Enhancing Diversity-Accuracy Technique on User-Based Top-N Recommendation Algorithms

Premchaiswadi, Wichian; Poompuang, Pitaya; Jongswat, Nipat; Premchaiswadi, Nucharee

doi:10.1109/compsacw.2013.68

Cited by 13 publications

(6 citation statements)

References 6 publications

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“…They also proposed a graph-theoretic approach to increase the diversity of recommended items based on maximum bipartite matching computations. Premchaiswadi et al ( 2013 ) proposed a total diversity effect ranking for improving diversity by considering the diversity effect of each item in the recommendation list. Sun et al ( 2020 ) proposed a recommendation method based on Bayesian graph convolutional neural networks.…”

Section: Related Workmentioning

confidence: 99%

FROST: Fallback Voice Apps Recommendation for Unhandled Voice Commands in Intelligent Personal Assistants

Mohamed

Xiao

et al. 2022

Front. Big Data

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Intelligent personal assistants (IPAs) such as Amazon Alexa, Google Assistant and Apple Siri extend their built-in capabilities by supporting voice apps developed by third-party developers. Sometimes the smart assistant is not able to successfully respond to user voice commands (aka utterances). There are many reasons including automatic speech recognition (ASR) error, natural language understanding (NLU) error, routing utterances to an irrelevant voice app, or simply that the user is asking for a capability that is not supported yet. The failure to handle a voice command leads to customer frustration. In this article, we introduce a fallback skill recommendation system (FROST) to suggest a voice app to a customer for an unhandled voice command. There are several practical issues when developing a skill recommender system for IPAs, i.e., partial observation, hard and noisy utterances. To solve the partial observation problem, we propose collaborative data relabeling (CDR) method. To mitigate hard and noisy utterance issues, we propose a rephrase-based relabeling technique. We evaluate the proposed system in both offline and online settings. The offline evaluation results show that the FROST system outperforms the baseline rule-based system. The online A/B testing results show a significant gain of customer experience metrics.

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Section: Related Workmentioning

confidence: 99%

FROST: Fallback Voice Apps Recommendation for Unhandled Voice Commands in Intelligent Personal Assistants

Mohamed

Xiao

et al. 2022

Front. Big Data

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“…As for diversification strategies, its nature is a trade-off between diversity and accuracy. One common and easy-toconduct strategy is post-filtering, which recommends top N+ND accurate items first, and then removes ND items to achieve the highest diversity [31], or just select top N representative items after the clustering [32]. Another strategy regards this trade-off as a multi-objective optimization, which leverages swarm intelligence, simulated annealing, genetic algorithms, or other heuristic algorithms to re-rank the recommendation list and achieves a…”

Section: Related Work 21 Recommendation Approaches and Diversity Stra...mentioning

confidence: 99%

“…This section will fine-tune it to achieve the best recommendation performance. Specifically, for each length of L, Top 9 items were firstly recommended by 5 nearest neighbors, and 4 items were then filtered out by the diversification strategy (i.e., K = 5, N = 5, and ND = 4, referring to [31]). The average scores of F-Score, NDCG, Div, and T on total 22 non-repetitive pairs of (U, C) were shown in Figure 5.…”

Section: Implementation and Fine-tuning Of The Proposed Approachmentioning

confidence: 99%

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A context-aware diversity-oriented knowledge recommendation approach for smart engineering solution design

Chen

Zheng

et al. 2021

Knowledge-Based Systems

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“…Many research works [1,3,14,49] have been undertaken to propose novel diversification algorithms. Our proposed module can learn the diverse trends of user preference and provide recommendation with diversity.…”

Section: Recommendation Diversitymentioning

confidence: 99%

Multi-trends Enhanced Dynamic Micro-video Recommendation

Lu,

Huang,

Zhang

et al. 2021

Preprint

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The explosively generated micro-videos on content sharing platforms call for recommender systems to permit personalized microvideo discovery with ease. Recent advances in micro-video recommendation have achieved remarkable performance in mining users' current preference based on historical behaviors. However, most of them neglect the dynamic and time-evolving nature of users' preference, and the prediction on future micro-videos with historically mined preference may deteriorate the effectiveness of recommender systems. In this paper, we propose to explicitly model dynamic multi-trends of users' current preference and make predictions based on both the history and future potential trends. We devise the DMR framework, which comprises: 1) the implicit user network module which identifies sequence fragments from other users with similar interests and extracts the sequence fragments that are chronologically behind the identified fragments; 2) the multi-trend routing module which assigns each extracted sequence fragment into a trend group and update the corresponding trend vector; 3) the history-future trend prediction module jointly uses the history preference vectors and future trend vectors to yield the final click-through-rate. We validate the effectiveness of the proposed framework over multiple state-of-the-art micro-video recommenders on two publicly available real-world datasets. Relatively extensive analysis further demonstrate the superiority of modeling dynamic multi-trend for micro-video recommendation.

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Enhancing Diversity-Accuracy Technique on User-Based Top-N Recommendation Algorithms

Cited by 13 publications

References 6 publications

FROST: Fallback Voice Apps Recommendation for Unhandled Voice Commands in Intelligent Personal Assistants

FROST: Fallback Voice Apps Recommendation for Unhandled Voice Commands in Intelligent Personal Assistants

A context-aware diversity-oriented knowledge recommendation approach for smart engineering solution design

Multi-trends Enhanced Dynamic Micro-video Recommendation

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