Sentiment and Preference Guided Social Recommendation

Chen, Yoke Yie; Ferrer, Xavier; Wiratunga, Nirmalie; Plaza, Enric

doi:10.1007/978-3-319-11209-1_7

Cited by 8 publications

(18 citation statements)

References 25 publications

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“…The Digital SLR Camera dataset we use was extracted by us from Amazon during April 2014 [13] contained more than 20,000 user generated reviews over a set of 2,264 products. We pruned those products older than 1st January 2008 and with less than 15 user reviews, and merged any synonymous products, leaving us data on 50 products.…”

Section: Discussionmentioning

confidence: 99%

“…In our previous work on social recommender systems we harnessed knowledge from product reviews, and characterized every product by a set of aspect-sentiment pairs extracted from its reviews [13]. Based on these characterizations, we ranked and selected the most useful aspects for recommendation [14].…”

Section: Aspects and Basic Level Conceptsmentioning

confidence: 99%

“…In this work, we use a similar approach to [13] in order to extract the set of salient aspects used to define important characteristics of photographic digital cameras. We call aspect vocabulary A the set of extracted aspects.…”

Section: Aspects and Basic Level Conceptsmentioning

confidence: 99%

“…To this end, we analyze textual reviews of user experiences with digital cameras and identify the set of aspects the users use and the polarity of the sentiment words associated with them [13,14]. Aspects are grouped in basic level concepts, creating a new concept vocabulary, to overcome the disparate granularity of the extracted aspects.…”

Section: Introductionmentioning

confidence: 99%

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Concept Discovery and Argument Bundles in the Experience Web

Ferrer

Plaza

2016

Case-Based Reasoning Research and Development

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Abstract. In this paper we focus on a particular interesting web usergenerated content: people's experiences. We extend our previous work on aspect extraction and sentiment analysis and propose a novel approach to create a vocabulary of basic level concepts with the appropriate granularity to characterize a set of products. This concept vocabulary is created by analyzing the usage of the aspects over a set of reviews, and allows us to find those features with a clear positive and negative polarity to create the bundles of arguments. The argument bundles allow us to define a concept-wise satisfaction degree of a user query over a set of bundles using the notion of fuzzy implication, allowing the reuse experiences of other people to the needs a specific user.

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Section: Discussionmentioning

confidence: 99%

Section: Aspects and Basic Level Conceptsmentioning

confidence: 99%

Section: Aspects and Basic Level Conceptsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Concept Discovery and Argument Bundles in the Experience Web

Ferrer

Plaza

2016

Case-Based Reasoning Research and Development

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“…In this paper, we infer user preferences from a preference graph by comparing the sentiment-rich user-generated content unique to the subgraph of interest. Chen et al (2014) estimated the utility of a product by combining product popularity and users' sentiment feedback of a product. Specifically, an aspect weighting algorithm using sentiment scores of aspects and view-purchased product pairs was proposed.…”

Section: Social Recommender Systemsmentioning

confidence: 99%

Integrating selection-based aspect sentiment and preference knowledge for social recommender systems

Chen

Wiratunga

Lothian

2019

OIR

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Purpose Recommender system approaches such as collaborative and content-based filtering rely on user ratings and product descriptions to recommend products. More recently, recommender system research has focussed on exploiting knowledge from user-generated content such as product reviews to enhance recommendation performance. The purpose of this paper is to show that the performance of a recommender system can be enhanced by integrating explicit knowledge extracted from product reviews with implicit knowledge extracted from analysis of consumer’s purchase behaviour. Design/methodology/approach The authors introduce a sentiment and preference-guided strategy for product recommendation by integrating not only explicit, user-generated and sentiment-rich content but also implicit knowledge gleaned from users’ product purchase preferences. Integration of both of these knowledge sources helps to model sentiment over a set of product aspects. The authors show how established dimensionality reduction and feature weighting approaches from text classification can be adopted to weight and select an optimal subset of aspects for recommendation tasks. The authors compare the proposed approach against several baseline methods as well as the state-of-the-art better method, which recommends products that are superior to a query product. Findings Evaluation results from seven different product categories show that aspect weighting and selection significantly improves state-of-the-art recommendation approaches. Research limitations/implications The proposed approach recommends products by analysing user sentiment on product aspects. Therefore, the proposed approach can be used to develop recommender systems that can explain to users why a product is recommended. This is achieved by presenting an analysis of sentiment distribution over individual aspects that describe a given product. Originality/value This paper describes a novel approach to integrate consumer purchase behaviour analysis and aspect-level sentiment analysis to enhance recommendation. In particular, the authors introduce the idea of aspect weighting and selection to help users identify better products. Furthermore, the authors demonstrate the practical benefits of this approach on a variety of product categories and compare the approach with the current state-of-the-art approaches.

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Preference and Sentiment Guided Social Recommendations with Temporal Dynamics

Ferrer

Chen

Wiratunga

et al. 2014

Research and Development in Intelligent Systems XXXI

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Capturing users' preference that change over time is a great challenge in recommendation systems. What makes a product feature interesting now may become the accepted standard in the future. Social recommender systems that harness knowledge from user expertise and interactions to provide recommendation have great potential in capturing such trending information. In this paper, we model our recommender system using sentiment rich user generated product reviews and temporal information. Specifically we integrate these two resources to formalise a novel aspect-based sentiment ranking that captures temporal distribution of aspect sentiments and so the preferences of the users over time. We demonstrate the utility of our proposed model by conducting a comparative analysis on data extracted from Amazon.com and Cnet. We show that considering the temporal preferences of users leads to better recommendation and that user preferences change over time.

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Sentiment and Preference Guided Social Recommendation

Cited by 8 publications

References 25 publications

Concept Discovery and Argument Bundles in the Experience Web

Concept Discovery and Argument Bundles in the Experience Web

Integrating selection-based aspect sentiment and preference knowledge for social recommender systems

Preference and Sentiment Guided Social Recommendations with Temporal Dynamics

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