Text-Based User-kNN: Measuring User Similarity Based on Text Reviews

Terzi, Maria; Rowe, Matthew; Ferrario, Maria Angela; Whittle, Jon

doi:10.1007/978-3-319-08786-3_17

Cited by 13 publications

(8 citation statements)

References 14 publications

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“…reviews, comments) via either word-level text similarity or extracted sentiment. For instance, Terzi et al (2014) proposed TextKNN to measure the similarity between users based on the similarity of text reviews instead of ratings. Pappas and Popescu-Belis (2013) developed a sentiment-aware nearest neighbor model (SANN) for recommendations over TED talks.…”

Section: Memory-based Methods With Side Informationmentioning

confidence: 99%

Research commentary on recommendations with side information: A survey and research directions

Sun

Guo

Yang

et al. 2019

Electronic Commerce Research and Applications

181

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Recommender systems have become an essential tool to help resolve the information overload problem in recent decades. Traditional recommender systems, however, suffer from data sparsity and cold start problems. To address these issues, a great number of recommendation algorithms have been proposed to leverage side information of users or items (e.g., social network and item category), demonstrating a high degree of effectiveness in improving recommendation performance. This Research Commentary aims to provide a comprehensive and systematic survey of the recent research on recommender systems with side information. Specifically, we provide an overview of state-of-the-art recommendation algorithms with side information from two orthogonal perspectives. One involves the different methodologies of recommendation: the memory-based methods, latent factor, representation learning and deep learning models. The others cover different representations of side information, including structural data (flat, network, and hierarchical features, and knowledge graphs); and non-structural data (text, image and video features). Finally, we discuss challenges and provide new potential directions in recommendation, along with the conclusion of this survey.

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Section: Memory-based Methods With Side Informationmentioning

confidence: 99%

Research commentary on recommendations with side information: A survey and research directions

Sun

Guo

Yang

et al. 2019

Electronic Commerce Research and Applications

181

View full text Add to dashboard Cite

show abstract

“…To identify if the tweet is relevant to the event under focus, we investigate the semantic similarity ρ of the previously processed text of the tweet with the name and type of the event. This is achieved by utilising WordNet [36]: a large lexical database of nouns, verbs, adjectives and adverbs grouped into sets of cognitive synonyms to compute similarity between short texts [37], and the semantic similarity measure described in [38], effectively utilised in [39], [40].…”

Section: Relatedness Calculation (ρ)mentioning

confidence: 99%

Towards a Social-media Driven Multi-Drone Tasking platform

Terzi

Kolios

Panayiotou

et al. 2020

2020 International Conference on Unmanned Aircraft Systems (ICUAS)

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We present a framework for utilising social-media for tasking drone swarms in search and rescue missions. As elaborated in this work, social-media are a source of timely and valuable information regarding the occurrence, the evolution and the damage produced in disasters. Exploiting this source of knowledge can be very valuable in planning search and rescue missions and identifying tasks to be conducted. In this work a novel multi-drone search and rescue framework is developed that considers social-media signals in the creation of search tasks. The key design challenges of implementing the proposed framework are discussed and a simulated scenario based on a real-life Twitter data set is presented as a means of providing evidence of the effectiveness of the proposed framework.

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“…Or because of the director? So this paper debate that text reviews offer a deduce opinion of a user for an item, making them an ideal source of knowledge for enhancing recommendation process by extracting the latent feature from reviews [13].…”

Section: Sentiment Analysis As An Implicit Feedbackmentioning

confidence: 99%

Proposed Collaborative Filtering Recommender System Based on Implicit and Explicit User's Preferences

Hassan

Abdulwahhab

2018

ijs

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The expansion of web applications like e-commerce and other services yields an exponential increase in offers and choices in the web. From these needs, the recommender system applications have arisen. This research proposed a recommender system that uses user's reviews as implicit feedback to extract user preferences from their reviews to enhance personalization in addition to the explicit ratings. Diversity also improved by using k-furthest neighbor algorithm upon user's clusters. The system tested using Douban movie standard dataset from Kaggle, and show good performance.

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Text-Based User-kNN: Measuring User Similarity Based on Text Reviews

Cited by 13 publications

References 14 publications

Research commentary on recommendations with side information: A survey and research directions

Research commentary on recommendations with side information: A survey and research directions

Towards a Social-media Driven Multi-Drone Tasking platform

Proposed Collaborative Filtering Recommender System Based on Implicit and Explicit User's Preferences

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