with family. In this paper, we address this problem by proposing a Hybrid Two-phase Group Recommender Framework (HTGF) that takes into consideration the possibility of users having simultaneous membership of multiple groups. Unlike the existing group recommender systems that use traditional methods like K-Means, Pearson correlation, and cosine similarity to form groups, we use Fuzzy C-means clustering which assigns a degree of membership to each user for each group, and then Pearson similarity is used to form groups. We demonstrate the usefulness of our proposed framework using a movies data set. The experiments were conducted on MovieLens 1M dataset where we used Neural Collaborative Filtering to recommend Top-k movies to each group. The results demonstrate that our proposed framework outperforms the traditional approaches when compared in terms of group satisfaction parameters, as well as the conventional metrics of precision, recall, and F-measure.
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Abstract
Nowadays, recommender systems are present in multiple application domains, such as e-commerce, digital libraries, music streaming services, etc. In the music domain, these systems are especially useful, since users often like to listen to new songs and discover new bands. At the same time, group music consumption has proliferated in this domain, not just physically, as in the past, but virtually in rooms or messaging groups created for specific purposes, such as studying, training, or meeting friends. Single-user recommender systems are no longer valid in this situation, and group recommender systems are needed to recommend music to groups of users, taking into account their individual preferences and the context of the group (when listening to music). In this paper, a group recommender system in the music domain is proposed, and an extensive comparative study is conducted, involving different collaborative filtering algorithms and aggregation methods.
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Bobadilla J, González-Prieto Á, Ortega F, Lara-Cabrera R. Deep learning approach to obtain collaborative filtering neighborhoods. Neural Comput Appl 2021. [DOI: 10.1007/s00521-021-06493-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
AbstractIn the context of recommender systems based on collaborative filtering (CF), obtaining accurate neighborhoods of the items of the datasets is relevant. Beyond particular individual recommendations, knowing these neighbors is fundamental for adding differentiating factors to recommendations, such as explainability, detecting shilling attacks, visualizing item relations, clustering, and providing reliabilities. This paper proposes a deep learning architecture to efficiently and accurately obtain CF neighborhoods. The proposed design makes use of a classification neural network to encode the dataset patterns of the items, followed by a generative process that obtains the neighborhood of each item by means of an iterative gradient localization algorithm. Experiments have been conducted using five popular open datasets and five representative baselines. The results show that the proposed method improves the quality of the neighborhoods compared to the K-Nearest Neighbors (KNN) algorithm for the five selected similarity measure baselines. The efficiency of the proposed method is also shown by comparing its computational requirements with that of KNN.
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Wan Y, Zhu L, Yan C, Zhang B. Attribute interaction aware matrix factorization method for recommendation. INTELL DATA ANAL 2021. [DOI: 10.3233/ida-205407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Matrix factorization (MF) models are effective and easy to expand and are widely used in industry, such as rating prediction and item recommendation. The basic MF model is relatively simple. In practical applications, side information such as attributes or implicit feedback is often combined to improve accuracy by modifying the model and optimizing the algorithm. In this paper, we propose an attribute interaction-aware matrix factorization (AIMF) method for recommendation tasks. We partition the original rating matrix into different sub-matrices according to the attribute interactions, train each sub-matrix independently, and merge all the latent vectors to generate the final score. Since the generated sub-matrices vary in size, an adaptive regularization coefficient optimization strategy and an adaptive latent vector dimension optimization strategy are proposed for sub-matrix training, and a variety of latent vector merging methods are put forward. The method AIMF has two advantages. When the original rating matrix is particularly large, the training time complexity of the MF-based model becomes higher and the update cost of the model is also higher. In AIMF, because each sub-matrix is usually much smaller than the original rating matrix, the training time complexity is greatly reduced after using parallel computing technology. Secondly, in AIMF, it is not necessary to modify the matrix factorization model to incorporate attributes and their interactive information into the model to improve the performance. The experimental results on the two classic public datasets MovieLens 1M and MovieLens 100k show that AIMF can not only effectively improve the accuracy of recommendation, but also make full use of parallel computing technology to improve training efficiency without modifying the matrix factorization model.
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Affiliation(s)
- Yongquan Wan
- School of Computer Engineering and Science, Shanghai University, Shanghai, China
- Department of Computer Science and Technology, Shanghai Jianqiao University, Shanghai, China
| | - Lihua Zhu
- School of Computer Science and Technology, Donghua University, Shanghai, China
| | - Cairong Yan
- School of Computer Science and Technology, Donghua University, Shanghai, China
| | - Bofeng Zhang
- School of Computer Engineering and Science, Shanghai University, Shanghai, China
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Tourist group itinerary design: When the firefly algorithm meets the n-person Battle of Sexes. Knowl Based Syst 2021. [DOI: 10.1016/j.knosys.2021.107257] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
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Combining Cluster-Based Profiling Based on Social Media Features and Association Rule Mining for Personalised Recommendations of Touristic Activities. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11146512] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Tourists who visit a city for the first time may find it difficult to decide on places to visit, as the amount of information in the Web about cultural and leisure activities may be large. Recommender systems address this problem by suggesting the points of interest that fit better with the user’s preferences. This paper presents a novel recommender system that leverages tweets to build user profiles, taking into account not only their personal preferences but also their travel habits. Association rules, which are mined from the previous visits of users documented on Twitter, are used to make the final recommendations of places to visit. The system has been applied to data of the city of Barcelona, and the results show that the use of the social media-based clustering procedure increases its performance according to several relevant metrics.
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Aljunid MF, Huchaiah MD. An efficient hybrid recommendation model based on collaborative filtering recommender systems. CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY 2021. [DOI: 10.1049/cit2.12048] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
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