Recommendation with diversity: An adaptive trust-aware model

Yu, Ting; Guo, Jintang; Li, Wenhua; Wang, Harry Jiannan; Ling, Fan

doi:10.1016/j.dss.2019.113073

Cited by 28 publications

(17 citation statements)

References 36 publications

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“…This is a one-fits-all method that provides recommendations to all users with a constant accuracy-diversity balance. However, individuals have different needs for diversity, thus it is important to provide recommendations with an adaptive degree of diversity [10,48]. We aim to investigate how to learn the trade-off parameter from users' behavior so as to address this need.…”

Section: Discussionmentioning

confidence: 99%

Improving End-to-End Sequential Recommendations with Intent-aware Diversification

Chen

Ren

Cai

et al. 2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

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Sequential recommenders that capture users' dynamic intents by modeling sequential behavior, are able to accurately recommend items to users. Previous studies on sequential recommendations (SRs) mostly focus on optimizing the recommendation accuracy, thus ignoring the diversity of recommended items. Many existing methods for improving the diversity of recommended items are not applicable to SRs because they assume that user intents are static and rely on post-processing the list of recommended items to promote diversity. We consider both accuracy and diversity by reformulating SRs as a list generation task, and propose an integrated approach with an end-to-end neural model, called intent-aware diversified sequential recommendation (IDSR). Specifically, we introduce an implicit intent mining (IIM) module for SR to capture multiple user intents reflected in sequences of user behavior. We design an intent-aware diversity promoting (IDP) loss function to supervise the learning of the IIM module and guide the model to take diversity into account during training. Extensive experiments on four datasets show that IDSR significantly outperforms stateof-the-art methods in terms of recommendation diversity while yielding comparable or superior recommendation accuracy.

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Section: Discussionmentioning

confidence: 99%

Improving End-to-End Sequential Recommendations with Intent-aware Diversification

Chen

Ren

Cai

et al. 2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

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“…e similarity values with high discrepancy indicate that the users tend to change their preferences more frequently and dramatically. e similarity between users' long-term preference profiles and the selected item is measured by equation (11) throughout the selected period. Figure 3 shows the preference changes of two users toward the same item.…”

Section: E User Changes Preferences Examplementioning

confidence: 99%

“…Furthermore, the novelty and the diversity of the recommendation systems can cover the shortages of the recommendation systems. For example, the problem of long-tail items (e.g., less popular and newly added items) can be effectively addressed by diverse (e.g., with variant and wide-ranging features) recommendations [11]. Moreover, the influence of repeated recommended items on user satisfaction and business sales can be alleviated by considering the novelty of recommendations [12,13].…”

Section: Introductionmentioning

confidence: 99%

A Time‐Aware Hybrid Approach for Intelligent Recommendation Systems for Individual and Group Users

Huang

Stakhiyevich

2021

Complexity

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Although personal and group recommendation systems have been quickly developed recently, challenges and limitations still exist. In particular, users constantly explore new items and change their preferences throughout time, which causes difficulties in building accurate user profiles and providing precise recommendation outcomes. In this context, this study addresses the time awareness of the user preferences and proposes a hybrid recommendation approach for both individual and group recommendations to better meet the user preference changes and thus improve the recommendation performance. The experimental results show that the proposed approach outperforms several baseline algorithms in terms of precision, recall, novelty, and diversity, in both personal and group recommendations. Moreover, it is clear that the recommendation performance can be largely improved by capturing the user preference changes in the study. These findings are beneficial for increasing the understanding of the user dynamic preference changes in building more precise user profiles and expanding the knowledge of developing more effective and efficient recommendation systems.

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“…Wu et al [31] took into account users' personality and proposed a generalized, dynamic personality-based greedy re-ranking approach to improve the personalized diversity in web applications. Yu et al [32] proposed an adaptive trust-aware recommendation model to improve the trade-off strategy of accuracy and diversity by studying the trust relationships among users, which could balance and adapt individual and aggregate diversity measures.…”

Section: Relationship With Some Related Workmentioning

confidence: 99%

Improving Recommendation Diversity by Highlighting the ExTrA Fabricated Experts

Dong

Yuan

et al. 2020

IEEE Access

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Nowadays, recommender systems (RSes) are becoming increasingly important to individual users and business marketing, especially in the online e-commerce scenarios. However, while the majority of recommendation algorithms proposed in the literature have focused their efforts on improving prediction accuracy, other important aspects of recommendation quality, such as diversity of recommendations, have been more or less overlooked. In the latest decade, recommendation diversity has drawn more research attention, especially in the models based on user-item bipartite networks. In this paper, we introduce a family of approaches to extract fabricated experts from users in RSes, named as the Expert Tracking Approaches (ExTrA for short), and explore the capability of these fabricated experts in improving the recommendation diversity, by highlighting them in a well-known bipartite network-based method, called the Mass Diffusion (MD for short) model. These ExTrA-based models are compared with two state-of-the-art MD-improved models HHP and BHC, with respect to recommendation accuracy and diversity. Comprehensive empirical results on three real-world datasets MovieLens, Netflix and RYM show that, our proposed ExTrA-based models can achieve significant diversity gain while maintain comparable level of recommendation accuracy.

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Recommendation with diversity: An adaptive trust-aware model

Cited by 28 publications

References 36 publications

Improving End-to-End Sequential Recommendations with Intent-aware Diversification

Improving End-to-End Sequential Recommendations with Intent-aware Diversification

A Time‐Aware Hybrid Approach for Intelligent Recommendation Systems for Individual and Group Users

Improving Recommendation Diversity by Highlighting the ExTrA Fabricated Experts

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