A social-semantic recommender system for advertisements

García‐Sánchez, Francisco; Colomo‐Palacios, Ricardo; Valencia‐García, Rafael

doi:10.1016/j.ipm.2019.102153

Cited by 76 publications

(39 citation statements)

References 52 publications

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“…Yin et al [84] embed all the observed relations among users, events, locations, time, and text content in shared low-dimension space, which is able to leverage the correlation between events and their associated content and contextual information. García-Sánchez et al [85] proposed a framework that uses a shared ontological model to represent user interests and advertisements as vectors of concepts. The advertisement textual description is analyzed using natural language techniques to identify ontology concepts.…”

Section: ) Multidimensional-based Recommender Systemmentioning

confidence: 99%

“…[84] Hybrid [24], [31], [33], [34], [37], [56], [74], [81], [85], [86], [93] [24], [40], [56], [61], [81], [85], [86], [89] Over the last years, CARSs have been a focus of attention for various research communities where enormous studies were proposed to enhance their efficiency. In this section, we briefly present context-based recommender system approaches and techniques and highlight recent developments in modern state-of-the-art techniques, including deep learning and hybrid techniques.…”

Section: Sparsitymentioning

confidence: 99%

“…[87] Matrix factorization [33], [34], [39], [42], [43], [45], [48], [52], [58], [64], [82], [92], [97] Mixture model [32], [106] Ontology model [85], [112] Random-walk [53], [57], [62], [77], [81], [115]…”

Section: Markov Chainmentioning

confidence: 99%

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Context-Aware Recommender Systems for Social Networks: Review, Challenges and Opportunities

Suhaim

Berri

2021

IEEE Access

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Context-aware recommender systems dedicated to online social networks experienced noticeable growth in the last few years. This has led to more research being done in this area stimulated by the omnipresence of smartphones and the latest web technologies. These systems are able to detect specific user needs and adapt recommendations to actual user context. In this research, we present a comprehensive review of context-aware recommender systems developed for social networks. For this purpose, we used a systematic literature review methodology which clearly defined the scope, the objective, the timeframe, the methods, and the tools to undertake this research. Our focus is to investigate approaches and techniques used in the development of context-aware recommender systems for social networks and identify the research gaps, challenges, and opportunities in this field. In order to have a clear vision of the research potential in the field, we considered research articles published between 2015 and 2020 and used a research portal giving access to major scientific research databases. Primary research articles selected are reviewed and the recommendation process is analyzed to identify the approach, the techniques, and the context elements employed in the development of the recommendation systems. The paper presents the detail of the review study, provides a synthesis of the results, proposes an evaluation based on measurable evaluation tools developed in this study, and advocates future research and development pathways in this interesting field.

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Section: ) Multidimensional-based Recommender Systemmentioning

confidence: 99%

Section: Sparsitymentioning

confidence: 99%

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Context-Aware Recommender Systems for Social Networks: Review, Challenges and Opportunities

Suhaim

Berri

2021

IEEE Access

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“…In ''CUP'' [20], Alan et al presented the concept of inferred contextual profiles that describe a user in a given situation or context. Sanchez et al [21] used ontological model to represent user and item profiles for advertisement recommendation. Wei et al [22] proposed a method to determine user authority in a social tagging system for personalised recommendations.…”

Section: Related Workmentioning

confidence: 99%

Reviewer Credibility and Sentiment Analysis Based User Profile Modelling for Online Product Recommendation

Kumar

Al‐Turjman³

et al. 2020

IEEE Access

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Deciphering user purchase preferences, their likes and dislikes is a very tricky task even for humans, making its automation a very complex job. This research work augments heuristic-driven user interest profiling with reviewer credibility analysis and fine-grained feature sentiment analysis to devise a robust recommendation methodology. The proposed credibility, interest and sentiment enhanced recommendation (CISER) model has five modules namely candidate feature extraction, reviewer credibility analysis, user interest mining, candidate feature sentiment assignment and recommendation module. Review corpus is given as an input to the CISER model. Candidate feature extraction module uses context and sentiment confidence to extract features of importance. To make our model robust to fake and unworthy reviews and reviewers, reviewer credibility analysis proffers an approach of associating expertise, trust and influence scores with reviewers to weigh their opinion according to their credibility. The user interest mining module uses aesthetics of review writing as heuristics for interest-pattern mining. The candidate feature sentiment assignment module scores candidate features present in review based on their fastText sentiment polarity. Finally, the recommendation module uses credibility weighted sentiment scoring of user preferred features for purchase recommendations. The proposed recommendation methodology harnesses not only numeric ratings, but also sentiment expressions associated with features, customer preference profile and reviewer credibility for quantitative analysis of various alternative products. The mean average precision (MAP@1) for CISER is 93% and MAP@3 is 49%, which is better than current state-of-the-art systems.

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“…One of the pillars of the Semantic Web are the ontologies, which can be referred as to a formal and explicit specification of a shared conceptualization [7]. Nowadays, decision support systems based on ontologies have been widely accepted as solutions in different domains such as recommender systems [8], software engineering [9], and health [10][11][12]. This opens the door for opportunities to apply this technology to the agriculture domain, specifically for crop insect pest's diagnosis and management.…”

Section: Introductionmentioning

confidence: 99%

AgriEnt: A Knowledge-Based Web Platform for Managing Insect Pests of Field Crops

et al. 2020

Self Cite

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In the agricultural context, there is a great diversity of insects and diseases that affect crops. Moreover, the amount of data available on data sources such as the Web regarding these topics increase every day. This fact can represent a problem when farmers want to make decisions based on this large and dynamic amount of information. This work presents AgriEnt, a knowledge-based Web platform focused on supporting farmers in the decision-making process concerning crop insect pest diagnosis and management. AgriEnt relies on a layered functional architecture comprising four layers: the data layer, the semantic layer, the web services layer, and the presentation layer. This platform takes advantage of ontologies to formally and explicitly describe agricultural entomology experts’ knowledge and to perform insect pest diagnosis. Finally, to validate the AgriEnt platform, we describe a case study on diagnosing the insect pest affecting a crop. The results show that AgriEnt, through the use of the ontology, has proven to produce similar answers as the professional advice given by the entomology experts involved in the evaluation process. Therefore, this platform can guide farmers to make better decisions concerning crop insect pest diagnosis and management.

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A social-semantic recommender system for advertisements

Cited by 76 publications

References 52 publications

Context-Aware Recommender Systems for Social Networks: Review, Challenges and Opportunities

Context-Aware Recommender Systems for Social Networks: Review, Challenges and Opportunities

Reviewer Credibility and Sentiment Analysis Based User Profile Modelling for Online Product Recommendation

AgriEnt: A Knowledge-Based Web Platform for Managing Insect Pests of Field Crops

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