An Effective e-Commerce Recommender System Based on Trust and Semantic Information

Shambour, Qusai; Turab, Nidal M.; Adwan, Omar

doi:10.2478/cait-2021-0008

Cited by 7 publications

(10 citation statements)

References 30 publications

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“…By leveraging this supplementary information, recommendation approaches can compensate for insufficient user ratings and produce more precise recommendations. Illustrations of such supplementary information include semantic relationships among items or users (Gohari & Tarokh, 2015;Shambour et al, 2021), as well as multi-criteria ratings that capture more complex user preferences (Shambour, 2016;Shambour, 2021). Semantic information can be represented using taxonomies.…”

Section: Introductionmentioning

confidence: 99%

“…By grouping items based on their attributes and properties, the system can identify relevant items and recommend them to the user. Additionally, taxonomy-based recommender systems can handle new item coldstart problems, by utilizing the existing taxonomy to categorize new items and recommend them to users with similar preferences (Gohari & Tarokh, 2015;Shambour et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Recommendation Model for Drug Selection

KHARMA,

SHAMBOUR,

HUSSEIN

2023

SIC

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Medical errors associated with medication pose significant threats to patients' safety, primarily due to the abundance of drug information available on various online healthcare platforms, leading to challenges in identifying relevant drugs. To address this issue, drug recommendation systems have been developed to assist in selecting appropriate medications for specific medical conditions. Collaborative filtering approaches have been widely used to generate personalized recommendations for various applications. They are easy to implement, debug, and provide justifiable reasoning for recommended items, which is not readily accessible in several other recommendation approaches. Regardless of their success, they still need further enhancements to address challenges related to insufficient rating data, such as data sparsity and new item problems. This paper proposes a drug recommendation model that effectively employs drug taxonomy and multi-criteria collaborative filtering to tackle these challenges. Drug taxonomy enhances recommendation quality by offering a more organized and granular representation of drugs, while multi-criteria rating captures the patients' preferences more accurately, enabling accurate recommendations that better match the patient's specific preferences. Experiments conducted on a real-world drug multi-criteria rating dataset demonstrate that the proposed model outperforms baseline recommendation approaches in addressing these challenges and improving prediction accuracy and coverage, making it a valuable tool to assist patients in selecting relevant drugs for their specific medical conditions.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Hybrid Recommendation Model for Drug Selection

KHARMA,

SHAMBOUR,

HUSSEIN

2023

SIC

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“…The term "new user" refers to a user who has only rated a very small number of items [14]. To address these issues and enhance the accuracy and coverage of user-based CF recommender systems, researchers have lately begun to utilize the multi-criteria ratings of users and combine CF with additional information, such as the trust relationships between users, to provide more trustworthy recommendations [15,16].…”

Section: Introductionmentioning

confidence: 99%

“…That is, understanding why users like things in addition to what they like is critical to making more successful recommendations. To put it another way, the exploitation of multicriteria ratings of items can ensure a better understanding of users' preferences, hence improving recommendation accuracy [15][16][17]. Furthermore, numerous websites nowadays allow users to rate items based on multiple criteria.…”

Section: Introductionmentioning

confidence: 99%

“…Users' explicit trust information can be collected, and each user can determine whether or not others are trustworthy. Implicit trust information, alternatively, can be inferred from user ratings [15,16]. The implicit trust information is used in this study as a supplementary source of information to alleviate the impact of sparsity and new user drawbacks and, as a result, increase the coverage of recommendations.…”

Section: Introductionmentioning

confidence: 99%

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A Trust-Based Recommender System for Personalized Restaurants Recommendation

Shambour

Abualhaj

Abu-Shareha

2022

Int. j. electr. comput. eng. syst. (Online)

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Several online restaurant applications, such as TripAdvisor and Yelp, provide potential consumers with reviews and ratings based on previous customers’ experiences. These reviews and ratings are considered the most important factors that determine the customer’s choice of restaurants. However, the selection of a restaurant among many unknown choices is still an arduous and time- consuming task, particularly for tourists and travellers. Recommender systems utilize the ratings provided by users to assist them in selecting the best option from many options based on their preferences. In this paper, we propose a trust-based recommendation model for helping consumers select suitable restaurants in accordance with their preferences. The proposed model utilizes multi- criteria ratings of restaurants and implicit trust relationships among consumers to produce personalized restaurant recommendations. The experimental results based on a real-world restaurant dataset demonstrated the superiority of the proposed model, in terms of prediction accuracy and coverage, in overcoming the sparsity and new user problems when compared to other baseline CF-based recommendation algorithms.

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A Trust-enhanced Recommender System for Patient-Doctor Matchmaking in Online Healthcare Communities

2023

IJIES

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In this era of vast online healthcare information, patients often find it challenging to choose the most appropriate doctors from a myriad of options available on different communities. The sheer volume of choices can be overwhelming, underscoring the importance of personalized doctor recommendations. Unfortunately, many existing communities rely on generic doctor recommendations through a global ranking system, potentially neglecting the unique needs and preferences of individual patients. To address these challenges, this study introduces a novel multicriteria user-item trust-enhanced collaborative filtering (MCUITeCF) approach, specifically designed to facilitate patients in locating doctors who match their unique preferences. This proposed approach capitalizes on multi-criteria ratings and integrates user-item trust relationships, aiming to enhance the quality of recommendations, while tackling the common issues of data sparsity and the cold-start problem. An examination of the proposed approach, using a realworld healthcare multi-criteria dataset, the RateMDs dataset, reveals its effectiveness in overcoming challenges associated with data sparsity and cold-start problems. The results demonstrate improved prediction accuracy and coverage compared to benchmark approaches, namely MC user-based CF, MC item-based CF, MC semantic-based CF, MC trust-based CF, and trust-semantic enhanced MC CF. Specifically, the results indicate that the MCUITeCF approach improves the average MAE by 66% compared to all of the benchmark approaches when tested on the RateMDs dataset. When dealing with data sparsity, the MCUITeCF approach improves the average MAE by 41% and prediction coverage by 23% compared to all of the benchmark approaches. In scenarios involving cold-start items, the MCUITeCF outperformed specific benchmark methods such as MC item-based CF, MC semantic-based CF, and Trust-Semantic enhanced MC CF, registering a 21% drop in average MAE and a 28% rise in prediction coverage. Similarly, for cold-start user situations, MCUITeCF excelled by decreasing the average MAE by 29% and a substantial increase in prediction coverage by 20% compared to MC user-based CF, MC trust-based CF, and trust-semantic enhanced MC CF benchmark approaches.

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An Effective e-Commerce Recommender System Based on Trust and Semantic Information

Cited by 7 publications

References 30 publications

A Hybrid Recommendation Model for Drug Selection

A Hybrid Recommendation Model for Drug Selection

A Trust-Based Recommender System for Personalized Restaurants Recommendation

A Trust-enhanced Recommender System for Patient-Doctor Matchmaking in Online Healthcare Communities

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