Deep neural network approach for a serendipity-oriented recommendation system

Ziarani, Reza Jafari; Ravanmehr, Reza

doi:10.1016/j.eswa.2021.115660

Cited by 20 publications

(6 citation statements)

References 32 publications

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“…Moreover, deploying a collaborative approach requires less effort from respondents to answer questions, because the users of the collaborative filtering system do not need to answer a list of questions, which is often required for using content-based recommender systems (Wang et al , 2014). Nevertheless, this technique suffers from the cold start problem (Tahmasebi et al , 2021), meaning that the effectiveness of the collaborative filtering technique decreases in the recommendation of unknown items (Ziarani and Ravanmehr, 2021).…”

Section: Framework Developmentmentioning

confidence: 99%

Augmenting machine learning with human insights: the model development for B2B personalization

Yaghtin,

Mero

2024

JBIM

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Purpose Machine learning (ML) techniques are increasingly important in enabling business-to-business (B2B) companies to offer personalized services to business customers. On the other hand, humans play a critical role in dealing with uncertain situations and the relationship-building aspects of a B2B business. Most existing studies advocating human-ML augmentation simply posit the concept without providing a detailed view of augmentation. Therefore, the purpose of this paper is to investigate how human involvement can practically augment ML capabilities to develop a personalized information system (PIS) for business customers. Design/methodology/approach The authors developed a research framework to create an integrated human-ML PIS for business customers. The PIS was then implemented in the energy sector. Next, the accuracy of the PIS was evaluated using customer feedback. To this end, precision, recall and F1 evaluation metrics were used. Findings The computed figures of precision, recall and F1 (respectively, 0.73, 0.72 and 0.72) were all above 0.5; thus, the accuracy of the model was confirmed. Finally, the study presents the research model that illustrates how human involvement can augment ML capabilities in different stages of creating the PIS including the business/market understanding, data understanding, data collection and preparation, model creation and deployment and model evaluation phases. Originality/value This paper offers novel insight into the less-known phenomenon of human-ML augmentation for marketing purposes. Furthermore, the study contributes to the B2B personalization literature by elaborating on how human experts can augment ML computing power to create a PIS for business customers.

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Section: Framework Developmentmentioning

confidence: 99%

Augmenting machine learning with human insights: the model development for B2B personalization

Yaghtin,

Mero

2024

JBIM

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“…Zhang et al [13] proposed a serendipity-oriented next point-of-interest recommendation model, SNPR, and designed a transformer-based neural network to capture the complex interdependencies of POIs in a user's clicking sequence by weighing relevance and unexpectedness. Ziarani et al [14] proposed a deep neural network approach for a serendipity-oriented recommendation system, using unexpectedness and relevance parameters to compose focus shift points to generate novelty recommendations by integrating Convolutional Neural Networks and Particle Swarm Optimization algorithm. However, most of these studies are based on product recommendation systems, and only a few studies have introduced them into the research collaborators recommender systems.…”

Section: Related Workmentioning

confidence: 99%

“…√‖𝐹(𝐴 𝑖 )‖‖𝐹(𝐴 𝑗 )‖ (14) where 𝐹(𝐴 𝑖 ) and 𝐹(𝐴 𝑗 ) are the representation vectors of the scholars' nodes 𝐴 𝑖 and 𝐴 𝑗 , respectively.…”

Section: B Novelty Indicator Calculation Modulementioning

confidence: 99%

NovSRC: A Novelty-Oriented Scientific Collaborators Recommendation Model

Li,

Wang,

Wang

et al. 2024

IJACSA

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Collaborator recommendation is a crucial topic in research management. This paper proposes a Novelty-Oriented Scientific Research Collaborator recommendation model (NovSRC). By recommending collaborators under the guidance of novel indicators, NovSRC aims to broaden scholars' research perspectives and facilitate the progress of research innovation. NovSRC utilizes heterogeneous academic networks composed of different academic entities and their relationships to learn vector representations of scholars and quantify their novelty metrics. A weighted academic collaboration network was constructed by measuring the novelty collaboration strength (NCS) among scholars under the novelty index, and based on this network, the final vector representation of scholars under the guidance of novelty characteristics was learned. By calculating the similarity between scholar vectors, NovSRC generates a Top-N recommendation list with a focus on novelty. The experimental results indicate that NovSRC achieved the best recommendation performance. Compared with the baseline models, the recommendation precision of NovSRC has improved by 6.9%, the F1 value has increased by 17.3%, and the novelty collaboration strength among scholars has increased by 3.3%. The analysis of the recommended list shows that compared to the target scholars, scholars recommended by the NovSRC model exhibit a wider distribution of research interests, which confirms that novelty has become a key benchmark factor for scholars seeking collaborators.

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“…However, the spatial semantic information of the HAN model needs to be mined, and the user data in the MOOC recommendation model is complex and not applicable [15,16,17]. Moreover, HAN model can only learn data expressed by fixed heterogeneous information network at present.…”

Section: User Embedding Expression Mode and Node Layer Designmentioning

confidence: 99%

Research on MOOC Curriculum Recommendation Model of Higher Vocational English based on Improved Intensive Learning Network

Zheng,

2024

SCPE

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With the development and maturity of the Internet education industry, more and more vocational colleges have opened English teaching courses based on massive open online courses courses. However, there are many English-related courses on the massive open online courses course platform, and the use of scientific recommendation models can improve the teaching quality of such courses. Therefore, this research attempts to design two improved attention mechanisms and user-based embedded expression using meta-path technology. At the same time, these two are combined with reinforcement learning technology to design an improved massive open online courses English course recommendation model. The test results show that the hit rate of the model designed in this study is 89.84%, 74.28%, 70.81% and 71.35% respectively when the rank number is 20 and the parameter is 10. At this time, the cumulative income of normalized discount is 48.24%, 34.58%, 25.96% and 28.69% respectively. However, when the number of calculated samples reaches the maximum value of 1158609, the calculation time of the improved reinforcement learning recommendation model is 1867 seconds, which is also higher than the comparison model. The experimental results show that the curriculum recommendation accuracy of the massive open online courses recommendation model designed in this study is higher and the recommendation results are more reasonable. The results of this research have a certain application potential in the field of the construction of online education in colleges and universities.

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Deep neural network approach for a serendipity-oriented recommendation system

Cited by 20 publications

References 32 publications

Augmenting machine learning with human insights: the model development for B2B personalization

Augmenting machine learning with human insights: the model development for B2B personalization

NovSRC: A Novelty-Oriented Scientific Collaborators Recommendation Model

Research on MOOC Curriculum Recommendation Model of Higher Vocational English based on Improved Intensive Learning Network

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