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.
Social recommender systems harness knowledge from social content, experiences and interactions to provide recommendations to users. The retrieval and ranking of products, using similarity knowledge, ...is central to the recommendation architecture. To enhance recommendation performance, having an effective representation of products is essential. Social content such as product reviews contain experiential knowledge in the form of user opinions centred on product aspects. Making sense of these for recommender systems requires the capability to reason with text. However, Natural Language Processing (NLP) toolkits trained on formal text documents encounter challenges when analysing product reviews, due to their informal nature. This calls for novel methods and algorithms to capitalise on textual content in product reviews together with other knowledge resources. In this thesis, methods to utilise user purchase preference knowledge - inferred from the viewed and purchased product behaviour - are proposed to overcome the challenges encountered in analysing textual content. This thesis introduces three major methods to improve the performance of social recommender systems. First, an effective aspect extraction method that combines strengths of both dependency relations and frequent noun analysis is proposed. Thereafter, this thesis presents how extracted aspects can be used to structure opinionated content enabling sentiment knowledge to enrich product representations. Second, a novel method to integrate aspect-level sentiment analysis and implicit knowledge extracted from users' product purchase preferences analysis is presented. The role of sentiment distribution and threshold analysis on the proposed integration method is also explored. Third, this thesis explores the utility of feature selection techniques to rank and select relevant aspects for product representation. For this purpose, this thesis presents how established dimensionality reduction approaches from text classification can be employed to select a subset of aspects for recommendation purposes. Finally, a comprehensive evaluation of all the proposed methods in this thesis is presented using a computational measure of 'better' and Mean Average Precision (MAP) with seven real-world datasets.
Escalating global population and expanding industrialisation have significantly contributed to an increased demand for plastics. Methods for plastic waste disposal predominantly involve landfilling, ...which consumes space and pollutes soil and water bodies, posing a substantial threat to the environment and public health. The critical challenge herein lies in efficiently managing the abundant biomass and plastics waste. Here we review gasification and pyrolysis, two prominent waste treatment processes. Co-pyrolysis of plastics and biomass exhibits a synergistic effect, significantly enhancing the yield and quality of bio-oil, biochar, and syngas. Integration of gasification and pyrolysis reduces activation energy, increases hydrogen production during co-gasification, and improves bio-oil calorific value. The gasification of polyethylene terephthalate with air in the presence of activated carbon limits tar formation with enhanced syngas production. Steam gasification enhances hydrogen generation by up to 40.0% compared to air gasification. Selective catalysts in the integrated pyrolysis–gasification process of biomass and plastics mixtures demonstrate optimisation of the production of high-purity carbon nanotubes and bio-oil. Application of innovative approaches such as microwaves, steam purging, and catalysts like zeolite or composite catalysts enhance heat transfer mechanism, tar cracking, and devolatilisation.
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