Progress in context-aware recommender systems — An overview

Raza, Shaina; Ding, Chen

doi:10.1016/j.cosrev.2019.01.001

Cited by 111 publications

(54 citation statements)

References 33 publications

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“…Various techniques for product recommendations like exploiting ratings for quantifying user-user and user-product adherence [content based and collaborative filtering], sentiment-analysis based recommendations, context-aware recommendations, user-preference and trust oriented recommendations have been reported. Literature is well equipped with primary and secondary studies on state-of-the-art techniques for recommender systems [8]- [10].…”

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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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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“…When taking into account temporal changes, time is considered as a special case of the user's decision-making context. The contextual approach involves identifying patterns of user behavior when selecting data for recommendations [14]. The temporal pattern reflects the frequency of changes in the requirements of a group of users.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Detailing explanations in the recommender system based on matching temporal knowledge

Chalyi

Leshchynskyi

Leshchynska

2020

EEJET

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about users may be inaccurate or incomplete, which leads to distortion of recommendations. The received incorrect recommendations do not meet the interests of users, which reduces the credibility of the recommender system. Inaccuracies and distortions in user preference data result from shilling attacks [4]. Such attacks are used to change the sales of target items in the direction desired by the attacker. The essence of a shilling attack is to artificially change the ratings of the target items. As a result, a personal recommendation is formed with distortions and may not take into account the real preferences of users. The recommender system, using an incorrect personal list of goods and services, "forces" the user to choose those items that the attacker is interested in. In case of incomplete information about the user, recommendations are built for new or irregular users [5]. In such a cold start situation, there are no data obtained as a result of feedback from the user, since the latter has not yet made purchases or posted ratings. Therefore, at present, one of the trends in the development of recommender systems, which makes it possible to consider the incompleteness and inaccuracy of the initial data, is to

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“…The injection of contextual data and contextual factors in the recommendation process can significantly improve the overall accuracy of the suggestions. As shown by several works [1], [4], [21], [37], Context-Aware Recommenders (CARs) usually outperform merely content or collaborativebased recommendations.…”

Section: Context-aware Recommender Systemsmentioning

confidence: 99%

Generating Recommendations From Multiple Data Sources: A Methodological Framework for System Design and Its Application

et al. 2020

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Recommender systems (RSs) are systems that produce individualized recommendations as output or drive the user in a personalized way to interesting or useful objects in a space of possible options. Recently, RSs emerged as an effective support for decision making. However, when people make decisions, they usually take into account different and often conflicting information such as preferences, long-term goals, context, and their current condition. This complexity is often ignored by RSs. In order to provide an effective decision-making support, a RS should be "holistic", i.e., it should rely on a complete representation of the user, encoding heterogeneous user features (such as personal interests, psychological traits, health data, social connections) that may come from multiple data sources. However, to obtain such holistic recommendations some steps are necessary: first, we need to identify the goal of the decision-making process; then, we have to exploit common-sense and domain knowledge to provide the user with the most suitable suggestions that best fit the recommendation scenario. In this article, we present a methodological framework that can drive researchers and developers during the design process of this kind of "holistic" RS. We also provide evidence of the framework validity by presenting the design process and the evaluation of a food RS based on holistic principles.

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Progress in context-aware recommender systems — An overview

Cited by 111 publications

References 33 publications

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

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

Detailing explanations in the recommender system based on matching temporal knowledge

Generating Recommendations From Multiple Data Sources: A Methodological Framework for System Design and Its Application

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