Interest-Based personalized Recommender System

Vashisth, Pooja; Bedi, Punam

doi:10.1109/wict.2011.6141252

Cited by 10 publications

(4 citation statements)

References 13 publications

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“…Another majority group of argument-based recommendation approaches has centered on the provision of argumentative explanations of recommendations, independent from the underlying filtering algorithm [28,44,48]. In this case, arguments are mainly described as relationships between user preferences and item attributes, and sometimes are based on beliefs, desires and intentions [57].…”

Section: Argument-based Recommender Systemsmentioning

confidence: 99%

Argument-based generation and explanation of recommendations

Segura-Tinoco

2021

Fifteenth ACM Conference on Recommender Systems

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In the recommender systems literature, it has been shown that, in addition to improving system effectiveness, explaining recommendations may increase user satisfaction, trust, persuasion and loyalty. In general, explanations focus on the filtering algorithms or the users and items involved in the generation of recommendations. However, on certain domains that are rich on user-generated textual content, it would be valuable to provide justifications of recommendations according to arguments that are explicit, underlying or related with the data used by the systems, e.g., the reasons for customers' opinions in reviews of e-commerce sites, and the requests and claims in citizens' proposals and debates of e-participation platforms. In this context, there is a need and challenging task to automatically extract and exploit the arguments given for and against evaluated items. We thus advocate to focus not only on user preferences and item features, but also on associated arguments. In other words, we propose to not only consider what is said about items, but also why it is said. Hence, arguments would not only be part of the recommendation explanations, but could also be used by the recommendation algorithms themselves. To this end, in this thesis, we propose to use argument mining techniques and tools that allow retrieving and relating argumentative information from textual content, and investigate recommendation methods that exploit that information before, during and after their filtering processes. CCS CONCEPTS• Information systems → Recommender systems; • Computing methodologies → Discourse, dialogue and pragmatics.

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Section: Argument-based Recommender Systemsmentioning

confidence: 99%

Argument-based generation and explanation of recommendations

Segura-Tinoco

2021

Fifteenth ACM Conference on Recommender Systems

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“…Aghili, Shajari, Khadivi, and Morid () implemented user movie genre interest to detect account hacks as attackers would show random and different genre interests compared with the genres of the movies. Vashisth and Bedi () proposed a design framework for a multiagent interest‐based system. The agents in the system have a belief–desire–intention architecture.…”

Section: Related Workmentioning

confidence: 99%

An efficient hybrid similarity measure based on user interests for recommender systems

et al. 2019

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Recommender systems are used to suggest items to users based on their interests. They have been used widely in various domains, including online stores, web advertisements, and social networks. As part of their process, recommender systems use a set of similarity measurements that would assist in finding interesting items. Although many similarity measurements have been proposed in the literature, they have not concentrated on actual user interests. This paper proposes a new efficient hybrid similarity measure for recommender systems based on user interests. This similarity measure is a combination of two novel base similarity measurements: the user interest-user interest similarity measure and the user interest-item similarity measure. This hybrid similarity measure improves the existing work in three aspects. First, it improves the current recommender systems by using actual user interests. Second, it provides a comprehensive evaluation of an efficient solution to the cold start problem. Third, this similarity measure works well even when no corated items exist between two users. Our experiments show that our proposed similarity measure is efficient in terms of accuracy, execution time, and applicability. Specifically, our proposed similarity measure achieves a mean absolute error (MAE) as low as 0.42, with 64% applicability and an execution time as low as 0.03 s, whereas the existing similarity measures from the literature achieve an MAE of 0.88 at their best; these results demonstrate the superiority of our proposed similarity measure in terms of accuracy, as well as having a high applicability percentage and a very short execution time.

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“…Wei et al (2005) developed a reinforcement learning strategy for market based multi agent recommendation system when many recommender systems are used. Vashisth and Bedi (2011) proposed a design framework for a multi agent interest-based system. Aghili et al (2011) used user movie genre interest to detect account hacks, as attacker would give random and different genre interests.…”

Section: Literature Reviewmentioning

confidence: 99%

An efficient semantic recommender method forArabic text

Hawashin

AlZu’bi

Kanan

et al. 2019

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Purpose This paper aims to propose a new efficient semantic recommender method for Arabic content. Design/methodology/approach Three 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. Findings Experiments 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/value Although 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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Interest-Based personalized Recommender System

Cited by 10 publications

References 13 publications

Argument-based generation and explanation of recommendations

Argument-based generation and explanation of recommendations

An efficient hybrid similarity measure based on user interests for recommender systems

An efficient semantic recommender method forArabic text

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