Lit@EVE: Explainable Recommendation Based on Wikipedia Concept Vectors

Qureshi, Muhammad Atif; Greene, Derek

doi:10.1007/978-3-319-71273-4_41

Cited by 4 publications

(1 citation statement)

References 5 publications

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“…This approximation is used to make a rating prediction, but it can also be used to propose a tentative review on which the user can elaborate on. This is related to a number of approaches [32,18,24] on explainable recommendations. We think that this can lower the barrier to write reviews.…”

Section: Suggesting Reviews To Usersmentioning

confidence: 99%

TransRev: Modeling Reviews as Translations from Users to Items

García-Durán

González²,

Oñoro-Rubio³

et al. 2020

Lecture Notes in Computer Science

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The text of a review expresses the sentiment a customer has towards a particular product. This is exploited in sentiment analysis where machine learning models are used to predict the review score from the text of the review. Furthermore, the products costumers have purchased in the past are indicative of the products they will purchase in the future. This is what recommender systems exploit by learning models from purchase information to predict the items a customer might be interested in. We propose TRANSREV, an approach to the product recommendation problem that integrates ideas from recommender systems, sentiment analysis, and multi-relational learning into a joint learning objective. TRANSREV learns vector representations for users, items, and reviews. The embedding of a review is learned such that (a) it performs well as input feature of a regression model for sentiment prediction; and (b) it always translates the reviewer embedding to the embedding of the reviewed items. This allows TRAN-SREV to approximate a review embedding at test time as the difference of the embedding of each item and the user embedding. The approximated review embedding is then used with the regression model to predict the review score for each item. TRANSREV outperforms state of the art recommender systems on a large number of benchmark data sets. Moreover, it is able to retrieve, for each user and item, the review text from the training set whose embedding is most similar to the approximated review embedding.

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Section: Suggesting Reviews To Usersmentioning

confidence: 99%

TransRev: Modeling Reviews as Translations from Users to Items

García-Durán

González²,

Oñoro-Rubio³

et al. 2020

Lecture Notes in Computer Science

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Serendipity in Recommender Systems: A Systematic Literature Review

Ziarani

Ravanmehr

2021

J. Comput. Sci. Technol.

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Explainable AI for 6G Use Cases: Technical Aspects and Research Challenges

Wang,

Qureshi,

Miralles-Pechuán

et al. 2024

IEEE Open J. Commun. Soc.

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Around 2020, 5G began its commercialization journey, and discussions about the nextgeneration networks (such as 6G) emerged. Researchers predict that 6G networks will have higher bandwidth, coverage, reliability, energy efficiency, and lower latency, and will be an integrated "humancentric" network system powered by artificial intelligence (AI). This 6G network will lead to many real-time automated decisions, ranging from network resource allocation to collision avoidance for self-driving cars. However, there is a risk of losing control over decision-making due to the high-speed, data-intensive AI decision-making that may go beyond designers' and users' comprehension. To mitigate this risk, explainable AI (XAI) methods can be used to enhance the transparency of the black-box AI decisionmaking process. This paper surveys the application of XAI towards the upcoming 6G age, including 6G technologies (such as intelligent radio and zero-touch network management) and 6G use cases (such as industry 5.0). Additionally, the paper summarizes the lessons learned from recent attempts and outlines important research challenges in applying XAI for 6G use cases soon.

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Lit@EVE: Explainable Recommendation Based on Wikipedia Concept Vectors

Cited by 4 publications

References 5 publications

TransRev: Modeling Reviews as Translations from Users to Items

TransRev: Modeling Reviews as Translations from Users to Items

Serendipity in Recommender Systems: A Systematic Literature Review

Explainable AI for 6G Use Cases: Technical Aspects and Research Challenges

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