A knowledge-driven digital nudging approach to recommender systems built on a modified Onicescu method

Sitar‐Tăut, Dan‐Andrei; Mican, Daniel; Buchmann, Robert Andrei

doi:10.1016/j.eswa.2021.115170

Cited by 9 publications

(6 citation statements)

References 57 publications

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“…The Onicescu method and Zipf's law were the foundation of the system for creating tailored suggestions for users in e-commerce scenarios. A method for a recommendation systems that is knowledge-driven and uses digital nudging was given by Sitar-Taut et al [31]. To include domain information in the recommendation process and effectively influence user preferences, their approach used a modified Onicescu technique.…”

Section: Related Workmentioning

confidence: 99%

Adaptive KNN-Based Extended Collaborative Filtering Recommendation Services

Nguyen

2023

BDCC

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In the current era of e-commerce, users are overwhelmed with countless products, making it difficult to find relevant items. Recommendation systems generate suggestions based on user preferences, to avoid information overload. Collaborative filtering is a widely used model in modern recommendation systems. Despite its popularity, collaborative filtering has limitations that researchers aim to overcome. In this paper, we enhance the K-nearest neighbor (KNN)-based collaborative filtering algorithm for a recommendation system, by considering the similarity of user cognition. This enhancement aimed to improve the accuracy in grouping users and generating more relevant recommendations for the active user. The experimental results showed that the proposed model outperformed benchmark models, in terms of MAE, RMSE, MAP, and NDCG metrics.

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Section: Related Workmentioning

confidence: 99%

Adaptive KNN-Based Extended Collaborative Filtering Recommendation Services

Nguyen

2023

BDCC

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“…An RS learns from customers and suggests the most valuable and useful items of all available products (Bobadilla et al, 2013). Therefore, an RS can reduce information overload by offering relevant alternatives to the customer in a personalized way (Lu et al, 2015;Sitar-T aut et al, 2021). The recommendations significantly and positively influence sales, because they reduce search costs and the uncertainty associated with the purchase of unknown or poorquality products (Pathak et al, 2010).…”

Section: Recommender Systemsmentioning

confidence: 99%

The effect of perceived usefulness of recommender systems and information sources on purchase intention

Mican

Sitar‐Tăut

2023

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PurposeThe current study aims to empirically analyze the influence of different information sources, together with the persuasiveness of recommender systems (RSs) on the consumer’s purchase intention (PI). It also expands the research on RSs from the point of view of consumer behavior and psychology, considering perceived usefulness and relevance. In addition, it analyzes how different types of personalized recommendations, along with non-personalized ones, influence PI.Design/methodology/approachThe proposed model has been validated using partial least squares structural equation modeling (PLS-SEM), based on the data collected from 597 online shoppers.FindingsThis study proves that both information search and RSs influence PI, being complementary rather than mutually exclusive. Recommender systems’ findings indicate that the PI is primarily influenced by the perceived relevance of RSs, the information provided by manufacturers and reviews. Moreover, only the influence of the perceived usefulness of personalized recommendations strongly affects PI. Conversely, non-personalized recommendations do not affect PI.Practical implicationsDevelopers should focus on increasing the perceived usefulness and relevance of RSs. Thus, they could adopt the hybridization of RSs with the aggregation of both personal shopping behavior and social network contacts. It should integrate information signals from multiple sources to include sentiment extracted from reviews or links to the manufacturer’s page. Furthermore, the recommendation of discounted products must be only for products preferred by customers, because only these influence the PI.Originality/valueThis research provides a structural model that examines together, for the first time, the influence on the PI of the main RSs and sources of information.

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“…There are a few research works that deals with recommendations based on query context. Sitar-Tăut et al [38] suggested a knowledge-driven product recommendation, using a digital nudging mechanism, to tackle the cold-start problem. They utilized a KG to integrate managerial preferences, along with product attributes that enable semantic reasoning using SPARQL queries.…”

Section: Recommendationmentioning

confidence: 99%

Context-Aware Explainable Recommendation Based on Domain Knowledge Graph

Syed

Huy

Chung

2022

BDCC

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With the rapid growth of internet data, knowledge graphs (KGs) are considered as efficient form of knowledge representation that captures the semantics of web objects. In recent years, reasoning over KG for various artificial intelligence tasks have received a great deal of research interest. Providing recommendations based on users’ natural language queries is an equally difficult undertaking. In this paper, we propose a novel, context-aware recommender system, based on domain KG, to respond to user-defined natural queries. The proposed recommender system consists of three stages. First, we generate incomplete triples from user queries, which are then segmented using logical conjunction (∧) and disjunction (∨) operations. Then, we generate candidates by utilizing a KGE-based framework (Query2Box) for reasoning over segmented logical triples, with ∧, ∨, and ∃ operators; finally, the generated candidates are re-ranked using neural collaborative filtering (NCF) model by exploiting contextual (auxiliary) information from GraphSAGE embedding. Our approach demonstrates to be simple, yet efficient, at providing explainable recommendations on user’s queries, while leveraging user-item contextual information. Furthermore, our framework has shown to be capable of handling logical complex queries by transforming them into a disjunctive normal form (DNF) of simple queries. In this work, we focus on the restaurant domain as an application domain and use the Yelp dataset to evaluate the system. Experiments demonstrate that the proposed recommender system generalizes well on candidate generation from logical queries and effectively re-ranks those candidates, compared to the matrix factorization model.

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A knowledge-driven digital nudging approach to recommender systems built on a modified Onicescu method

Cited by 9 publications

References 57 publications

Adaptive KNN-Based Extended Collaborative Filtering Recommendation Services

Adaptive KNN-Based Extended Collaborative Filtering Recommendation Services

The effect of perceived usefulness of recommender systems and information sources on purchase intention

Context-Aware Explainable Recommendation Based on Domain Knowledge Graph

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