The Pure Cold-Start Problem: A deep study about how to conquer first-time users in recommendations domains

Silva, Nícollas; Carvalho, Diego; Pereira, Adriano C. M.; Mourão, Fernando; Rocha, Leonardo

doi:10.1016/j.is.2018.09.001

Cited by 44 publications

(24 citation statements)

References 19 publications

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“…The smart TV is a connected TV that brings further challenges in the form of security, privacy, irrelevant recommendations, and interactive user interfaces [3], [16]. The relevant recommendations can enhance the conversion rate up to some extent, which in turn contribute to e-commerce and e-business [22]. Therefore, the appropriate and precise recommendations on smart TV may further contribute to not only user satisfaction [3], but also e-commerce.…”

Section: Related Workmentioning

confidence: 99%

Tailoring Recommendations to Groups of Viewers on Smart TV: A Real-Time Profile Generation Approach

Alam

Khusro

2020

IEEE Access

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The recommender systems predict and calculate user preferences for recommendations. However, such predictions and calculations are neither accurate nor viable in the context of smart TV due to the reasons that it is a lean-back, non-personalized device, and normally enjoyed in groups. Hence, group recommendations have utmost importance, specifically from the perspectives of watching smart TV. The existing group recommendation techniques predict the individual's preferences and then create a virtual group profile for recommendations. However, identifying and satisfying every group member is challenging. Numerous techniques have been proposed, such as face detection and recognition systems, but these systems lead to security and privacy issues. This paper proposes a smart TV-based recommender system that aims to identify the individuals and group members from their ''age,'' ''gender,'' and ''number'' information to overcome the biases that occur due to predictions and estimations of user's preferences. The study proposes a novel formula and age-gender matrix for generating anonymous, consolidated, and secure profiles, including group profiles on a smart TV. This study further proposes a novel method for finding a dominant character in a group by utilizing the user's ratings. Results show that the group decision has a significant impact on supplying social metadata, such as ratings, comments, etc., which in turn improve recommendation results. For materializing the proposed work, smart TV's processing, storage, and camera are utilized. The prototypical implementation has been tested and analyzed with improved recommendation results and viewer(s) satisfaction. INDEX TERMS Recommender systems, smart TV, group modeling, personalized content.

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Section: Related Workmentioning

confidence: 99%

Tailoring Recommendations to Groups of Viewers on Smart TV: A Real-Time Profile Generation Approach

Alam

Khusro

2020

IEEE Access

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“…Then the final created recommendation list is evaluated with ground truth items in the test session. Evaluation metrics used in this paper are recall and precision, which are very common in the RS domain (Silva et al, 2019).…”

Section: Frameworkmentioning

confidence: 99%

“…There are several approaches that have been proposed to alleviate new user and item problems (Son, 2016;Herce-Zelaya et al, 2020;Son, 2016;Silva et al, 2019). Users' demographic data (Son, 2016;Silva et al, 2019) is the most commonly used data where similar users are found using their demographic information and items these users interacted are recommended for the new user. Also, in some works (Safoury and Salah, 2013;Bouadjenek et al, 2016), user demographic labels are matched with product features.…”

Section: Introductionmentioning

confidence: 99%

Session Similarity based Approach for Alleviating Cold-start Session Problem in e-Commerce for Top-N Recommendations

Esmeli

Bader-El-Den

Abdullahi³

2020

Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management

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Cold-start problem is one of the main challenges for the recommender systems. There are many methods developed for traditional recommender systems to alleviate the drawback of cold-start user and item problems. However, to the best of our knowledge, in session based recommender systems cold-start session problem still needs to be investigated. In this paper, we propose a session similarity-based method to alleviate drawback of cold-start sessions in e-commerce domain, in which there are no interacted items in the sessions that can help to identify users' preferences. In the proposed method, product recommendations are given based on the most similar sessions that are found using session features such as session start time, location, etc. Computational experiments on two real-world datasets show that when the proposed method applied, there is a significant improvement on the performance of recommender systems in terms of recall and precision metrics comparing to random recommendations for cold-start sessions.

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“…Other alternatives without user intervention are preferable. For example, in [19] the authors show how recommendations biased by popularity, recency and positive ratings do not suit all new users and therefore explore user coverage to diversify recommendations. Some authors studied how to modify kNN algorithms to improve the recommendations.…”

Section: State Of the Artmentioning

confidence: 99%

High Order Profile Expansion to tackle the new user problem on recommender systems

et al. 2019

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Collaborative Filtering algorithms provide users with recommendations based on their opinions, that is, on the ratings given by the user for some items. They are the most popular and widely implemented algorithms in Recommender Systems, especially in e-commerce, considering their good results. However, when the information is extremely sparse, independently of the domain nature, they do not present such good results. In particular, it is difficult to offer recommendations which are accurate enough to a user who has just arrived to a system or who has rated few items. This is the well-known new user problem, a type of cold-start. Profile Expansion techniques had been already presented as a method to alleviate this situation. These techniques increase the size of the user profile, by obtaining information about user tastes in distinct ways. Therefore, recommender algorithms have more information at their disposal, and results improve. In this paper, we present the High Order Profile Expansion techniques, which combine in different ways the Profile Expansion methods. The results show 110% improvement in precision over the algorithm without Profile Expansion, and 10% improvement over Profile Expansion techniques.

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The Pure Cold-Start Problem: A deep study about how to conquer first-time users in recommendations domains

Cited by 44 publications

References 19 publications

Tailoring Recommendations to Groups of Viewers on Smart TV: A Real-Time Profile Generation Approach

Tailoring Recommendations to Groups of Viewers on Smart TV: A Real-Time Profile Generation Approach

Session Similarity based Approach for Alleviating Cold-start Session Problem in e-Commerce for Top-N Recommendations

High Order Profile Expansion to tackle the new user problem on recommender systems

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