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Sökning: WFRF:(Lu Wei) > Högskolan i Halmstad

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1.
  • 2019
  • Tidskriftsartikel (refereegranskat)
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2.
  • Tirkkonen, Olav, et al. (författare)
  • On-Off Necklace Codes for Asynchronous Mutual Discovery
  • 2017
  • Ingår i: 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC). - : IEEE. - 9781538635315 - 9781538635292 - 9781538635308 - 9781538635322
  • Konferensbidrag (refereegranskat)abstract
    • We consider mutual discovery of asynchronous wireless transceivers with a fixed activity ratio. On-off activity patterns are slotted, and repeat in discovery frames. For discovery it has to be guaranteed that the activity patterns of two transceivers are not overlapping. We design necklace codes determining activity patterns to guarantee discovery within a discovery frame, so that two asynchronous transceivers always have non-overlapping activity patterns. The number of distinct patterns is analyzed as a function of discovery frame length, and on-off activity ratio. As an application example, we consider the discovery problem for vehicle-to-vehicle communication. To guarantee discovery of far-away vehicles, discovery sequences providing processing gain, and necklace coded activity patterns are needed. We find that billions of discovery code identities can be provided with a repetition frequency that is high enough to guarantee a missed discovery probability less than 10−6.
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3.
  • Zeyu, He, et al. (författare)
  • Causal embedding of user interest and conformity for long-tail session-based recommendations
  • 2023
  • Ingår i: Information Sciences. - Philadelphia, PA : Elsevier. - 0020-0255 .- 1872-6291. ; 644
  • Tidskriftsartikel (refereegranskat)abstract
    • Session-based recommendation is misleading by popularity bias and always favors short-head items with more popularity. This paper studies a new causal-based framework CauTailReS to increase the diversity of session recommendations. We first propose a new causal graph and then use the do-calculus in order to understand how popularity influences the process of making recommendations from the user's point of view. Popularity only misleads users temporarily, rather than in a long term and globally. Second, we believe that user clicks on popular products demonstrate their high quality and reputation. CauTailReS only eliminates ‘bad’ biases and retains ‘good’ effects through interest and consistent causal embedding mechanisms. To determine how similar various users are on various target items, CauTailReS also employs a re-ranking technique known as ‘conformity-aware re-ranking’. To discover interactions based on what actual users want, CauTailReS also employs counterfactual reasoning. Extensive comparative experiments on four real world datasets have shown CauTailReS can well capture the true interests and consistency of users. As compared to the current state-of-the-art, CauTailReS enhances long-tail performance (APLT is increased by 8.14%) and recommendation accuracy (MRR is increased by 2.75%). This proves that introducing causal embeddings helps to reasonably enhance the diversity of recommendations. © 2023 Elsevier Inc.
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