Neural Serendipity Recommendation

Xu, Yuanbo; Yang, Yintang; Wang, En; Han, Jiayu; Zhuang, Fuzhen; Yu, Zhiwen; Xiong, Hui

doi:10.1145/3396607

Cited by 21 publications

(3 citation statements)

References 35 publications

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“…However, previous studies argue that recommendation algorithms should consider more than accuracy. For example, diversity [4][5][6][7][8], coverage [9][10][11], unexpectedness [12][13][14] and fairness [15,16] are also important concepts in measuring a recommender system. Among these concepts, diversity requires the recommender system to generate item lists that contain more item attributes (e.g., genres of movies), and a fair recommender can provide unbiased recommendation lists for consumers or providers.…”

Section: Introductionmentioning

confidence: 99%

DACSR: Decoupled-Aggregated End-to-End Calibrated Sequential Recommendation

Chen

Shi

et al. 2022

Applied Sciences

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Sequential recommendations have made great strides in accurately predicting the future behavior of users. However, seeking accuracy alone may bring side effects such as unfair and overspecialized recommendation results. In this work, we focus on the calibrated recommendations for sequential recommendation, which is connected to both fairness and diversity. On the one hand, it aims to provide fairer recommendations whose preference distributions are consistent with users’ historical behaviors. On the other hand, it can improve the diversity of recommendations to a certain degree. But existing methods for calibration have mainly relied on the post-processing on the candidate lists, which require more computation time in generating recommendations. In addition, they fail to establish the relationship between accuracy and calibration, leading to the limitation of accuracy. To handle these problems, we propose an end-to-end framework to provide both accurate and calibrated recommendations for sequential recommendation. We design an objective function to calibrate the interests between recommendation lists and historical behaviors. We also provide distribution modification approaches to improve the diversity and mitigate the effect of imbalanced interests. In addition, we design a decoupled-aggregated model to improve the recommendation. The framework assigns two objectives to two individual sequence encoders, and aggregates the outputs by extracting useful information. Experiments on benchmark datasets validate the effectiveness of our proposed model.

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Section: Introductionmentioning

confidence: 99%

DACSR: Decoupled-Aggregated End-to-End Calibrated Sequential Recommendation

Chen

Shi

et al. 2022

Applied Sciences

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“…Satisfying requests that concern creativity is called serendipity [41,42]. Technically, theory refers to a balance or trade-off between accuracy and creativity (expansion), similar to the trade-off in the exploration of optimal solutions [43].…”

Section: Introductionmentioning

confidence: 99%

Multiagent-Based Data Presentation Mechanism for Multifaceted Analysis in Network Management Tasks

Sasai

Fukutani

Kobashi

et al. 2022

Sensors

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Although network management tasks are highly automated using big data and artificial intelligence technologies, when an unforeseen cybersecurity problem or fault scenario occurs, administrators sometimes directly analyze system data to make a heuristic decision. However, a wide variety of information is required to address complex cybersecurity risks, whereas current systems are focused on narrowing the candidates of information. In this study, we propose a multiagent-based data presentation mechanism (MADPM) that consists of agents operating data-processing tools that store and analyze network data. Agents in MADPM interact with other agents to form data-processing sequences. In this process, we design not only the composition of the sequence according to requirements, but also a mechanism to expand it to enable multifaceted analysis that supports heuristic reasoning. We tested five case studies in the prototype system implemented in an experimental network. The results indicated that the multifaceted presentation of data can support administrators more than the selected single-faceted optimal presentation. The final outcome of our proposed approach is the provision of a multifaceted and cross-system data presentation for heuristic inference in network management tasks.

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“…Cell phones are carried by most of the population, enabling mobile signal data to simulate all kinds of traffic modes between different OD areas, which is very suitable for traffic prediction scenarios. Besides, the OD flow between base stations is predicted and analyzed, which can achieve the monitoring of the urban population flow in base station spatial granularity, help urban manages study travel behavior mode of residents, and design and manage the urban traffic systems [6,7].…”

Section: Introductionmentioning

confidence: 99%

Human Origin‐Destination Flow Prediction Based on Large Scale Mobile Signal Data

Huang

Yang

et al. 2021

Wireless Communications and Mobile Computing

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The human origin-destination (OD) flow prediction is of great significance for urban safety control, stampede prevention, disease transmission control, urban planning, and many other aspects. Most of the existing methods generally divide the urban area into grids and use vehicle GPS trajectories and metrocard check-in data, combined with machine learning or deep learning models to predict human OD flow. However, these kinds of methods are challenging to capture fine-grained human mobility patterns. Moreover, these methods usually deviate from the actual human OD transfer patterns on a citywide scale due to the particularity of different datasets. To this end, in this paper, we use large-scale mobile phone signal data to achieve human OD flow prediction between the coverage of varying signal base stations. Many signal base stations are distributed in urban geographical space, collecting all the mobile phone user’s location information to obtain large-scale fine-grained unbiased human OD flow data. Due to the lack of natural topology structure between base stations, this paper adopts a TGCN model combined with a graph fusion module to pretrain the dynamic population distribution prediction task. The parameters of the graph fusion module are employed to capture the different semantic information in the proposed hybrid machine learning method and finally achieve citywide human OD flow prediction. Extensive experiments on the real-world signal datasets in Changchun, China, demonstrate the effectiveness of our model.

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Neural Serendipity Recommendation

Cited by 21 publications

References 35 publications

DACSR: Decoupled-Aggregated End-to-End Calibrated Sequential Recommendation

DACSR: Decoupled-Aggregated End-to-End Calibrated Sequential Recommendation

Multiagent-Based Data Presentation Mechanism for Multifaceted Analysis in Network Management Tasks

Human Origin‐Destination Flow Prediction Based on Large Scale Mobile Signal Data

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