Bootstrapped Personalized Popularity for Cold Start Recommender Systems

Chaimalas, Iason; Walker, Duncan Martin; Gruppi, Edoardo; Clark, Benjamin Richard; Toni, Laura

doi:10.1145/3604915.3608820

Cited by 3 publications

(1 citation statement)

References 46 publications

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“…To address these challenges, researchers have proposed various solutions. For example, some studies have focused on improving the accuracy of recommendations by incorporating additional information, such as user demographics or item features [41,42]. Others have explored the use of parallelization techniques, such as Apache Spark, to enhance the scalability of recommender systems [34,43].…”

Section: Traditional Recommender Systemsmentioning

confidence: 99%

From Traditional Recommender Systems to GPT-Based Chatbots: A Survey of Recent Developments and Future Directions

Al-Hasan,

Sayed,

Bensaali

et al. 2024

BDCC

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Recommender systems are a key technology for many applications, such as e-commerce, streaming media, and social media. Traditional recommender systems rely on collaborative filtering or content-based filtering to make recommendations. However, these approaches have limitations, such as the cold start and the data sparsity problem. This survey paper presents an in-depth analysis of the paradigm shift from conventional recommender systems to generative pre-trained-transformers-(GPT)-based chatbots. We highlight recent developments that leverage the power of GPT to create interactive and personalized conversational agents. By exploring natural language processing (NLP) and deep learning techniques, we investigate how GPT models can better understand user preferences and provide context-aware recommendations. The paper further evaluates the advantages and limitations of GPT-based recommender systems, comparing their performance with traditional methods. Additionally, we discuss potential future directions, including the role of reinforcement learning in refining the personalization aspect of these systems.

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Section: Traditional Recommender Systemsmentioning

confidence: 99%

From Traditional Recommender Systems to GPT-Based Chatbots: A Survey of Recent Developments and Future Directions

Al-Hasan,

Sayed,

Bensaali

et al. 2024

BDCC

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API Recommendation For Mashup Creation: A Comprehensive Survey

Alhosaini,

Alharbi,

Wang

et al. 2023

The Computer Journal

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Mashups are web applications that expedite software development by reusing existing resources through integrating multiple application programming interfaces (APIs). Recommending the appropriate APIs plays a critical role in assisting developers in building such web applications easily and efficiently. The proliferation of publicly available APIs on the Internet has inspired the community to adopt various models to accomplish the recommendation task. Until present, considerable efforts have been made to recommend the optimal set of APIs, delivering fruitful results and achieving varying recommendation performance. This paper presents a timely review on the topic of API recommendations for mashup creation. Specifically, we investigate and compare not only traditional data mining approaches and recommendation techniques but also more recent approaches based on network representation learning and deep learning techniques. By analyzing the merits and pitfalls of existing approaches, we pinpoint a few promising directions to address the remaining challenges in the current research. This survey provides a timely comprehensive review of the API recommendation research and could be a useful reference for relevant researchers and practitioners.

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SARDINE: Simulator for Automated Recommendation in Dynamic and Interactive Environments

Deffayet,

Thonet,

Hwang

et al. 2024

ACM Trans. Recomm. Syst.

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Simulators can provide valuable insights for researchers and practitioners who wish to improve recommender systems, because they allow one to easily tweak the experimental setup in which recommender systems operate, and as a result lower the cost of identifying general trends and uncovering novel findings about the candidate methods. A key requirement to enable this accelerated improvement cycle is that the simulator is able to span the various sources of complexity that can be found in the real recommendation environment that it simulates. With the emergence of interactive and data-driven methods – e.g., reinforcement learning or online and counterfactual learning-to-rank – that aim to achieve user-related goals beyond the traditional accuracy-centric objectives, adequate simulators are needed. In particular, such simulators must model the various mechanisms that render the recommendation environment dynamic and interactive, e.g., the effect of recommendations on the user or the effect of biased data on subsequent iterations of the recommender system. We therefore propose s imulator for a utomated r ecommendation in d ynamic and in teractive e nvironments, a flexible and interpretable recommendation simulator that can help accelerate research in interactive and data-driven recommender systems. We demonstrate its usefulness by studying existing methods within nine diverse environments derived from SARDINE, and even uncover novel insights about them.

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Bootstrapped Personalized Popularity for Cold Start Recommender Systems

Cited by 3 publications

References 46 publications

From Traditional Recommender Systems to GPT-Based Chatbots: A Survey of Recent Developments and Future Directions

From Traditional Recommender Systems to GPT-Based Chatbots: A Survey of Recent Developments and Future Directions

API Recommendation For Mashup Creation: A Comprehensive Survey

SARDINE: Simulator for Automated Recommendation in Dynamic and Interactive Environments

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