Journal articles: 'Semantic Recommender'

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Hendrik, Khamidudin Azzakiy, and Aditya Budi Utomo. "Semantic Hybrid Recommender System." Advanced Science Letters 21, no. 10 (October 1, 2015): 3363–66. http://dx.doi.org/10.1166/asl.2015.6499.

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Sulieman, Dalia, Maria Malek, Hubert Kadima, and Dominique Laurent. "Toward Social-Semantic Recommender Systems." International Journal of Information Systems and Social Change 7, no. 1 (January 2016): 1–30. http://dx.doi.org/10.4018/ijissc.2016010101.

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In this article, the authors consider the basic problem of recommender systems that is identifying a set of users to whom a given item is to be recommended. In practice recommender systems are run against huge sets of users, and the problem is then to avoid scanning the whole user set in order to produce the recommendation list. To cope with problem, they consider that users are connected through a social network and that taxonomy over the items has been defined. These two kinds of information are respectively called social and semantic information. In their contribution the authors suggest combining social information with semantic information in one algorithm in order to compute recommendation lists by visiting a limited part of the social network. In their experiments, the authors use two real data sets, namely Amazon.com and MovieLens, and they compare their algorithms with the standard item-based collaborative filtering and hybrid recommendation algorithms. The results show satisfying accuracy values and a very significant improvement of performance, by exploring a small part of the graph instead of exploring the whole graph.

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Fraihat, Salam, and Qusai Shambour. "A Framework of Semantic Recommender System for e-Learning." Journal of Software 10, no. 3 (March 2015): 317–30. http://dx.doi.org/10.17706/jsw.10.3.317-330.

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Cantador, Iván, Pablo Castells, and Alejandro Bellogín. "An Enhanced Semantic Layer for Hybrid Recommender Systems." International Journal on Semantic Web and Information Systems 7, no. 1 (January 2011): 44–78. http://dx.doi.org/10.4018/jswis.2011010103.

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Recommender systems have achieved success in a variety of domains, as a means to help users in information overload scenarios by proactively finding items or services on their behalf, taking into account or predicting their tastes, priorities, or goals. Challenging issues in their research agenda include the sparsity of user preference data and the lack of flexibility to incorporate contextual factors in the recommendation methods. To a significant extent, these issues can be related to a limited description and exploitation of the semantics underlying both user and item representations. The authors propose a three-fold knowledge representation, in which an explicit, semantic-rich domain knowledge space is incorporated between user and item spaces. The enhanced semantics support the development of contextualisation capabilities and enable performance improvements in recommendation methods. As a proof of concept and evaluation testbed, the approach is evaluated through its implementation in a news recommender system, in which it is tested with real users. In such scenario, semantic knowledge bases and item annotations are automatically produced from public sources.

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Hawashin, Bilal, Shadi Alzubi, Tarek Kanan, and Ayman Mansour. "An efficient semantic recommender method forArabic text." Electronic Library 37, no. 2 (April 1, 2019): 263–80. http://dx.doi.org/10.1108/el-12-2018-0245.

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PurposeThis paper aims to propose a new efficient semantic recommender method for Arabic content.Design/methodology/approachThree semantic similarities were proposed to be integrated with the recommender system to improve its ability to recommend based on the semantic aspect. The proposed similarities are CHI-based semantic similarity, singular value decomposition (SVD)-based semantic similarity and Arabic WordNet-based semantic similarity. These similarities were compared with the existing similarities used by recommender systems from the literature.FindingsExperiments show that the proposed semantic method using CHI-based similarity and using SVD-based similarity are more efficient than the existing methods on Arabic text in term of accuracy and execution time.Originality/valueAlthough many previous works proposed recommender system methods for English text, very few works concentrated on Arabic Text. The field of Arabic Recommender Systems is largely understudied in the literature. Aside from this, there is a vital need to consider the semantic relationships behind user preferences to improve the accuracy of the recommendations. The contributions of this work are the following. First, as many recommender methods were proposed for English text and have never been tested on Arabic text, this work compares the performance of these widely used methods on Arabic text. Second, it proposes a novel semantic recommender method for Arabic text. As this method uses semantic similarity, three novel base semantic similarities were proposed and evaluated. Third, this work would direct the attention to more studies in this understudied topic in the literature.

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Jaglan, G., and S. K. Malik. "Blending Semantic Web with Recommender Systems." International Journal of Computer Sciences and Engineering 6, no. 5 (May 31, 2018): 523–31. http://dx.doi.org/10.26438/ijcse/v6i5.523531.

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Vellino, Andre. "Recommending research articles using citation data." Library Hi Tech 33, no. 4 (November 16, 2015): 597–609. http://dx.doi.org/10.1108/lht-06-2015-0063.

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Purpose – The purpose of this paper is to present an empirical comparison between the recommendations generated by a citation-based recommender for research articles in a digital library with those produced by a user-based recommender (ExLibris “bX”). Design/methodology/approach – For these computer experiments 9,453 articles were randomly selected from among 6.6 M articles in a digital library as starting points for generating recommendations. The same seed articles were used to generate recommendations in both recommender systems and the resulting recommendations were compared according to the “semantic distance” between the seed articles and the recommended ones, the coverage of the recommendations and the spread in publication dates between the seed and the resulting recommendations. Findings – Out of the 9,453 test runs, the recommendation coverage was 30 per cent for the user-based recommender vs 24 per cent for the citation-based one. Only 12 per cent of seed articles produced recommendations with both recommenders and none of the recommended articles were the same. Both recommenders yielded recommendations with about the same semantic distance between the seed article and the recommended articles. The average differences between the publication dates of the recommended articles and the seed articles is dramatically greater for the citation-based recommender (+7.6 years) compared with the forward-looking user-based recommender. Originality/value – This paper reports on the only known empirical comparison between the Ex Librix “bX” recommendation system and a citation-based collaborative recommendation system. It extends prior preliminary findings with a larger data set and with an analysis of the publication dates of recommendations for each system.

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Durao, Frederico, and Peter Dolog. "Semantic Grounding Strategies for Tagbased Recommender Systems." International journal of Web & Semantic Technology 2, no. 4 (October 30, 2011): 67–79. http://dx.doi.org/10.5121/ijwest.2011.2405.

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García-Sánchez, Francisco, Ricardo Colomo-Palacios, and Rafael Valencia-García. "A social-semantic recommender system for advertisements." Information Processing & Management 57, no. 2 (March 2020): 102153. http://dx.doi.org/10.1016/j.ipm.2019.102153.

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Shu, Qiong, He Ping Chen, and Jin Guang Gu. "Semantic Reasoning-Based Chinese Recipe Recommender System." Advanced Materials Research 718-720 (July 2013): 1998–2004. http://dx.doi.org/10.4028/www.scientific.net/amr.718-720.1998.

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For the blank of the recommender system for chinese recipes, this paper uses OWLS-WSDL to build a semantic reasoning-based chinese recipe recommender system. This system through the tool of Protégé to establish the ontology of chinese recipes and then add rules for ontology reasoning. On this basis bring out a catering algorithm, using Euclidean distance and Jaccard to calculate the similarity between the dishes. According to the similarity as well as user preference, provides a quick means of siding dishes for users.

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Montuschi, Paolo, Fabrizio Lamberti, Valentina Gatteschi, and Claudio Demartini. "A Semantic Recommender System for Adaptive Learning." IT Professional 17, no. 5 (September 2015): 50–58. http://dx.doi.org/10.1109/mitp.2015.75.

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Moses, Sharon J., and L. D. Dhinesh Babu. "Buyagain Grocery Recommender Algorithm for Online Shopping of Grocery and Gourmet Foods." International Journal of Web Services Research 15, no. 3 (July 2018): 1–17. http://dx.doi.org/10.4018/ijwsr.2018070101.

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Online shopping of grocery and gourmet products differ from other shopping activities due to its routine nature of buy-consume-buy. The existing recommendation algorithms of ecommerce websites are suitable only to render recommendation for products of one time purchase. So, in order to identify and recommend the products that users are likely to buy again and again, a novel recommender algorithm is proposed based on linguistic decision analysis model. The proposed buyagain recommender algorithm finds the semantic value of the user comments and computes the semantic value along with the user rating to render recommendation to the user. The efficiency of the buyagain recommender algorithm is evaluated using the grocery and gourmet dataset of amazon ecommerce websites. The end result proves that the algorithm accurately recommends the product that the user likes to purchase once again.

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Angelis, Sotiris, Konstantinos Kotis, and Dimitris Spiliotopoulos. "Semantic Trajectory Analytics and Recommender Systems in Cultural Spaces." Big Data and Cognitive Computing 5, no. 4 (December 16, 2021): 80. http://dx.doi.org/10.3390/bdcc5040080.

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Semantic trajectory analytics and personalised recommender systems that enhance user experience are modern research topics that are increasingly getting attention. Semantic trajectories can efficiently model human movement for further analysis and pattern recognition, while personalised recommender systems can adapt to constantly changing user needs and provide meaningful and optimised suggestions. This paper focuses on the investigation of open issues and challenges at the intersection of these two topics, emphasising semantic technologies and machine learning techniques. The goal of this paper is twofold: (a) to critically review related work on semantic trajectories and knowledge-based interactive recommender systems, and (b) to propose a high-level framework, by describing its requirements. The paper presents a system architecture design for the recognition of semantic trajectory patterns and for the inferencing of possible synthesis of visitor trajectories in cultural spaces, such as museums, making suggestions for new trajectories that optimise cultural experiences.

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Zheng, Jianxing, and Yanjie Wang. "Personalized Recommendations Based on Sentimental Interest Community Detection." Scientific Programming 2018 (August 5, 2018): 1–14. http://dx.doi.org/10.1155/2018/8503452.

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Communities have become a popular platform of mining interests for recommender systems. The semantics of topics reflect users’ implicit interests. Sentiments on topics imply users’ sentimental tendency. People with common sentiments can form resonant communities of interest. In this paper, a resonant sentimental interest community-based recommendation model is proposed to improve the accuracy performance of recommender systems. First, we learn the weighted semantics vector and sentiment vector to model semantic and sentimental user profiles. Then, by combining semantic and sentimental factors, resonance relationship is computed to evaluate the resonance relationship of users. Finally, based on resonance relationships, resonant community is detected to discover a resonance group to make personalized recommendations. Experimental results show that the proposed model is more effective in finding semantics-related sentimental interests than traditional methods.

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Khalid Khoshnaw, Karwan Hoshyar, Zardasht Abdulaziz Abdulkarim Shwany, Twana Mustafa, and Shayda Khudhur Ismail. "Mobile recommender system based on smart city graph." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 3 (March 1, 2022): 1771. http://dx.doi.org/10.11591/ijeecs.v25.i3.pp1771-1776.

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Mobile recommender systems have changed the way people find items, purposes of intrigue, administrations, or even new companions. The innovation behind mobile recommender systems has developed to give client inclinations and social impacts. This paper introduces a first way to build a mobile recommendation system based on smart city graphs that appear topic features, user profiles, and impacts acquired from social connections. It exploits graph centrality measures to expand customized recommendations from the semantic information represented in the graph. The graph shows and chooses graph algorithms for computing chart centrality that is the center of the mobile recommender system are exhibited. Semantic ideas, for example, semantic transcendence and likeness measures, are adjusted to the graph model. Usage challenges confronted to settle execution issues are additionally examined.

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Sellami, Khaled, Rabah Kassa, and Pierre F. Tiako. "Extending Recommender System by Incorporating Semantic-social Information." Research Journal of Applied Sciences, Engineering and Technology 11, no. 7 (November 5, 2015): 674–84. http://dx.doi.org/10.19026/rjaset.11.2030.

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Khalil Sharif, Halo, and Kamaran Hama Ali. A. Faraj. "Semantic Web Recommender System over Different Operating Platforms." UHD Journal of Science and Technology 6, no. 2 (August 10, 2022): 19–23. http://dx.doi.org/10.21928/uhdjst.v6n2y2022.pp19-23.

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Semantic-Web Recommender System (SWRS) evaluation over different operating systems (OSs) used to facilitate and improve human electronic recommendation management (HERM). The HERM is address the needs of user and dataset of movie in our proposed system through internetworking means which increase the speed of automated recommendation and enhance the goodness of SWRS and services also electronically to select right movies-title to user demand. Furthermore, it will be a benefit for selection a right favor by user for right selection from (i.e., 3000 records in dataset of movie-Lens) in the backend. There are a direct relation between time-consume of selection movie-title, also the time-consume, and accuracy. The two-mentioned parameters, namely, time-consume and accuracy over two different operation system (OSs) which designed by web technology Python. In our research, SWR system is proposed; it is provide with some recommendation methods. The system designed and improved using content-based algorithm (CBA). Investigational results indicate that the developed algorithm technique confident a reasonable performance such as accuracy and time consuming compared to other existing works with a testing average accuracy of 85.63 for windows and 88.35 for Linux operating system. In conclusion, SWRS investigated on two different operating platforms and could be seen that the Linux is faster than windows in accuracy and time consuming.

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Haiba, Maria El, Lamyaa Elbassiti, and Rachida Ajhoun. "A Semantic Recommender Engine for Idea Generation Improvement." Computer and Information Science 11, no. 3 (July 29, 2018): 112. http://dx.doi.org/10.5539/cis.v11n3p112.

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In order to develop the ability to be competitive in considering rapidly growing global market and enormously changing in technology, organizations are looking for up-to-date procedures to respond to all these transformations. Being smart and innovative is actually the most significant pillars of successful organization strategies. In other words, organizations need to encourage learning, manage knowledge and create innovative ideas. A major issue of creative ideation is improving the quality of the ideas generated. In this paper, we propose a semantic recommender engine for idea generation in order to assist organizations to improve their ways of generating new ideas. Through this novel system, innovation actors will be able to consider new perspectives, make new connections, think differently and thus produce new promising ideas. We initially introduce the concept behind a smart organization, explore the idea generation in such organizations and examine the role of recommender systems for managing this stage and identifying breakthrough ideas. Next, we present the context of design, the conceptual architecture of the suggested system and finally expand the workflow of semantic similarity matching of ideas with a focus on the key components of the semantic recommendation engine.

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Davoodi, Elnaz, Keivan Kianmehr, and Mohsen Afsharchi. "A semantic social network-based expert recommender system." Applied Intelligence 39, no. 1 (October 12, 2012): 1–13. http://dx.doi.org/10.1007/s10489-012-0389-1.

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Afolabi, Ibukun Tolulope, Opeyemi Samuel Makinde, and Olufunke Oyejoke Oladipupo. "Semantic Web mining for Content-Based Online Shopping Recommender Systems." International Journal of Intelligent Information Technologies 15, no. 4 (October 2019): 41–56. http://dx.doi.org/10.4018/ijiit.2019100103.

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Currently, for content-based recommendations, semantic analysis of text from webpages seems to be a major problem. In this research, we present a semantic web content mining approach for recommender systems in online shopping. The methodology is based on two major phases. The first phase is the semantic preprocessing of textual data using the combination of a developed ontology and an existing ontology. The second phase uses the Naïve Bayes algorithm to make the recommendations. The output of the system is evaluated using precision, recall and f-measure. The results from the system showed that the semantic preprocessing improved the recommendation accuracy of the recommender system by 5.2% over the existing approach. Also, the developed system is able to provide a platform for content-based recommendation in online shopping. This system has an edge over the existing recommender approaches because it is able to analyze the textual contents of users feedback on a product in order to provide the necessary product recommendation.

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Pliakos, Konstantinos, and Constantine Kotropoulos. "Building an Image Annotation and Tourism Recommender System." International Journal on Artificial Intelligence Tools 24, no. 05 (October 2015): 1540021. http://dx.doi.org/10.1142/s0218213015400217.

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The interest in image annotation and recommendation has been increased due to the ever rising amount of data uploaded to the web. Despite the many efforts undertaken so far, accuracy or efficiency still remain open problems. Here, a complete image annotation and tourism recommender system is proposed. It is based on the probabilistic latent semantic analysis (PLSA) and hypergraph ranking, exploiting the visual attributes of the images and the semantic information found in image tags and geo-tags. In particular, semantic image annotation resorts to the PLSA, exploiting the textual information in image tags. It is further complemented by visual annotation based on visual image content classification. Tourist destinations, strongly related to a query image, are recommended using hypergraph ranking enhanced by enforcing group sparsity constraints. Experiments were conducted on a large image dataset of Greek sites collected from Flickr. The experimental results demonstrate the merits of the proposed model. Semantic image annotation by means of the PLSA has achieved an average precision of 92% at 10% recall. The accuracy of content-based image classification is 82, 6%. An average precision of 92% is measured at 1% recall for tourism recommendation.

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Frikha, Mohamed, Mohamed Mhiri, and Faiez Gargouri. "Social Trust Based Semantic Tourism Recommender System: A Case of Medical Tourism in Tunisia." European Journal of Tourism Research 17 (October 1, 2017): 59–82. http://dx.doi.org/10.54055/ejtr.v17i.294.

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Based on the assumption that users generally have a tendency to use items recommended by friends rather than strangers and that trust among friends positively correlates with user preference, we decided to refer to research conducted on the emerging field of trust-based recommender system. We propose to integrate the temporal factor in measuring trust between social network friends. A Trusted Friend’s calculation method is developed for determining social trusted friends in Facebook. We have, accordingly, demonstrated the importance of the interactions’ time between users. Afterwards, we have used this method in a semantic tourism recommender system as a smart e-tourism tool able to recommend items based on the users’ preferences and their trusted friends’ preferences. We have also applied our tourism recommender system for the case of medical tourism in Tunisia to help users interested in traveling to Tunisia for medical purposes. Finally, we have implemented the system and collected feedback from real users to evaluate the quality of recommendation and prove its importance in improving the medical tourism domain.

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Desku, Astrit, Bujar Raufi, Artan Luma, and Besnik Selimi. "Recommender System for Software Engineering using SQL Semantic Search." International Journal of Engineering and Advanced Technology 11, no. 4 (April 30, 2022): 119–22. http://dx.doi.org/10.35940/ijeat.d3494.0411422.

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Recommender Systems are software tools that can assist developers with a wide range of activities, from reusing codes to suggest developers what to do during development of these systems. This paper outlines an approach to generating recommendation using SQL Semantic Search. Performance measurement of this recommender system is conducted by calculating precision, recall and F1-measure. Subjective evaluations consisted of 10 experienced developers for validating the recommendation. A statistical test t-Test is used to compare the means of two approaches of evaluations.

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Baizal, Zk Abdurahman, and Nur Rahmawati. "Conversational Recommender System with Explanation Facility Using Semantic Reasoning." International Journal on Information and Communication Technology (IJoICT) 2, no. 1 (July 1, 2016): 1. http://dx.doi.org/10.21108/ijoict.2016.21.64.

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Conversational recommender system is system that provides dialogue as user guide to obtain information from the user, in order to obtain preference for products needed. This research implements conversational recommender system with knowledge-based in the smartphone domain with an explanation facility. The recommended products are obtained based on the functional requirements of the user. Therefore, this study use ontology model as a knowledge to be more flexible in finding products that is suitable with the functional requirements of the user that is by tracing a series of semantic based on relationships in order to obtain the user model. By exploiting the relationship between instances of user models, the explanation facility generated can be more natural. Our filtering method uses semantic reasoning with inference method to avoid overspecialization. The evaluation show that the performance of our recommender system with explanation facilities is more efficient than the recommendation system without explanation facility, that can be seen from the number of iterations. We also notice that our system has accuracy of 84%.

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Pribadi, Nabil, Riyanarto Sarno, Adhatus Ahmadiyah, and Kelly Sungkono. "Semantic Recommender System Based on Semantic Similarity Using FastText and Word Mover’s Distance." International Journal of Intelligent Engineering and Systems 14, no. 2 (April 30, 2021): 377–85. http://dx.doi.org/10.22266/ijies2021.0430.34.

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Alfarhood, Sultan, Susan Gauch, and Kevin Labille. "Semantic Distance Spreading Across Entities in Linked Open Data." Information 10, no. 1 (January 2, 2019): 15. http://dx.doi.org/10.3390/info10010015.

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Recommender systems can utilize Linked Open Data (LOD) to overcome some challenges, such as the item cold start problem, as well as the problem of explaining the recommendation. There are several techniques in exploiting LOD in recommender systems; one approach, called Linked Data Semantic Distance (LDSD), considers nearby resources to be recommended by calculating a semantic distance between resources. The LDSD approach, however, has some drawbacks such as its inability to measure the semantic distance resources that are not directly linked to each other. In this paper, we first propose another variation of the LDSD approach, called wtLDSD, by extending indirect distance calculations to include the effect of multiple links of differing properties within LOD, while prioritizing link properties. Next, we introduce an approach that broadens the coverage of LDSD-based approaches beyond resources that are more than two links apart. Our experimental results show that approaches we propose improve the accuracy of the LOD-based recommendations over our baselines. Furthermore, the results show that the propagation of semantic distance calculation to reflect resources further away in the LOD graph extends the coverage of LOD-based recommender systems.

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Javed, Umair, Kamran Shaukat, Ibrahim A. Hameed, Farhat Iqbal, Talha Mahboob Alam, and Suhuai Luo. "A Review of Content-Based and Context-Based Recommendation Systems." International Journal of Emerging Technologies in Learning (iJET) 16, no. 03 (February 12, 2021): 274. http://dx.doi.org/10.3991/ijet.v16i03.18851.

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In our work, we have presented two widely used recommendation systems. We have presented a context-aware recommender system to filter the items associated with user’s interests coupled with a context-based recommender system to prescribe those items. In this study, context-aware recommender systems perceive the user’s location, time, and company. The context-based recommender system retrieves patterns from World Wide Web-based on the user’s past interactions and provides future news recommendations. We have presented different techniques to support media recommendations for smartphones, to create a framework for context-aware, to filter E-learning content, and to deliver convenient news to the user. To achieve this goal, we have used content-based, collaborative filtering, a hybrid recommender system, and implemented a Web ontology language (OWL). We have also used the Resource Description Framework (RDF), JAVA, machine learning, semantic mapping rules, and natural ontology languages that suggest user items related to the search. In our work, we have used E-paper to provide users with the required news. After applying the semantic reasoning approach, we have concluded that by some means, this approach works similarly as a content-based recommender system since by taking the gain of a semantic approach, we can also recommend items according to the user’s interests. In a content-based recommender system, the system provides additional options or results that rely on the user’s ratings, appraisals, and interests.

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Qassimi, Sara, and El Hassan Abdelwahed. "Towards a Semantic Graph-based Recommender System. A Case Study of Cultural Heritage." JUCS - Journal of Universal Computer Science 27, no. 7 (July 28, 2021): 714–33. http://dx.doi.org/10.3897/jucs.70330.

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Research on digital cultural heritage has raised the importance of providing visitors with relevant assistance before and during their visits. With the advent of the social web, the cultural heritage area is affected by the problem of information overload. Indeed, a large number of available resources have emerged coming from the social information systems (SocIS). Therefore, visitors are swamped with enormous choices in their visited cities. SocIS platforms use the features of collaborative tagging, named folksonomy, to commonly contribute to the management of the shared resources. However, collaborative tagging uses uncontrolled vocabulary which semanti- cally weakens the description of resources, consequently decreases their classification, clustering, thereby their recommendation. Therefore, the shared resources have to be pertinently described to ameliorate their recommendations. In this paper, we aim to enhance the cultural heritage visits by suggesting semantically related places that are most likely to interest a visitor. Our proposed approach represents a semantic graph-based recommender system of cultural heritage places through two steps; (1) constructing an emergent semantic description that semantically augments the place and (2) effectively modeling the emerging graphs representing the semantic relatedness of similar cultural heritage places and their related tags. The experimental evaluation shows relevant results attesting the efficiency of the proposed approach.

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Bouihi, Bouchra, and Mohamed Bahaj. "Ontology and Rule-Based Recommender System for E-learning Applications." International Journal of Emerging Technologies in Learning (iJET) 14, no. 15 (August 1, 2019): 4. http://dx.doi.org/10.3991/ijet.v14i15.10566.

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The continuous growth of the internet has given rise to an overwhelming mass of learning materials. Which has increased the demand for a recommendation system to filter information and to deliver the learning materials that fit learners learning context. In this paper, we propose an architecture of a semantic web based recommender system. The proposed architecture is a redesigned architecture of the classical 3-tiers web application architecture with an additional semantic layer. This layer holds two semantic subsystems: an Ontology-based subsystem and SWRL (Semantic Web Rule Language) rules one. The Ontology subsystem is used as a reusable and sharable domain knowledge to model the learning content and context. The SWRL rules are used as a recommendation and filtering technique based on learning object relevance and weighting. These rules are organized into four categories: Learning History Rules (LHR), Learning Performance Rules (LPR), Learning Social Network Rules (LSNR) and Learning Pathway Rules (PR).

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Movahedian, Hamed, and Mohammad Reza Khayyambashi. "A semantic recommender system based on frequent tag pattern." Intelligent Data Analysis 19, no. 1 (January 1, 2015): 109–26. http://dx.doi.org/10.3233/ida-140699.

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Gómez-Martínez, Elena, Marino Linaje, Fernando Sánchez-Figueroa, Andrés Iglesias-Pérez, Juan Carlos Preciado, Rafael González-Cabero, and José Merseguer. "A semantic approach for designing Assistive Software Recommender systems." Journal of Systems and Software 104 (June 2015): 166–78. http://dx.doi.org/10.1016/j.jss.2015.03.009.

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Codina, Victor, Francesco Ricci, and Luigi Ceccaroni. "Distributional semantic pre-filtering in context-aware recommender systems." User Modeling and User-Adapted Interaction 26, no. 1 (March 31, 2015): 1–32. http://dx.doi.org/10.1007/s11257-015-9158-2.

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Xu, Guandong, Yanhui Gu, Peter Dolog, Yanchun Zhang, and Masaru Kitsuregawa. "SemRec: A Semantic Enhancement Framework for Tag Based Recommendation." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 1 (August 4, 2011): 1267–72. http://dx.doi.org/10.1609/aaai.v25i1.8080.

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Collaborative tagging services provided by various social web sites become popular means to mark web resources for different purposes such as categorization, expression of a preference and so on. However, the tags are of syntactic nature, in a free style and do not reflect semantics, resulting in the problems of redundancy, ambiguity and less semantics. Current tag-based recommender systems mainly take the explicit structural information among users, resources and tags into consideration, while neglecting the important implicit semantic relationships hidden in tagging data. In this study, we propose a Semantic Enhancement Recommendation strategy (SemRec), based on both structural information and semantic information through a unified fusion model. Extensive experiments conducted on two real datasets demonstarte the effectiveness of our approaches.

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Radlinski, Filip, Craig Boutilier, Deepak Ramachandran, and Ivan Vendrov. "Subjective Attributes in Conversational Recommendation Systems: Challenges and Opportunities." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 11 (June 28, 2022): 12287–93. http://dx.doi.org/10.1609/aaai.v36i11.21492.

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The ubiquity of recommender systems has increased the need for higher-bandwidth, natural and efficient communication with users. This need is increasingly filled by recommenders that support natural language interaction, often conversationally. Given the inherent semantic subjectivity present in natural language, we argue that modeling subjective attributes in recommenders is a critical, yet understudied, avenue of AI research. We propose a novel framework for understanding different forms of subjectivity, examine various recommender tasks that will benefit from a systematic treatment of subjective attributes, and outline a number of research challenges.

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Zhang, Yin, Yueting Zhuang, Jiangqin Wu, and Liang Zhang. "Applying probabilistic latent semantic analysis to multi-criteria recommender system." AI Communications 22, no. 2 (2009): 97–107. http://dx.doi.org/10.3233/aic-2009-0446.

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Hsu, I.-Ching. "SXRS: An XLink-based Recommender System using Semantic Web technologies." Expert Systems with Applications 36, no. 2 (March 2009): 3795–804. http://dx.doi.org/10.1016/j.eswa.2008.02.062.

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Zhang, Hanzhong, Yinglong Wang, Chao Chen, Ruixia Liu, Shuwang Zhou, and Tianlei Gao. "Enhancing Knowledge of Propagation-Perception-Based Attention Recommender Systems." Electronics 11, no. 4 (February 11, 2022): 547. http://dx.doi.org/10.3390/electronics11040547.

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Researchers have introduced side information such as social networks or knowledge graphs to alleviate the problems of data sparsity and cold starts in recommendation systems. However, most of the methods ignore the exploration of feature differentiation aspects in the knowledge propagation process. To solve the above problem, we propose a new attention recommendation method based on an enhanced knowledge propagation perception. Specifically, to capture user preferences in a fine-grained manner in a knowledge graph, an asymmetric semantic attention mechanism is adopted. It identifies the influence of propagation neighbors on user preferences through a more precise representation of the preference semantics for head and tail entities. Furthermore, in consideration of the memory and generalization of different propagation depth features and adaptively adjusting the propagation weights, a new propagation feature exploration framework is designed. The performance of the proposed model is validated by two real-world datasets. The baseline model averagely increases by 9.65% and 9.15% for the Area Under Curve (AUC) and Accuracy (ACC) indicators, which proves the effectiveness of the model.

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Jian, Meng, Chenlin Zhang, Xin Fu, Lifang Wu, and Zhangquan Wang. "Knowledge-Aware Multispace Embedding Learning for Personalized Recommendation." Sensors 22, no. 6 (March 12, 2022): 2212. http://dx.doi.org/10.3390/s22062212.

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Recommender systems help users filter items they may be interested in from massive multimedia content to alleviate information overload. Collaborative filtering-based models perform recommendation relying on users’ historical interactions, which meets great difficulty in modeling users’ interests with extremely sparse interactions. Fortunately, the rich semantics hidden in items may be promising in helping to describing users’ interests. In this work, we explore the semantic correlations between items on modeling users’ interests and propose knowledge-aware multispace embedding learning (KMEL) for personalized recommendation. KMEL attempts to model users’ interests across semantic structures to leverage valuable knowledge. High-order semantic collaborative signals are extracted in multiple independent semantic spaces and aggregated to describe users’ interests in each specific semantic. The semantic embeddings are adaptively integrated with a target-aware attention mechanism to learn cross-space multisemantic embeddings for users and items, which are fed to the subsequent pairwise interaction layer for personalized recommendation. Experiments on real-world datasets demonstrate the effectiveness of the proposed KMEL model.

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Shambour, Qusai Y., Nidal M. Turab, and Omar Y. Adwan. "An Effective e-Commerce Recommender System Based on Trust and Semantic Information." Cybernetics and Information Technologies 21, no. 1 (March 1, 2021): 103–18. http://dx.doi.org/10.2478/cait-2021-0008.

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Abstract Electronic commerce has been growing gradually over the last decade as a new driver of the retail industry. In fact, the growth of e-Commerce has caused a significant rise in the number of choices of products and services offered on the Internet. This is where recommender systems come into play by providing meaningful recommendations to consumers based on their needs and interests effectively. However, recommender systems are still vulnerable to the scenarios of sparse rating data and cold start users and items. To develop an effective e-Commerce recommender system that addresses these limitations, we propose a Trust-Semantic enhanced Multi-Criteria CF (TSeMCCF) approach that exploits the trust relations and multi-criteria ratings of users, and the semantic relations of items within the CF framework to achieve effective results when sufficient rating data are not available. The experimental results have shown that the proposed approach outperforms other benchmark recommendation approaches with regard to recommendation accuracy and coverage.

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Kaur, Lovedeep, and Naveen Kumari. "A Review on User Recommendation System Based Upon Semantic Analysis." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 11 (November 30, 2017): 35. http://dx.doi.org/10.23956/ijarcsse.v7i11.465.

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Recommender system applied various techniques and prediction algorithm to predict user interest on information, items and services from the tremendous amount of available data on the internet. Recommender systems are now becoming increasingly important to individual users, businesses and specially e-commerce for providing personalized recommendations. Recommender systems have been evaluated and improved in many, often incomparable, ways. In this paper, we review the evaluation and improvement techniques for improving overall performance of recommendation systems and proposing a semantic analysis based approach for clustering based collaborative filtering to improve the coverage of recommendation. The basic algorithm or predictive model we use are – simple linear regression, k-nearest neighbours(kNN), naives bayes, support vector machine. We also review the pearson correlation coefficient algorithm and an associative analysis-based heuristic. The algorithms themselves were implemented from abstract class recommender, which was extended from weka distribution classifier class. The abstract class adds prediction method to the classifier.

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Kaur, Lovedeep, and Naveen Kumari. "A Research on user Recommendation System Based upon Semantic Analysis." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 11 (November 30, 2017): 72. http://dx.doi.org/10.23956/ijarcsse.v7i11.471.

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Recommender system applied various techniques and prediction algorithm to predict user interest on information, items and services from the tremendous amount of available data on the internet. Recommender systems are now becoming increasingly important to individual users, businesses and specially e-commerce for providing personalized recommendations. Recommender systems have been evaluated and improved in many, often incomparable, ways. In this paper, we review the evaluation and improvement techniques for improving overall performance of recommendation systems and proposing a semantic analysis based approach for clustering based collaborative filtering to improve the coverage of recommendation. The basic algorithm or predictive model we use are – simple linear regression, k-nearest neighbours(kNN), naives bayes, support vector machine. We also review the pearson correlation coefficient algorithm and an associative analysis-based heuristic. The algorithms themselves were implemented from abstract class recommender, which was extended from weka distribution classifier class. The abstract class adds prediction method to the classifier.

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Rslan, Esraa, Mohamed H. Khafagy, Mostafa Ali, Kamran Munir, and Rasha M. Badry. "AgroSupportAnalytics: big data recommender system for agricultural farmer complaints in Egypt." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 1 (February 1, 2023): 746. http://dx.doi.org/10.11591/ijece.v13i1.pp746-755.

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The world’s agricultural needs are growing with the pace of increase in its population. Agricultural farmers play a vital role in our society by helping us in fulfilling our basic food needs. So, we need to support farmers to keep up their great work, even in difficult times such as the coronavirus disease (COVID-19) outbreak, which causes hard regulations like lockdowns, curfews, and social distancing procedures. In this article, we propose the development of a recommender system that assists in giving advice, support, and solutions for the farmers’ agricultural related complaints (or queries). The proposed system is based on the latent semantic analysis (LSA) approach to find the key semantic features of words used in agricultural complaints and their solutions. Further, it proposes to use the support vector machine (SVM) algorithm with Hadoop to classify the large agriculture dataset over Map/Reduce framework. The results show that a semantic-based classification system and ﬁltering methods can improve the recommender system. Our proposed system outperformed the existing interest recommendation models with an accuracy of 87%.

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Konaté, Jacqueline, Amadou G. Diarra, Seydina O. Diarra, and Aminata Diallo. "SyrAgri: A Recommender System for Agriculture in Mali." Information 11, no. 12 (November 30, 2020): 561. http://dx.doi.org/10.3390/info11120561.

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This paper focuses on recommender system for agriculture in Mali called SyrAgri. The goal is to guide and improve the quality-of-experience of farmers by offering them good farming practices according to their needs. Two types of recommendations are essentially taken into account: the recommendation of crops and the recommendation of farming practices based on some predefined criteria which are: yield, life cycle of the crop, type of soil, growing season, etc. SyrAgri also informs farmers about crop rotation and the similarity between different types of crops based on the following parameters: crop families, growing seasons and appropriate soil types. For the development of this system a hybrid recommendation approach was used: demographic, semantic and collaborative methods. Each method is adapted to a specific stage of a user’s visit to the system. The demographic approach is first activated in order to offer recommendations to new users of the system, which resolves the concept of cold start (immediate inclusion of a new item or a new user in the system). The semantic approach is then activated to recommend to the user items (crops, agricultural practices) semantically close to those (s)he has appreciated. Finally, the collaborative approach is used to recommend items that similar users have liked.

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Liu, Zhifeng, Xianzhan Zhong, and Conghua Zhou. "Personalized Relationships-Based Knowledge Graph for Recommender Systems with Dual-View Items." Symmetry 14, no. 11 (November 11, 2022): 2386. http://dx.doi.org/10.3390/sym14112386.

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The knowledge graph has received a lot of interest in the field of recommender systems as side information because it can address the sparsity and cold start issues associated with collaborative filtering-based recommender systems. However, when incorporating entities from a knowledge graph to represent semantic information, most current KG-based recommendation methods are unaware of the relationships between these users and items. As such, the learned semantic information representation of users and items cannot fully reflect the connectivity between users and items. In this paper, we present the PRKG-DI symmetry model, a Personalized Relationships-based Knowledge Graph for recommender systems with Dual-view Items that explores user-item relatedness by mining associated entities in the KG from user-oriented entity view and item-oriented entity view to augment item semantic information. Specifically, PRKG-DI utilizes a heterogeneous propagation strategy to gather information on higher-order user-item interactions and an attention mechanism to generate the weighted representation of entities. Moreover, PRKG-DI provides a score feature as a filter for individualized relationships to evaluate users’ potential interests. The empirical results demonstrate that our approach significantly outperforms several state-of-the-art baselines by 1.6%, 2.1%, and 0.8% on AUC, and 1.8%, 2.3%, and 0.8% on F1 when applied to three real-world scenarios for music, movie, and book recommendations, respectively.

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Guzmán, Giovanni, Miguel Torres-Ruiz, Vianney Tambonero, Miltiadis D. Lytras, Blanca López-Ramírez, Rolando Quintero, Marco Moreno-Ibarra, and Wadee Alhalabi. "A Collaborative Framework for Sensing Abnormal Heart Rate Based on a Recommender System: Semantic Recommender System for Healthcare." Journal of Medical and Biological Engineering 38, no. 6 (May 16, 2018): 1026–45. http://dx.doi.org/10.1007/s40846-018-0421-y.

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Tao, Xiaohui, Nischal Sharma, Patrick Delaney, and Aimin Hu. "Semantic Knowledge Discovery for User Profiling for Location-Based Recommender Systems." Human-Centric Intelligent Systems 1, no. 1-2 (2021): 32. http://dx.doi.org/10.2991/hcis.k.210704.001.

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CHEN, Jian, Qing-yong OU, Yu-xin ZHENG, and Dong LI. "Semantic clustering-based attack detection model on CF-based recommender systems." Journal of Computer Applications 29, no. 5 (July 23, 2009): 1312–15. http://dx.doi.org/10.3724/sp.j.1087.2009.01312.

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Santos, Olga C., and Jesus G. Boticario. "Requirements for Semantic Educational Recommender Systems in Formal E-Learning Scenarios." Algorithms 4, no. 2 (July 20, 2011): 131–54. http://dx.doi.org/10.3390/a4030131.

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Indra, R., and Muthuraman Thangaraj. "An Integrated Recommender System Using Semantic Web With Social Tagging System." International Journal on Semantic Web and Information Systems 15, no. 2 (April 2019): 47–67. http://dx.doi.org/10.4018/ijswis.2019040103.

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Social tagging systems (STSs) allow collaborative users to share and annotate many types of resources with descriptive and semantically meaningful information in freely chosen text labels. STS provides three recommendations such as tag, item and user recommendations. Existing recommendation algorithms transform the three dimensional space of user, resource, and tag into two dimensions using pair relations in order to apply existing techniques. However, users may have different interests for an item, and items may have multiple facets. To circumvent this, a new system that models three types of entities user, tag and item in a STS as a 3-order tensor is proposed. The sparsity is reduced using stemming and predictions are made by applying latent semantic indexing using randomized singular value decomposition (RSVD). The proposal provides all the three recommendations using semantic web and shows notable improvements in terms of effectiveness through indices such as recall, precision, time and space.

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Ye, Mao, Zhi Tang, Jianbo Xu, and Lifeng Jin. "Recommender System for E-Learning Based on Semantic Relatedness of Concepts." Information 6, no. 3 (August 4, 2015): 443–53. http://dx.doi.org/10.3390/info6030443.

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Journal articles on the topic 'Semantic Recommender'

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