Session-Based Recommender Systems

Jannach, Dietmar; Quadrana, Massimo; Cremonesi, Paolo

doi:10.1007/978-1-0716-2197-4_8

Cited by 14 publications

(4 citation statements)

References 87 publications

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“…Future works considers exploring the explored temporal (and also spatial) bias in more recent approaches to POI recommendation, such as session-based models [9,10] and/or exploring content (e.g., images of POIs) [7,8]. Moreover, we plan to investigate approaches that can mitigate these unfair effects, through the lens of data characteristics [5] or via algorithmic interventions [18].…”

Section: Discussionmentioning

confidence: 99%

Exploring the Impact of Temporal Bias in Point-of-Interest Recommendation

Rahmani,

Naghiaei,

Tourani

et al. 2022

Preprint

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Recommending appropriate travel destinations to consumers based on contextual information such as their check-in time and location is a primary objective of Point-of-Interest (POI) recommender systems. However, the issue of contextual bias (i.e., how much consumers prefer one situation over another) has received little attention from the research community. This paper examines the effect of temporal bias, defined as the difference between users' check-in hours, leisure vs. work hours, on the consumer-side fairness of context-aware recommendation algorithms. We believe that eliminating this type of temporal (and geographical) bias might contribute to a drop in traffic-related air pollution, noting that rush-hour traffic may be more congested. To surface effective POI recommendation, we evaluated the sensitivity of state-of-the-art context-aware models to the temporal bias contained in users' check-in activities on two POI datasets, namely Gowalla and Yelp. The findings show that the examined context-aware recommendation models prefer one group of users over another based on the time of check-in and that this preference persists even when users have the same amount of interactions.

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Section: Discussionmentioning

confidence: 99%

Exploring the Impact of Temporal Bias in Point-of-Interest Recommendation

Rahmani,

Naghiaei,

Tourani

et al. 2022

Preprint

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“…Dengan menggunakan Item-based Collaborative Filtering, sistem dapat memberikan rekomendasi item berdasarkan preferensi pengguna lain yang memiliki selera yang mirip. Metode ini berguna dalam merekomendasikan item seperti produk, film, buku, atau resep kepada pengguna [18].…”

Section: Item-based Collaborative Filtering Dengan Cosine Similarityunclassified

Penggunaan Metode Analisis Data Untuk Rekomendasi Menu Makanan Berdasarkan Persediaan Bahan dan Preferensi Pengguna

Syaifudin,

Ardyningrum

2024

INFORMAL

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Cooking represents a significant and enjoyable activity, enabling individuals to create delectable dishes. However, the challenge of discovering recipes that align with available ingredients and personal preferences persists. To tackle this issue, a recommendation system is introduced, employing a content-based filtering approach that utilizes Jaccard similarity to harness users' ingredient ownership. Additionally, an item-based collaborative filtering approach employing cosine similarity is utilized to propose recipes based on user preferences and those of fellow users. This comprehensive approach delivers precise and pertinent recipe recommendations, optimizing ingredient utilization and elevating the cooking experience. Furthermore, the assessment of recommendations based on user preferences reveals varying outcomes.

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“…In recent years, there has also been a shift towards incorporating temporal information into recommendation systems, recognizing that users' interests and preferences may change over time (Raza and Ding, 2019;Wu et al, 2018). This has led to the development of sessionbased and sequential recommendation systems, which consider the order and timing of user interactions when making recommendations (Jannach et al, 2020(Jannach et al, , 2022. Other areas of research in recommendation systems include the incorporation of side information, such as user demographics or location, the handling of cold-start problems for new users or items, and the development of hybrid recommendation systems that combine multiple methods (Ravi and Vairavasundaram, 2016).…”

Section: Related Workmentioning

confidence: 99%

A cross-platform recommendation system from Facebook to Instagram

Chang

Chen

2023

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Purpose The purpose of this paper is to provide a cross-platform recommendation system that recommends the most suitable public Instagram accounts to Facebook users. Design/methodology/approach We collect data from both Facebook and Instagram and then propose a similarity matching mechanism for recommending the most appropriate Instagram accounts to Facebook users. By removing the data disparity between the two heterogeneous platforms and integrating them, the system is able to make more accurate recommendations. Findings The results show that the method proposed in this paper can recommend suitable public Instagram accounts to Facebook users with very high accuracy. Originality/value To the best of the authors’ knowledge, this is the first study to propose a recommender system to recommend Instagram public accounts to Facebook users. Second, our proposed method can integrate heterogeneous data from two different platforms to generate collaborative recommendations. Furthermore, our cross-platform system reveals an innovative concept of how multiple platforms can promote their respective platforms in a unified, cooperative and collaborative manner.

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Session-Based Recommender Systems

Cited by 14 publications

References 87 publications

Exploring the Impact of Temporal Bias in Point-of-Interest Recommendation

Exploring the Impact of Temporal Bias in Point-of-Interest Recommendation

Penggunaan Metode Analisis Data Untuk Rekomendasi Menu Makanan Berdasarkan Persediaan Bahan dan Preferensi Pengguna

A cross-platform recommendation system from Facebook to Instagram

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