A novel top-n recommendation method for multi-criteria collaborative filtering

Kaya, Tugba; Kaleli, Cihan

doi:10.1016/j.eswa.2022.116695

Cited by 14 publications

(6 citation statements)

References 33 publications

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“…It is mainly based on the assumption that similar things or people fit well together. Correspondingly, learners who like the same items are more likely to have the same interests and preferences in general [12,13]. At present, collaborative filtering algorithms are mainly of two types, namely, user-and item-based.…”

Section: Recommendation Algorithm Based On Collaborative Filteringmentioning

confidence: 99%

Personalized Hybrid Recommendation Algorithm for MOOCs Based on Learners’ Dynamic Preferences and Multidimensional Capabilities

Liu

2023

Applied Sciences

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In the MOOCs context, learners experience information overload. Thus, it is necessary to improve personalized recommendation algorithms for learners. The current recommendation algorithm focuses mainly on the learners’ course ratings. However, the choice of courses is not only based on the learners’ interests and preferences. It is also affected by learners’ knowledge domains and learning capabilities, all of which change dynamically over time. Therefore, this study proposes a personalized hybrid recommendation algorithm combining clustering with collaborative filtering. First, data on learners’ course rating preferences, course attribute preferences, and multidimensional capabilities that match course traits are used based on multidimensional item response theory. Second, considering that learners’ preferences and multidimensional capabilities change dynamically over time, the Ebbinghaus forgetting curve is introduced by integrating memory weights to improve the accuracy and interpretation of the proposed recommendation algorithm for MOOCs. Finally, the performance of the proposed recommendation algorithm is investigated using data from Coursera, an internationally renowned MOOCs platform. The experimental results show that the proposed recommendation algorithm is superior to the baseline algorithms. Accordingly, relevant suggestions are proposed for the development of MOOCs.

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Section: Recommendation Algorithm Based On Collaborative Filteringmentioning

confidence: 99%

Personalized Hybrid Recommendation Algorithm for MOOCs Based on Learners’ Dynamic Preferences and Multidimensional Capabilities

Liu

2023

Applied Sciences

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“…This approach was proposed by University of Minnesota researchers in 2001 [16]. As a system grows, the number of users increases and so does the complexity of finding similar users.…”

Section: Item-based Approachmentioning

confidence: 99%

A Manhattan distance based hybrid recommendation system

UYANIK>

Orman

2023

International Journal of Applied Mathematics Electronics and Computers

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Many online service providers use a recommendation system to assist their customers' decision-making by generating recommendations. Accordingly, this paper proposes a new recommendation system for tourism customers to make online reservations for hotels with the features they need, saving customers time and increasing the impact of personalized hotel recommendations. This new system combined collaborative and content-based filtering approaches and created a new hybrid recommendation system. Two datasets containing customer information and hotel features were analyzed by Recency, Frequency, Monetary (RFM) method in order to identify customers according to their purchasing nature. The main idea of the recommendation system is to establish correlations between users and products and make the decision to choose the most suitable product or information for a particular user. As a result of the exponential growth of online data, this vast amount of information for use in the tourism industry can be leveraged by decision-makers to make purchasing decisions[20]. Filtering, prioritizing, and beneficially presenting relevant information reduces this overload. There are following three main ways that recommendation systems can generate a recommendation list for a user; content-based, collaborative-based, and hybrid approaches[1]. This paper describes each category and its techniques in detail. RFM Analysis is used to identify customer segments by measuring customers' purchasing habits. It is the process of labeling customers by determining the Recency, Frequency, and Monetary values of their purchases and ranking them on a scoring model. Scoring is based on how recently they bought (Recency), how often they bought (Frequency), and purchase size (Monetary). Experimental results show that the accuracy of behavior analysis using Manhattan distance-based hybrid filtering is greatly improved compared to collaborative and content-based algorithms.

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“…Number of popular items (NrPopItem): It is the number of popular items in the profile of user u, which can be calculated using Equation (11).…”

Section: Our Proposed Featuresmentioning

confidence: 99%

“…Therefore, the MCRSs are more talented at accurately capturing the correlations between user profiles and thus producing better-personalized recommendations, since two users might highly differentiate when considering their opinions on subaspects of the items even if they seem pretty similar to each other based on their overall ratings. These systems commonly benefit from such multi-criteria rating information in the recommendation generation phase by following either function-based or similarity-based approaches; 10,11 the former utilizes an aggregation function that is defined based on relationships between subcriteria preferences and overall ratings, while the latter aims at aggregating the estimated similarity values for each criterion to achieve overall similarities between users/items.…”

Section: Introductionmentioning

confidence: 99%

A novel classification‐based shilling attack detection approach for multi‐criteria recommender systems

Kaya

Yalçin

Kaleli

2023

Computational Intelligence

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Recommender systems are emerging techniques guiding individuals with provided referrals by considering their past rating behaviors. By collecting multi-criteria preferences concentrating on distinguishing perspectives of the items, a new extension of traditional recommenders, multi-criteria recommender systems reveal how much a user likes an item and why user likes it; thus, they can improve predictive accuracy. However, these systems might be more vulnerable to malicious attacks than traditional ones, as they expose multiple dimensions of user opinions on items. Attackers might try to inject fake profiles into these systems to skew the recommendation results in favor of some particular items or to bring the system into discredit. Although several methods exist to defend systems against such attacks for traditional recommenders, achieving robust systems by capturing shill profiles remains elusive for multi-criteria rating-based ones. Therefore, in this study, we first consider a prominent and novel attack type, that is, the power-item attack model, and introduce its four distinct variants adapted for multi-criteria data collections. Then, we propose a classification method detecting shill profiles based on various generic and model-based user attributes, most of which are new features usually related to item popularity and distribution of rating values. The experiments conducted on three benchmark datasets conclude that

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A novel top-n recommendation method for multi-criteria collaborative filtering

Cited by 14 publications

References 33 publications

Personalized Hybrid Recommendation Algorithm for MOOCs Based on Learners’ Dynamic Preferences and Multidimensional Capabilities

Personalized Hybrid Recommendation Algorithm for MOOCs Based on Learners’ Dynamic Preferences and Multidimensional Capabilities

A Manhattan distance based hybrid recommendation system

A novel classification‐based shilling attack detection approach for multi‐criteria recommender systems

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