A user-centric evaluation of context-aware recommendations for a mobile news service

Pessemier, Toon De; Courtois, Cédric; Vanhecke, Kris; Damme, Kristin Van; Martens, Luc; Marez, Lieven De

doi:10.1007/s11042-014-2437-9

Cited by 32 publications

(17 citation statements)

References 31 publications

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“…An accurate definition of recommender system, given by Burke [35], is ''any system that produces individualized recommendations as output or has the effect of guiding the user in a personalized way to interesting or useful objects in a large space of possible options''. Within recommender systems, various techniques can be distinguished based on the knowledge source [27]: demographic, knowledge-based, community-based, content-based, collaborative, and hybrid recommendations. Among them, we focus on CB.…”

Section: A Content-based Recommender Systemsmentioning

confidence: 99%

“…Here we focus on those CB that use a vector space modeling to represent items. With this regard there are CB with (i) feature-based representations, where items are usually stored in a database table where rows are items and columns are the item features [27], [36], [37]; and (ii) free-text representation, where there is a natural language piece of text that describes the item [38].…”

Section: A Content-based Recommender Systemsmentioning

confidence: 99%

“…As key works in this direction, Musto et al [25] take as base the movie recommendation domain, and consider context as a weighting factor that influences the recommendation score of a user for a certain item, and Son et al [26] consider the location context and define user by the articles read in the past along with his/her location, working over the movie recommendation domain. De Pessemier et al [27] consider recommendations in mobile devices as very suitable to integrate context-awareness, and use devices sensors and time of the day to deliver contextualized news recommendations.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Big Data Semantic Driven Context Aware Recommendation Method for Question-Answer Items

et al. 2019

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Content-Based recommender systems (CB) filter relevant items to users in overloaded search spaces using information about their preferences. However, classical CB scheme is mainly based on matching between items descriptions and user profile, without considering that context may influence user preferences. Therefore, it cannot achieve high accuracy on user preference prediction. This paper aims to handle context-awareness (CA) to improve quality of recommendation taking contextual information as the trend in current trend interest, in which a stream of status updates can be analyzed to model the context. It proposes a novel CA-CB approach that recommends question/answer items by considering context awareness based on topic detection within current trend interest. A case study and related experiments were developed in the big data framework Spark to show that the context integration benefits recommendation performance.INDEX TERMS Content-based recommender system, context-awareness, user profile contextualization, map-reduce, big data.• A suitable way to study status updates, i.e. a userprovided free text, in the QA recommendation scenario to provide recommendations tailored to both the user preferences and current trend interest.

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Section: A Content-based Recommender Systemsmentioning

confidence: 99%

Section: A Content-based Recommender Systemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Big Data Semantic Driven Context Aware Recommendation Method for Question-Answer Items

et al. 2019

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“…The authors of [61] proposed a light-weight mobile RS with semantic-rich awareness information, which is able to provide content recommendations that are tailored to learners' background, contexts and the task at hand. In [62], an assessment of the usefulness of contextual information with respect to classical RS was performed to determine users' utilization of mobile news services and how contextual information influences their consumption behavior. While [61] and [62] suggest new mechanisms in mobile RS, [63] report a framework designed for library staff to provide location-based access to information via mobile computing platform, which eases access and utilization of the library collection.…”

Section: (2) E-documents Domainmentioning

confidence: 99%

“…It is observed that only one or two contexts are incorporated by the researchers in the RS process such as in [62], and considering the dynamicity of users, incorporating a single context may not necessarily represent a user's interests and preferences.…”

Section: Recommendations and Future Research Directionsmentioning

confidence: 99%

Context-Aware Recommender System: A Review of Recent Developmental Process and Future Research Direction

et al. 2017

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Intelligent data handling techniques are beneficial for users; to store, process, analyze and access the vast amount of information produced by electronic and automated devices. The leading approach is to use recommender systems (RS) to extract relevant information from the vast amount of knowledge. However, early recommender systems emerged without the cognizance to contextualize information regarding users' recommendations. Considering the historical methodological limitations, Context-Aware Recommender Systems (CARS) are now deployed, which leverage contextual information in addition to the classical two-dimensional search processes, providing better-personalized user recommendations. This paper presents a review of recent developmental processes as a fountainhead for the research of a context-aware recommender system. This work contributes by taking an integrated approach to the complete CARS developmental process, unlike other review papers, which only address a specific aspect of the CARS process. First, an in-depth review is presented pertaining to the state-of-the-art and classified literature, considering the domain of the application models, filters, extraction and evaluation approaches. Second, viewpoints are presented relating to the extraction of literature with analysis on the merit and demerit of each, and the evolving processes between them. Finally, the outstanding challenges and opportunities for future research directions are highlighted.

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Towards the significance of taxi recommender systems in smart cities

Katarya

2022

Concurrency and Computation

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Since their launch in the early 1990's, recommender systems (RSs) have played an essential role in information filtering and providing personalized information to users by utilizing the web, mobile, context, personalized, and practical applications. Many research articles have been published in various application domains such as movies, books, documents, news, images, music, shopping, TV programs, tourism, group, social, taxi, and others. However, from these domains, taxi recommendation is a new and thrust area of research, as publications in this domain are few and recently published.Taxi recommendations are associated with drivers-passengers with their: behavior, hotspots, trajectory, point of interest, route planning or prediction, taxi finding, anomaly or fraud detection, urban computing, mobility patterns and so forth. Due to this diversity and various agendas in taxi recommendations, more exploration is required, as it is a less mature and growing field than other domains. So we have done a literature review in the taxi domain by classification, incorporating reputed articles published from 2001 to August 2022. We include the reputed papers which highlight, analyze and perform studies only on the taxi domain. This article categorizes and discusses the research and development in taxi recommendations as nature, approaches, algorithms, methods, datasets, models, devices, information, and evaluation techniques. This survey article will actively help the researchers and professionals understand the current situations in taxi RS and resolve future trends, opportunities, and research.

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A user-centric evaluation of context-aware recommendations for a mobile news service

Cited by 32 publications

References 31 publications

A Big Data Semantic Driven Context Aware Recommendation Method for Question-Answer Items

A Big Data Semantic Driven Context Aware Recommendation Method for Question-Answer Items

Context-Aware Recommender System: A Review of Recent Developmental Process and Future Research Direction

Towards the significance of taxi recommender systems in smart cities

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