Visualizing Program Genres' Temporal-Based Similarity in Linear TV Recommendations

Bogina, Veronika; Sheidin, Julia; Kuflik, Tsvi; Berkovsky, Shlomo

doi:10.1145/3399715.3399813

Cited by 5 publications

(5 citation statements)

References 14 publications

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“…Hidasi et al [28] explored how to add candidate attribute information (such as text and image) into the RNN framework, and proposed several model frameworks for fusion attributes. Bogina et al [29] consider the length of time a user stays on a candidate item in a specific session as one of the features; that is, the longer the user stays on the item, the higher the level of interest. Li et al [30] used two GRU encoders with attention mechanisms to monitor the macro overall information and micro purpose in user behavior data, and then integrated the two vector results together.…”

Section: Related Workmentioning

confidence: 99%

A Job Recommendation Model Based on a Two-Layer Attention Mechanism

Mao,

Lin,

Cheng

2024

Electronics

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In the field of job recruitment, traditional recommendation methods only rely on users’ rating data of positions for information matching. This simple strategy has problems such as low utilization of multi-source heterogeneous data and difficulty in mining relevant information between recruiters and applicants. Therefore, this paper proposes a recurrent neural network model based on a two-layer attention mechanism. The model first improves the entity representation of recruiters and applicants through user behavior, company-related knowledge and other information. The entities and their combinations are then mapped to the vector space using one-hot and TransR methods, and a recurrent neural network with a two-layer attention mechanism is used to obtain their potential interests from the click sequence, and then a recommendation list is generated. The experimental results show that this model achieves better results than previous models.

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Section: Related Workmentioning

confidence: 99%

A Job Recommendation Model Based on a Two-Layer Attention Mechanism

Mao,

Lin,

Cheng

2024

Electronics

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“…One such enhanced RNN model evolved from GRU4Rec employs data augmentation techniques to bolster the stability of the model training process [88]. Another modified RNN model adjusts for batch generation by considering dwell time to more precisely capture user behaviors, discovering the potential impact of dwell time on the results [89]. However, a significant challenge is that when all candidate items rank above popular items based on a popularity-driven sampling method, the learning speed of the developed model is constrained, particularly when recommending long-tailed items.…”

Section: Long-and Short-term Sequence Recommendationmentioning

confidence: 99%

A Comprehensive Survey of Recommender Systems Based on Deep Learning

Zhou,

Xiong,

Chen

2023

Applied Sciences

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With the increasing abundance of information resources and the development of deep learning techniques, recommender systems (RSs) based on deep learning have gradually become a research focus. Although RSs have evolved in recent years, a systematic review of existing RS approaches is still warranted. The main focus of this paper is on recommendation models that incorporate deep learning techniques. The objective is to guide novice researchers interested in this field through the investigation and application of the proposed recommendation models. Specifically, we first categorize existing RS approaches into four types: content-based recommendations, sequence recommendations, cross-domain recommendations, and social recommendation methods. We then introduce the definitions and address the challenges associated with these RS methodologies. Subsequently, we propose a comprehensive categorization framework and novel taxonomies for these methodologies, providing a thorough account of their research advancements. Finally, we discuss future developments regarding this topic.

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“…This concept refers to the question if an item is available at a specific point in time for a user. For example, TV programs that are scheduled to be broadcast at different times throughout the day are not always available to be recommended (Oh et al, 2012;Turrin et al, 2014;Bogina et al, 2020). Another example are the opening hours of POIs in tourism or other factors, such as weather, that may affect temporal availability (Trattner et al, 2016).…”

Section: Short-term Preferencesmentioning

confidence: 99%

“…The biggest challenge here is that items are scheduled at specific times. Therefore, there is a limited set of programs that can be recommended to the user based on her profile (Oh et al, 2012;Turrin et al, 2014;Bogina et al, 2020). Moreover, watching TV is often a group activity.…”

Section: Entertainmentmentioning

confidence: 99%

Considering temporal aspects in recommender systems: a survey

Bogina

Kuflik

Jannach

et al. 2022

User Model User-Adap Inter

Self Cite

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The widespread use of temporal aspects in user modeling indicates their importance, and their consideration showed to be highly effective in various domains related to user modeling, especially in recommender systems. Still, past and ongoing research, spread over several decades, provided multiple ad-hoc solutions, but no common understanding of the issue. There is no standardization and there is often little commonality in considering temporal aspects in different applications. This may ultimately lead to the problem that application developers define ad-hoc solutions for their problems at hand, sometimes missing or neglecting aspects that proved to be effective in similar cases. Therefore, a comprehensive survey of the consideration of temporal aspects in recommender systems is required. In this work, we provide an overview of various time-related aspects, categorize existing research, present a temporal abstraction and point to gaps that require future research. We anticipate this survey will become a reference point for researchers and practitioners alike when considering the potential application of temporal aspects in their personalized applications.

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Visualizing Program Genres' Temporal-Based Similarity in Linear TV Recommendations

Cited by 5 publications

References 14 publications

A Job Recommendation Model Based on a Two-Layer Attention Mechanism

A Job Recommendation Model Based on a Two-Layer Attention Mechanism

A Comprehensive Survey of Recommender Systems Based on Deep Learning

Considering temporal aspects in recommender systems: a survey

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