An empirical study of content-based recommendation systems in mobile app markets

Jozani, Mohsen; Liu, Charles Zhechao; Choo, Kim‐Kwang Raymond

doi:10.1016/j.dss.2023.113954

Cited by 10 publications

(7 citation statements)

References 34 publications

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“…It analyzes the similarities and patterns in user-item interactions to identify items that are likely to be of interest to a particular user [23,28,29]. Content-based filtering, on the other hand, focuses on the characteristics of the items themselves [30,31]. It recommends items to users based on the similarity between the content attributes of items and the users' preferences [25,32,33].…”

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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“…, 2020), making the entrepreneur introducing the app the signaler, the app store and its consumers the receivers. Consumer reviews and recommendation function as forms of feedback (Jozani et al. , 2023).…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…To illustrate, the introduction of a new app to the app market serves as a positive signal (Cucculelli and Ermini, 2012;Landoni et al, 2020), making the entrepreneur introducing the app the signaler, the app store and its consumers the receivers. Consumer reviews and recommendation function as forms of feedback (Jozani et al, 2023). Additionally, another form of signal arises when an app is algorithmically co-listed with others in a "consideration set", assisting in the "consider-then-purchase" decision.…”

Section: Signaling and Feedbackmentioning

confidence: 99%

Nudging digital entrepreneurs: the influence of the Google Play Store top developer award on technological innovation

Kuk

2023

IJEBR

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PurposeThis study explores how app store awards shape the behavior of leading digital entrepreneurs, focusing on their engagement in technological innovation through feedback and re-signaling mechanisms. It aims to reveal the nudging effect of award signals on entrepreneurial behavior.Design/methodology/approachIn this study, data from 349 leading mobile app entrepreneurs in the UK Google Play Store were collected over an extended period from various sources. Functionality novelty and explorative behavior were assessed by analyzing app release date and permission technologies in comparison to both their own prior apps and those of their closest competitors. Hypothesized relationships were tested using accelerated failure time parametric models.FindingsThis study extends the literature on signaling by showing that (1) the top developer award signal served to nudge entrepreneurs to improve the functionality novelty of their apps and those who succeeded were less likely to switch to another product category, (2) the award signal created a window of opportunity for non-award entrepreneurs to respond and those who released new apps around the midpoint of a normal app development cycle significantly improved the likelihood of winning the award in a subsequent round of award-giving, and (3) the effect of functionality novelty on winning the award was more pronounced when non-award entrepreneurs pursued more explorative than exploitative behavior in app development.Originality/valueThe results offer novel insights into an understudied area, specifically the influence of online award signals on nudging entrepreneurs to pursue technological innovation. The research also highlights the crucial role played by the app store as an intermediary signaler.

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“…Content-Based Filtering adalah metode yang digunakan pada penelitian ini dan berfokus pada analisis ulasan review konten, ulasan profil restoran dan ulasan harga untuk menghasilkan rekomendasi yang sesuai dengan profil pengguna. [3] Penelitian ini diharapkan dapat membantu konsumen dalam memilih tempat kuliner dengan kriteria tertentu, karena setiap orang memiliki kebutuhan serta kriteria yang diinginkan.…”

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“…Selain itu, sistem rekomendasi dapat mempengaruhi proses pengambilan keputusan konsumen, menyebabkan aliran permintaan. Temuan penelitian ini memberikan implikasi penting bagi pengembang dan operator pasar untuk lebih mempromosikan produk mereka di pasar aplikasi mobile yang sangat kompetitif [5].…”

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Pemodelan Sistem Rekomendasi Restoran berdasarkan Preferensi Pengguna dengan Pendekatan Content-Based Filtering

Ilhamsyah,

Rahmayuda,

Midyanti

et al. 2024

JEPIN

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Setiap individu memiliki preferensi makanan yang unik. Karakteristik rasa yang khas dapat memengaruhi seberapa bersedia seseorang membayar untuk hidangan di restoran tertentu. Keterkaitan antara preferensi makanan seseorang dengan karakteristik harga makanan dapat digunakan sebagai faktor penting dalam menentukan rekomendasi restoran. Penelitian ini memodelkan sebuah sistem rekomendasi restoran berdasarkan prefrensi rasa, harga dan rating makan penggunanya sebagai faktor utama dalam mempengaruhi hasil rekomendasi. Analisis data menggunakan 3 atribut yaitu data ulasan restoran, rating dan harga restoran. Teknik scraping dilakukan untuk pengumpulan dataset, adapun jumlah dataset sebanyak 661 data restoran dari hasil scraping. Pengubahan dataset dilakukan dengan proses Pra-Processing yang kemudian dilanjutkan dengan mempelajari model data dengan pendekatan Content-Based Filtering (CBF). Hasil penelitian menunjukkan bahwa rata-rata akurasi yang diberikan sistem rekomendasi yang dibangun adalah 73.33% dari rekomendasi restoran berdasarkan harga dan ulasan.

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An empirical study of content-based recommendation systems in mobile app markets

Cited by 10 publications

References 34 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

Nudging digital entrepreneurs: the influence of the Google Play Store top developer award on technological innovation

Pemodelan Sistem Rekomendasi Restoran berdasarkan Preferensi Pengguna dengan Pendekatan Content-Based Filtering

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