Context-Aware Recommendation Methods

Agagu, Tosin; Tran, Thomas

doi:10.5815/ijisa.2018.09.01

Cited by 5 publications

(3 citation statements)

References 20 publications

(50 reference statements)

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“…Table 1 shows the average user ratings for the QoS, interest, QoR, and QoE among all high-QoS and low-QoS videos 3 . We see that the impact on user perception of recommendation quality (QoR), is not significant for the nudged recommendations.…”

Section: Resultsmentioning

confidence: 99%

“…For instance, if the user is mobile or in areas with low quality connectivity, QoS awareness could be triggered in the RS. More specifically, research in context-aware RSs is particularly timely, e.g., as indicated by the revived RecSys workshop CARS 2.0 [1] and recent related works, e.g., [3,5,10,11,28,30,38,39]. The main algorithmic approaches for incorporating contextual information into rating-based RSs are pre-filtering, post-filtering, and modeling [38].…”

Section: Discussionmentioning

confidence: 99%

“…Predictions are mainly based on user interests, history, content/user similarity scores, etc. Recommendations may also take into account the context of a viewing session (e.g., location, time, type of device, user activity, environment) [2,3,5,6,27,28,38,39].…”

Section: Introductionmentioning

confidence: 99%

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Towards QoS-Aware Recommendations

Sermpezis,

Kastanakis,

Pinheiro

et al. 2019

Preprint

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This paper suggests the concept of QoS-aware recommendations for multimedia services/applications. We propose that recommendation systems (RSs) should take into account the expected QoS with which a content can be delivered to a user, to increase the user satisfaction and retention rates. While QoS-aware (or, networkaware) recommendations have been recently proposed as a promising solution to improve network performance, the impact of this idea on real user experience has only been speculated. Our contributions in this direction are the following: (i) we reconsider the problem from the perspective of users and the RS; (ii) to support our suggestions, we conduct experiments with real users, and present initial experimental results. Our analysis demonstrates the potential of the idea: QoS-aware recommendations could be beneficial for both the users (better experience) and content providers (higher retention rates). We believe that our study can help open a future research direction.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Towards QoS-Aware Recommendations

Sermpezis,

Kastanakis,

Pinheiro

et al. 2019

Preprint

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Gated Hypergraph Neural Network for Scene-Aware Recommendation

Yang

Zhang

Shi

et al. 2022

Database Systems for Advanced Applications

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When Should Recommenders Account for Low QoS?

Nogueira,

Pinheiro,

Assis

et al. 2023

IEEE Access

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Content recommendation systems, also known as recommenders, are pervasive and have significant impact on user demands over the Internet. Platforms such as YouTube and Netflix constantly seek to improve their recommendation systems, to provide better quality of experience (QoE) for their users. QoE, in turn, depends on a multitude of factors, including the quality of recommendation (QoR), e.g., based on users histories and content categories, and the quality of service (QoS), e.g., measured by network delay and throughput. Even though QoS is key in a best-effort Internet, existing recommendation systems overlook it, resulting in recommendations that are suboptimal in terms of QoE. In this study, our goal is to devise a QoS-aware, QoE-friendly, content recommendation system and indicate its feasibility in the wild. For this purpose, we conducted an experiment with real users driven by the following question: When should recommenders account for low QoS? Each user is requested to evaluate pairs of videos, that vary in their contents and QoS levels. We experimentally determined category-dependent thresholds that determine the sensitivity of users with respect to QoS and QoR. Given the collected insights on QoS-aware recommendations, we considered our second research question: Can recommenders compensate for low QoS? We conducted experiments over the Internet, relying on YouTube API and network measurements tools, and report our findings on (i) the characterization of QoS and (ii) the compensation for low QoS. Our measurements suggest that content far from the trends tends to be far from the user. We quantified the extent to which unpopular content tends to be served with a lower QoS and established a methodology to determine the relationship between content popularity and its physical proximity to users. Then, we verified that making requests a bit trendier can hit much closer content.

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Context-Aware Recommendation Methods

Cited by 5 publications

References 20 publications

Towards QoS-Aware Recommendations

Towards QoS-Aware Recommendations

Gated Hypergraph Neural Network for Scene-Aware Recommendation

When Should Recommenders Account for Low QoS?

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