Cold Start Problem in Recommendation System: A Solution Model Based on Clustering and Association Rule Techniques

Khatwal, Ravi; Hasan, Sayed Nasir

doi:10.1109/impact55510.2022.10029293

Cited by 2 publications

(1 citation statement)

References 22 publications

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“…Classification methods can also predict the interests or preferences of new users based on demographic or registration information. Open programming interfaces can encourage user engagement and facilitate data collection, helping the system gather more reliable data and reduce the impact of the cold start problem [6][7][8][9].…”

Section: Principles Of Intelligent Algorithm-based Recommendationsmentioning

confidence: 99%

The Investigation of Music Recommendation Systems

2024

HSET

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A recommendation system represents a machine learning technique that examines and presents user preferences derived from their online activities. In today's digital landscape, recommendation systems find widespread use in short video platforms. These systems scrutinize user actions such as search history and viewing patterns to proactively suggest videos aligned with individual tastes. Leveraging an effective recommendation system to propose high-quality videos tailored to user preferences proves advantageous in sustaining user engagement and boosting platform traffic. Nonetheless, users with specialized interests or newcomers to the platform may receive less accurate recommendations due to limited data availability. The development of an artificial intelligence-based recommendation system can significantly expedite product searches, enhance search precision, foster user loyalty, augment average order values, and bolster overall conversion rates. In the realm of artificial intelligence applications, recommendation systems have become indispensable components. This paper delves into the algorithms for automatically analyzing and recommending music based on users' historical playback records. The study employs a random forest model to train the recommendation algorithm.

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Section: Principles Of Intelligent Algorithm-based Recommendationsmentioning

confidence: 99%

The Investigation of Music Recommendation Systems

2024

HSET

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Addressing the Cold-Start Problem in Recommender Systems Based on Frequent Patterns

Panteli

Boutsinas

2023

Algorithms

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Recommender systems aim to forecast users’ rank, interests, and preferences in specific products and recommend them to a user for purchase. Collaborative filtering is the most popular approach, where the user’s past purchase behavior consists of the user’s feedback. One of the most challenging problems in collaborative filtering is handling users whose previous item purchase behavior is unknown, (e.g., new users) or products for which user interactions are not available, (e.g., new products). In this work, we address the cold-start problem in recommender systems based on frequent patterns which are highly frequent in one set of users, but less frequent or infrequent in other sets of users. Such discriminant frequent patterns can distinguish one target set of users from all other sets. The proposed methodology, first forms different clusters of old users and then discovers discriminant frequent patterns for each different such cluster of users and finally exploits the latter to hallucinate the purchase behavior of new users. We also present empirical results to demonstrate the efficiency and accuracy of the proposed methodology.

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Cold Start Problem in Recommendation System: A Solution Model Based on Clustering and Association Rule Techniques

Cited by 2 publications

References 22 publications

The Investigation of Music Recommendation Systems

The Investigation of Music Recommendation Systems

Addressing the Cold-Start Problem in Recommender Systems Based on Frequent Patterns

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